Ferdous Jahan

Tarik Bourouina

Professeur Pascal Nouet

Professeur João Pedro Oliveira

Dr Bérengère Lebental

Dr Jérémie Drévillon

Frederic Marty

Dr Patrick Poulichet

Prof Yamin

Miniature Multi-parameter Sensor for Water Network Monitoring Capteur multi-paramétrique miniature pour la gestion des réseaux d'eau Directeurs de thèse

My boundless thankfulness to Dr. William Cesar for his valuable contribution at the very initial stage of the thesis. His introductory diverse attempt helped me to proceed in the right direction during the whole period of my thesis study. I also would like to mention a few more names of intern students in different periods of my thesis whose important contribution alleviated stress and helped me to handle specified complexity. Thanks to Mr. Massimo Pellegrino, whose offering in conductivity sensor was significantly conducive to develop the initial prototype and Ms. Kruthiga Gopalan for helping me by conducting experiments during

Table of Contents

List of Figures vii

List of Figures

I. Introduction The need for environment monitoring

With the growth of the global industrialization and the population, the environment on earth is being dilapidated day by day. The main natural resources, namely air, water and soil are increasingly polluted by human activities either deliberately or accidentally. As a consequence, living organisms are mortally affected leading to less biodiversity, climate change as well as numerous severe health concerns. Pollution in its various forms and climate change are now becoming a global concern which led the United Nations, among other international organizations, to establish a policy of Sustainable Development called #Envision2030, which translates into 17 Goals to transform our world i . A healthy environment relies on the limpidity of its basic elements. There are numerous research activities going on to this horizon to maintain the natural ecological balance for improving the present condition and future unexpected environmental antagonism. All the elements of the environment are reciprocally kindred and unitedly they form an organic complex. If one of the elements is changed or contaminated within this chain, the whole system decisively faces a rough change which brings an imbalance in the usual ecological condition. For example, the purity of the soil is interconnected with the groundwater purity and in some perspective; it affects the air quality as well. Hence, excessive use of chemical fertilizer and insects killer (pesticides, herbicides) for cultivation or the presence of heavy metals and arsenic in the soil damages the natural characteristics of the soil 1,2 and it is also liable for the water pollution as well. The detrimental chemical substances get mixed with the natural sources' water due to the rain and the close contact of subterranean water with the soil which is admixed with harmful metal cause water pollution. As a consequence, in order to protect the environment and enable actions to be taken, there is a need to monitor through measurements all it's the basic elements from the accidental and deliberate pollution. This kind of measurement of a diverse number of parameters over very large domains requires the development of new tools capable of ubiquitous sensing. In other words, there is a need to measure almost everything almost everywhere.

Motivation

In the context of monitoring natural resources and related need of developing new measurement tools, the focus of this thesis is on water. The need is to develop ubiquitous sensors towards their wide-scale deployment for the measurement of a wide variety of parameters. It is obvious that wide-scale deployment will require low-cost solutions and hence, the use of miniaturization seems unavoidable. A vast majority of the commercially available devices for measurement in water are not miniaturized and their functionality i https://www.un.org/development/desa/disabilities/envision2030.html 2 does not cover both the physical and chemical aspects of the water. A few exceptions were reported of multi-parameter sensors based on the heterogeneous assembly of several micro-sensors, but those solutions lead to high costs in the order of 10.000 € unit price, which makes it not compatible with large deployment through water networks.

In order to respond to this need, the H2020 European project PROTEUS is aimed at to produce a monolithic multi-parameter sensing module which can measure 4 physical and 5 chemical parameters of the water. This project is funded by European Commission under H2020 programme for research. The goal of this project is to deliver a smart water monitoring system which is enabled to perform real-time analysis along with wireless data transmission option. In addition, this is a self-powered device; an energy harvester is joined with the sensor node which can generate power from the water flow. The proposed device is 10 times smaller and cost-effective to compare with the state of the art. A schematic diagram of the proposed water distribution monitoring network using PROTEUS multi-parameter sensor chip is presented in Figure I-1, where all the other associated equipment is also sketched. This system includes the wastewater and rainwater under the same monitoring network as well. reports the sensor node that will be implemented in the water distribution network for measuring the parameters. All the subsidiary electronics are adjoined inside this sensor node, also developed by UNINOVA.

In total, there are 9 different sensing elements to determine several physical and chemical parameters of the water. Among them, only the four physical MEMS sensors are reported here in this thesis study, while the chemical sensors required post-processing of the chip using ink-jet printing of functionalized carbon nanotubes, which has been developed by IFSTTAR in the frame of their joint team with LPICM Laboratory (CNRS-Ecole Polytechnique). These four sensors can measure the conductivity, pressure, flow-rate and temperature of the water. Different versions of the MPSC have been fabricated in order to study different phenomena i.e., the impact of the substrate material on the sensors' performance, power consumption, the thermal efficiency of the thermo-resistive sensor. Although, all the sensors are based on resistive read-out but they are distinct in terms of their working principle, physical structure. Therefore, each of the sensors has independent specifications in order to ensure

CMOS Chip

their functionality at the desired level. For instance, the conductivity sensor measures the electrical conductivity of the water and it works with an AC current supply. It's a four-probe conductivity sensor, whose sensitivity depends strongly on the shape of the electrodes and the supply current frequency. The fundamental principles are somehow similar for the flow-rate and the temperature sensor. Both of them are a similar form of thin-film platinum RTD with the typical working principle, where the functionality of these two sensors relies on the heat transfer. As a consequence, low thermal conductive substrate and proper thermal insulation are required for the effectiveness of those sensors. On the other hand, the pressure sensing element of the MPSC is a piezo-resistive pressure sensor. The essential part of this pressure sensor is a membrane where four piezo-resistors placed at the edges of this membrane. Hence, this sensor requires a material compatible with membrane micro-fabrication. The usual material is silicon that is the most suitable for this purpose when dealing with MEMS technologies. The membrane of the considered pressure sensor is made by single crystal silicon while the piezo-resistors are made from a polysilicon thin film. Therefore, silicon is mandatory in order to create the pressure sensor. So, it is obvious that co-integration of all the above-mentioned sensors on the same chip will face some constraints in different perspectives. The challenge regarding the co-integration process and how it was overcome will be explained in Chapter 2 along with the MPSC fabrication details.

Outline of the thesis

This thesis is organized into 4 main chapters and is split into two Parts. Part I (Chapter 1 and Chapter 2) deals with the Multi-Parameter Sensing Chip (MPSC) for Water Network Monitoring. We will elaborate on how we managed most of the co-integration constraints in order to eventually come out with the realization of a monolithic MPSC, embedding four different physical sensors and ready to host additional chemical sensors through further post-processing of this chip. We will also present in this Part I, the results obtained on two sensors among the four sensors; the pressure sensor and the conductivity sensor.

The two other sensors will be described in much more detail in Part II (Chapter 3 and Chapter 4), which is fully dedicated to sensors involving thermal effects: temperature sensor and flow-rate sensors. In this part, we will discuss how proper and innovative designs allowed us to achieve full co-integration of a flowrate sensor, despite the constraint of operation with a silicon substrate, which is not favourable in terms of thermal conductivity.

In Chapter 1, the general background of environmental monitoring and its necessity is highlighted. The monitoring importance of the two most crucial environment elements air and water are discussed elaborately. Some of the existing devices that have been used to monitor these elements are also presented.

An introductory discussion regarding the multi-parameter sensor chip which has been developed under the frame of this thesis work presented here in this chapter as well.

Chapter 2 deals with the co-integration effort that was required to achieve a Multi-Parameter Sensing Chip (MPSC) for Water Network Monitoring. The design strategy and fabrication steps from the very initial version to the final prototype are discussed in a consecutive manner. The first prototype of the multi-sensing device was based on a glass substrate and in the subsequent versions; silicon was used as a substrate material in order to enable the co-integration. Hence, the co-integration of different sensors on the same chip and the appeared challenge regarding the co-integration step also stated in Chapter 2. The conductivity and pressure sensor are included in this chapter as well. Their physical structure and fundamental working principles stated along with the experimental data. Particularly, the pressure sensor introduces the constraint regarding the co-integration process in the perspective of the substrate material. Common substrate material that allows the co-integration causes performance degradation of thermal sensing element like a flow-rate sensor of the MPSC. The detailed strategy of the successful co-integration is presented in this chapter.

Experimental results are also given for the assessment of the first sensor's performance and dispersion of their characteristics.

Chapter 3 gives an elaborate discussion on thermometry and multi-functionality of a resistor. It illustrates the different methods of thermometry and its history. The major three methods of the temperature measurement: invasive, semi-invasive and non-invasive and their sub-categories are briefly explained in the chapter. Afterwards, the diverse applications of a resistor are explored where it is demonstrated that a resistor can be used for different applications based on a similar operation principle. The functionality of a thin-film RTD can be defined as a thermo-resistive flow-rate sensor or temperature sensor with a simple modification in the operation mode. The flow-rate and temperature sensor of the MPSC is also a thin-film RTD. The difference between these two sensors lies in their physical structure and operation mode. Hence, the detailed mechanism behind the different application of an identical resistor is explained at the end of this chapter. The designed temperature sensor is also illustrated in this chapter. The importance of water temperature measurement is highlighted since several physical and chemical parameters depend on water temperature. The relationship between the temperature and the other parameters of the water is provided.

In the end, the discussion about the experimental results is reported.

Chapter 4 includes a comprehensive study of the flow-rate sensor. The considered flow-rate sensor is a thermo-resistive water flow measuring sensor, which senses the water flow by transferring the heat from the resistor to the operation medium (water). Different operation methods have been developed so far for the thermal flow-rate sensor. The three conventional operation principles: Anemometric (Hot-wire),

6
Calorimetric and Time-of-flight (TOF) are stated in this chapter. The flow sensing device of the MPSC is operated under anemometric operation mode. The primary prototype of the flow-rate sensor was fabricated on a glass substrate and the subsequent devices on the silicon with further important and crucial physical modifications. All the numerical and experimental results related to the different versions are discussed. A comparative study of the different models of the sensor is reported at the end of this chapter so as to provide a better understanding of the effectiveness of the proposed approaches and routes towards optimization.

Finally, this thesis dissertation is ended up with a general conclusion about the four physical MEMS sensors of the MPSC and few suggestions regarding the future works are also stated.

Part I: Multi-Parameter Sensing Chip

Non thermal sensors For decades, there were numerous attempts to invent an efficient technological solution to monitor the environment and at the same time, to provide early warning about the pollution. At the very early stage, the monitoring techniques rely on classical offline analysis system. This requires collecting a sample and performing the analysis in a laboratory, but this process needs a long operational time and costly resources to get the necessary data. Therefore, the traditional laboratory-based procedure appears non-resilient as it may lead to substantial risk for the environment and human health simultaneously. With the evolution of the technology, the trend is to build an intelligent and real-time multi-parameter monitoring system. Rather than creating a parted monitoring device for the different parameters; it is more pointful to design a monolithic multi-parameter sensing device. Thanks to the proven advantages of microfabrication technologies, a miniaturized modular format for such multi-parameter sensing device saves money and allows large-scale implementation in the plausible media. At present, there are several techniques and devices developed with the combination of smart electronic and computer interface in respond to this demand. Some devices are focused on measuring some specific parameters where the others are designed for measuring different parameters simultaneously.

Majority of the environmental monitoring systems are designed for monitoring one of the two most crucial elements of the environment which are air and water. Since these two elements are directly related to the human being health and their effects become visible very rapidly. Contaminated air and water cause both the short term and long term fatal diseases. An average estimation shows that a person spends around 90% of his life at indoor 3 and water is an essential ingredient for a person to complete his daily life activities. Therefore, the purity and cleanness must be ensured of these two essential elements in order to lead a sound and healthy life.

Air Quality Monitoring

The monitoring of indoor air quality (IAQ) and outdoor air quality (OAQ) both are important for the human well-being. Normally, it is comprehended that the IAQ is better than the OAQ, but evidence illustrates that the presence of the pollutants in the indoor air may be two or five times greater than the Chapter 1 10 outdoor air 3 . As a consequence, the air quality monitoring concern is shifted from outdoor to indoor in the last ten years 4 . There are several environmental risks for public health, which are traced out by the experts and the indoor air quality is always ranked in the top five 3 . Some serious health issues like heart and chronic respiratory diseases, lung cancer as well as short time sicknesses like fatigue and nausea are caused by poor air quality 3 . Several factors are responsible to degrade the air quality such as-biological factors, physical and chemical factors and sometimes their combination 4 . Daily activities of mankind also produce some harmful substances which increase the number of the air pollutants. and the children are suffering from respiratory and cardiovascular diseases [START_REF] Rudge | Excess winter morbidity among older people at risk of cold homes: a population-based study in a London borough[END_REF] . Excessive heat leads to heat stroke for the people those who are working under sunburn for a long time and causes heat rashes as well [START_REF]Making the Case: the impact of heat on healthnow and in the future -Heatwave_plan_2013_-_Making_the_case_Accessible_updated[END_REF] . If the percentage of the RH in the air is less than or surpass the standard value, then it creates some adverse effects on the health. Low RH enhances to spread the airborne disease and cause eyes irritation. It is harmful to the respiratory system as well as mucous membranes are produced inside the respiratory system due to the low RH [START_REF] Arundel | Indirect health effects of relative humidity in indoor environments[END_REF] . Besides, high RH leads to allergies, respiratory infection, dysphonia and wheeze [START_REF] Husman | Health effects of indoor-air microorganisms[END_REF] . The suggested moderate value of RH in the air to avoid the negative health effects is 40%-60% [START_REF] Arundel | Indirect health effects of relative humidity in indoor environments[END_REF] . Overmuch VOCs quantity present in the air is determined as one of the possible reason for sick building syndrome (SBS) [START_REF] Yu | A review of the emission of VOCs from polymeric materials used in buildings[END_REF] .

Nowadays there are plenty of devices and measurement methods are developed by the researchers to determine the IAQ. The classical measurement methods are building an air quality monitoring station, but this sort of measurement system is costly and provides low resolution sensing data [START_REF] Liu | Developed urban air quality monitoring system based on wireless sensor networks[END_REF] . Moreover, this type of monitoring station is not autonomous always and there is a need for an operator. Another short come is accumulating different sensing devices on the same system. Therefore, the traditional measurement method Chapter 1

11
does not allow for large-scale implementation and multi-parameter measurement simultaneously. The recent technologies are more focused on to develop miniature multi-parameter measurement system based on real-time analysis and remote access to extracted data. The existing devices are based on different working principle like-chemical analysis, using metal oxide sensor array, using optics, etc. 4,[START_REF] Postolache | Smart Sensors Network for Air Quality Monitoring Applications[END_REF][START_REF] Kwon | A study on NDIR-based CO2 sensor to apply remote air quality monitoring system[END_REF] . Some of them are using manufactured sensing elements, where some of them fabricate their own sensing elements.

Sherin Abraham and Xinrong Li develop a Wireless Sensor Network System (WSNS) indoor air quality monitoring system using Arduino, XBee modules and the sensing elements are micro gas sensors 3 . The device is presented in Figure 1-1. The Arduino board is the data processing unit and for wireless communication established by using XBee module. Péter Volgyesi et al. develop a mobile air quality monitoring system, where the sensor node is mounted on a car for measuring the different pollutants in the outdoor air [START_REF] Völgyesi | Air Quality Monitoring with SensorMap[END_REF] . The sensor prototype is demonstrated in Figure 1-2. The data sampling rate differs when the car is moving and when it is parked.

During stationary mode, the samples are taken few times an hour and when the car is in motion the sensors are sampled every minute and the data are stored with the location and time stamp using onboard global positioning system. The whole prototype consists of other two devices which are wireless wearable device and BGA Microsensor. The wireless wearable device is designed for the thermal comfort of the user. It measures the most relevant parameter of the air surrounding to the user and turns on the appropriate HVAC (Heating, Ventilation and Air-conditioning) system to create the desire comfortable environment for the user. Besides, Chapter 1

13 the BGA Micro-sensor is sensing module where all the sensing elements of the desired parameter are cointegrated on the small footprint in order to achieve easy installation and reduce power consumption.

Water Quality Monitoring

The necessity of water monitoring is no way less significant than air quality monitoring. Both of them are equally essential for the human being to survive on this planet. Monitoring is needed at a different level in the case of water. Due to the rapid urbanization all the natural and unnatural sources of water such as a river, lake, catchments, underground water, etc. are getting polluted and without chemical or physical treatment the water directly from the source cannot be used for drinking or doing any household work.

Therefore, the monitoring is important to avoid contamination at the source level as well as during the treatment operation. Moreover, there is a water distribution network by which the water is carried from the source or water plant to the user end. Monitoring is more crucial for the distribution network since poor maintenance or any accidental leakages in the distribution network cause huge water loss and unaware contamination, which increase both the health risk and economic loss. Water quality monitoring is related to the determination of physical, chemical, biological and to some extent microbiological characteristics of the water. The extracted data from water monitoring are used to take any decision regarding health and environmental issues.

Nowadays, four methods of water monitoring are available-a) sampling with a potable water quality monitoring device and following lab analysis, b) Continuous monitoring of water environment by an autonomous monitoring system, c) Water quality monitoring using remote sensing technology d)

Ascertainment the presence of a poisonous substance in the water using the sensitivity of aquatic organism [START_REF] Jiang | Design of a Water Environment Monitoring System Based on Wireless Sensor Networks[END_REF] . Although significant advances are achieved recently in the water quality monitoring technology but yet there is still a need to build an online efficient monitoring system which can measure multi-parameter simultaneously in a cost-effective way.

The idyllic miniature microsensor arrays are cost-effective which allows large-scale implementation and easy installation. The convenience of using microsensors for the water network monitoring is that it is suitable to monitor complex distribution infrastructure. Some smart sensing system can provide an early stage warning about the contamination which is really helpful to avoid possible health risk due to the polluted water. Generally, an early warning system consists of instrumentation technology monitoring system along with data analysis and interpreting in real time [START_REF] Grayman | Design of Early Warning and Predictive Sourcewater Monitoring Systems[END_REF] . The recently available devices which are manufactured by different companies for water quality monitoring, the mode of operation of those devices are based on different physics.

The water quality and water network monitoring can be addressed by investigating some key parameters like-pH level, chlorine, temperature, pressure, flow-rate, conductivity, turbidity, heavy metals, phosphates, nitrates, dissolved oxygen [START_REF] Jiang | Design of a Water Environment Monitoring System Based on Wireless Sensor Networks[END_REF][START_REF] Shaun | Robust multi-parameter sensing probe for water monitoring based on ALD-coated metallic micro-patterns[END_REF][START_REF] Storey | Advances in on-line drinking water quality monitoring and early warning systems[END_REF] . There are numerous sensors capable of measuring one of the abovementioned parameters. We will not elaborate on those individual sensors here as there is a very rich variety of these. Some of them are even based on micro-fabrication technologies, which lead to a reasonably lowcost. However, the global emphasis is on an autonomous on-line multi-parameter microsensor based water quality monitoring system, which is capable of measuring several parameters using the same device. In order to reach this objective, there are at least 3 approaches:

(i) -Sensor array or electronic tongue. Somehow similar to the electronic nose concept, this approach is based on taking advantage of the different responses of several sensors on different chemical species, so as to build a correspondence table or implement machine learning algorithms. Even though it is very promising, the Technology Readiness Level (TRL) of this approach is still very low, still far from commercialization. We will not elaborate on it in what will follow.

(ii) -Multi-parameter sensing based on Analytical Chemistry, which includes optical spectroscopy, chromatography techniques as well as mass spectrometry. Some of those techniques can lead to ultracompact miniaturization.

(iii) -Multi-parameter sensing measurement devices, based on co-integration of several sensors onto the same device. This co-integration is mostly based on the heterogeneous integration of sensors outsourced from different providers, leading to high cost (a few k€ up to 10 k€), mainly due to the need for assembly of those sensors and related low fabrication yield. This is the approach chosen in our work, targeting full monolithic co-integration on a single chip.

Analytical Chemistry for multi-parameter sensing of chemical pollutants of water

A submersible multiple water quality measurement device which can measure the total organic carbon (TOC) equivalent, biochemical oxygen demand (BOD), turbidity, an aromatic compound, nitrate and nitrite in the water. This device is manufactured by spectro::lyser™ 23 named s::can whose working principle relies on ultraviolet-visible spectroscopy. The device is shown in Figure 1234. The company ONDAVIA Inc [START_REF] Ondavia | Definitive Results. Decisive Actions[END_REF] uses Raman Spectroscopy for the quantitative detection of chemical micro-pollutants down to the sub-ppb concentration level. The measurements require water sampling. A droplet of water is then fed into a disposable microfluidic chip, embedding gold nanoparticle for plasmonic signal amplification (SERS: Surface Enhanced Raman Spectroscopy). Those disposable chips are also chemically functionalized so as to fit with a specific chemical target. Although very sensitive and very selective, this approach is not compatible with continuous online monitoring.

In addition, besides spectroscopy, there are other analytical chemistry instrumentation techniques that are used in several multiple parameter monitoring devices by different companies to trace the organic micro-pollutants like-gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) etc. [START_REF] Storey | Advances in on-line drinking water quality monitoring and early warning systems[END_REF] .

Multi-parameter sensing probes for water network monitoring

Within the category of multi-parameter sensing probes presented above, the most comprehensive is probably the Intellisonde system provided by the Intellitect company. It is based on heterogeneous cointegration and is. However, it is worth mentioning that there are already a few commercial multi-parameter probes, which are based on co-integration of different sensors on the same sensor chip [the KAPTA 3000 from ENDETEC [START_REF] Kapta Tm | AC2 In-line chlorine sensor | Veolia Water Technologies[END_REF] and the SIX-SENSE from CENSAR [START_REF] About | [END_REF] . However, our solution developed in the frame of the PROTEUS project is expected to out-perform those solutions in many aspects:

 Increased measurement capabilities: 7 up to 9 different parameters will be accessible.

 Reconfigurability of some sensors (flow-rate and conductivity) is expected to allow addressing different measurement ranges hand hence, different needs using the same probe.

Chapter 1

16

 Redundancy allows better addressing reliability issues as well as improved possibilities regarding temperature compensation schemes.

 Improved anti-biofouling is targeted, which is expected to provide longer lifetime and/or significant reduction of maintenance needs.

Particulate and microbiological pollutants

It is also worth mentioning that besides the chemical pollutants, discussed before, there are two additional types of pollutants: particulate pollutants and biological pollutants; several commercial products are available for detecting this kind of pollutants. For instance, the company JMar BioSentry™ developed a device which can monitor the particles in the water [START_REF] Biosentry | [END_REF] . The device works based on laser technology and on-line measurement system. Figure 1-6 shows the water particle monitoring system. Regarding pollution by biological micro-organisms, one can cite the commercial product of two startup companies: FLUIDION [START_REF]fluidion fluidic intelligence[END_REF] and Parasitometer from WATER OPTICS TECHNOLOGIES [START_REF]Water Optics Technology[END_REF] . These companies developed an effective warning detection system for bacteria and total coliforms, on one hand, and cryptosporidium, on the other hand, respectively. Both systems rely on advanced optofluidic technologies but they remain bulky and rather expensive, which makes them not suitable for large-scale deployment.

Introduction

The considered MPSC has different versions and the fabrication processes differ with respect to the physical structure of the corresponding versions which came out sequentially through the project with increasing sophistication as well as increased capabilities in terms of co-integration of various kinds of sensors and related performance. The chip versions vary from each other according to their substrate material, sensing elements arrangement and distinct sensors' configuration and even their topography. It is challenging to obtain fully functional manifold parameters sensing elements on a small foot-print.

Therefore, in order to facilitate the fabrication, integration and measured responses readability, a homogenous transduction principle is adopted for all the sensors, which is the resistive read-out. This means that all the sensors' responses to the corresponding different parameters of interest will be expressed in terms of a resistance variation. The reason and the benefit of selecting resistive sensing elements for the multi-parameter will be illustrated in the next section. In this chapter, the evolution of the different MPSC versions and their fabrication details will be introduced.

Choice of Resistive Readout

The data reading procedure based on resistive readout is the process of measuring a specific parameter by the means of resistance variation of the sensing element of a sensor device. Both the chemical and physical sensing elements of the MPSC are operating based on this principle. Each of the chemical sensors changes their resistance level according to the presence of the selective chemical substance in the water.

Likewise, the physical MEMS sensors also provide an indication varying their resistance value as they are introduced to the corresponding different physical parameters of the water. In such case, each sensor has to be designed so that it is selectively sensitive to a given parameter solely -or mainly. There are many advantages of using resistive readout for all sensors of the sensing system. Resistive sensing is one of the simplest transduction manners which allow easy electronic readout in comparison with the capacitive sensing for instance [START_REF] Winncy | Resistive and Capacitive Based Sensing Technologies[END_REF] . Furthermore, when comparing resistive and capacitive transduction schemes, it appears that capacitive transducers exhibit high impedance, which makes them behave such as antennas, in a sense that they are very sensitive to surrounding electromagnetic radiations, which can lead to parasitic signal at the sensor's output. On the contrary, resistive transducers have low impedance; they are therefore ideal for immunity to electromagnetic interference. This is of particular importance when considering multiple sensors on the same chip that may interfere with each other. From this point-of-view resistive readout is expected to minimize crosstalk between neighbour sensors. Besides the sophisticated microfabrication technology, constructing resistive sensing elements are simple as well, which involve mainly patterning of thin-film materials. Moreover, the resistive sensing system allows fabricating multiple sensors on a small footprint since the resistance value scalable controlling the ratio of a length over width. For instance, the pressure sensor of the MPSC is made of polysilicon strain gauges, which measure the mechanical tensile and compressive stress levels on a thin membrane-based on the piezo-resistive effect, which involves a resistance variation. This kind of piezo-resistive pressure sensor has high gauge factors and it can sense very small strain. On the other hand, the realized temperature and flow-rate sensor are based on a thin-film RTD (Resistance Temperature Device) made of platinum that exhibits a significant variation of the resistance value due to a small variation in temperature. The high sensitivity and linearity of such resistive devices makes them extremely useful for this kind of applications. 31

Fabrication

As it was mentioned above, several versions of the MPSC were fabricated and tested in order to find out the suitable form of the MPSC through successive improvements. The primary version of the MPSC was fabricated on a glass substrate and the subsequent versions are on silicon. The substrate material was switched from glass to silicon so as to enable co-integration of the pressure sensor with the remaining sensors on the same chip. In the primary version, all the physical sensors -except the pressure sensor, were successfully co-integrated on the glass substrate using a simple process involving only metal micropatterning. The co-integration issue will be discussed in the following sub-section. Based on the substrate material and fabrication process, the whole fabrication sequence can be divided into some sub-categories such as: (i) metal on a substrate -either glass or silicon, (ii) silicon substrate with back-side etching, (iii) silicon substrate with front-side etching, (iv) silicon substrate with both the front and back-side etching.

Metal on Glass and co-integration challenge

The primary prototype of the MPSC was fabricated on a glass substrate. This version has the allocated space for the chemical sensors and three physical sensors: water conductivity, temperature and flow-rate, which were successfully co-integrated on that version. The three physical sensors were realized by easy At first, the platinum (Pt) was deposited on a 500 µm thick glass substrate by metal sputtering. The Liftoff process was used to create the metallic pattern of the sensing elements. In some trials, there was an additional thin surface coating layer (10 nm) made of Al2O3 erected by the Atomic Layer Deposition (ALD)

technique. The aim of this coating layer is to protect the sensor chip against the biofouling under the water environment. A similar coating layer also created using TiO2 in some versions in order to identify the most efficient protective layer without degrading the sensitivity of the sensor; particularly in the case of the conductivity sensor, whose metallic electrodes are directly exposed to water.

Hence, the first version contains all the sensors except the pressure sensor. The pressure sensor cannot be fabricated in this version together with the others due to the substrate material constraint. The ascertained pressure sensor is a piezo-resistive sensor whose working principle depends on the deflection of a thin membrane on the top of which strain gauges can sense the corresponding mechanical strain. Therefore, a thin membrane is an indispensable part of the pressure sensor. Using silicon is the conventional method to create such membrane together with strain gauges so as to realize such pressure sensor [START_REF] Mosser | Piezoresistive pressure sensors based on polycrystalline silicon[END_REF] . In conclusion, the silicon substrate is mandatory for the MPSC as a substrate material for accumulating all the sensors on the same chip.

As a consequence, a co-integration issue arises regarding the pressure sensor with others from the perspective of the substrate material. In the following section, the challenges involved in the co-integration process and its impacts on the sensor performance will be addressed comprehensively.

Metal on a silicon substrate and first full co-integration of the whole sensors

Here, in this version silicon was used as the substrate instead of glass and another glass wafer was used as a support under the silicon so as to obtain a sealed vacuum cavity. The pressure sensor membrane was created using single-crystal silicon layer of an SOI substrate while polysilicon was used to achieve the strain gauges on top of this membrane. The fabrication procedure starts with a pressure sensor, henceforth the others. After the initial cleaning of the wafer, the process starts with a thermal oxidation. The resulting silicon dioxide (SiO2) layer is necessary to reduce the conduction heat loss since there is some Joule heating due to the thermo-resistive sensors, like flow-rate and temperature sensors. Indeed those sensors require such thermal insulation because the thermal conductivity of the silicon is quite high. Especially, the importance of thermal insulation layer is somewhat more critical in the case of the flow-rate sensor. Such an insulation layer was not required with glass substrate since glass has inherent high thermal insulation property. The thickness of the SiO2 insulation layer on top of silicon is about 450 nm. Afterwards, a 400 nm thick polysilicon layer was deposited on the top of the oxidation layer in order to create the strain gauge.

The latter step was the hard mask patterning of back-side and subsequent backside etching of the silicon substrate. The etching was performed using the DRIE method. Hence, the last step is to remove the hard mask for enabling the silicon-glass anodic bonding at the wafer level. The schematics of the main fabrication phases are presented sequentially in All the proposed sensors are co-integrated on the same chip successfully using silicon as the substrate material. Hence, this success could not lead to the conclusion due to the negative side effect of the silicon substrate on the flow-rate sensor performance. Indeed, the optimal heating condition is essential for a thermal flow-rate sensor for its proper transduction. Therefore, an efficient thermal insulation is requisite between the heater resistor and the substrate material. Due to the high thermal conductivity of the silicon, the heating element of the flow-rate sensor losses heats at the substrate through the conduction process. As a consequence, the sensitivity of the flow-rate sensor degraded significantly with respect to the water flow.

The related experimental results are presented in Chapter 4.

Hence, the rather high conductivity of the silicon substrate material and its negative consequence on the flow-rate sensor performance arise a critical situation. No alternative can be considered other than the silicon to fabricate the pressure sensor and using silicon will debase the flow-rate sensor's sensitivity. The further increment of the insulation layer thickness is not possible due to the fabrication limitation on thermal oxidation. So, to overcome this problem, geometric and topographic optimization appeared as the only options. The physical configuration of the flow-rate sensor had to be modified to reduce the conduction heat loss. In the beginning, a membrane was introduced under the flow-rate sensor which was created according to the similar manner as like the pressure sensor to minimize the heat loss. The schematic crosssection view of the membrane-based flow-rate sensor is provided in Figure 23. The inner medium of the membrane is a vacuum, so it is expected to improve the insulation level. The obtained measurement of the membrane-based flow-rate sensor exhibits insufficient improvement.

Therefore, further optimization was required for diminishing the heat conduction.

The ensuing structure of the flow-rate sensor is a pillar-like structure, where the heating resistor is fabricated on a silicon pillar. The detail of this configuration is presented in the section below.

Silicon substrate with front-side etching: attenuation of the negative effect of the high thermal conductivity of the silicon substrate

The pillar structure flow-rate sensor is obtained by front-side DRIE etching. Several numerical parametric studies were conducted in order to extract the appropriate design parameters such as aspect ratio of the pillar and the cavity. The obtained numerical results express that the silicon pillar configuration exhibits higher sensitivity than the glass-based flow-rate device within the velocity range of interest. The extracted experimental responses showed a good agreement with the numerical one. It was observed from the numerical study that there is an inverse relationship between the pillar widths, hence the resistor widths and the sensor's sensitivity. Therefore, different versions of the pillar structure device were fabricated with different pillar widths and heights in order to study their impact on the sensor's sensitivity. This pillar structure flow-rate sensor solves the concern about the dual complexity-substrate material and the sensor performance regarding the co-integration process.

Flow-rate sensor on a silicon substrate with suspended heater

Flow-rate sensor with the suspended heater is a successive version of the pillar structure flow-rate sensor intended to achieve further improvement of the thermal insulation and hence on the sensor's sensitivity.

The pillar structure flow-rate device evinces optimal sensitivity that is sufficient for the real application.

Hence, the suspended configuration was proposed for further investigation about the diminution of the thermal conductivity in terms of geometric optimization. This suspended structure can be considered as a bridge, where the heater resistor of the flow-rate device is fully suspended from the substrate and the two ends are connected. Several versions were fabricated with different cavity depth and resistor's width. This device was fabricated very recently and due to the time constraint; the experimental measurement study could not be conducted. As a consequence, the results on this bridge structure flow-rate sensor are not included in Chapter 4. The 2D cross-section view of the bridge structure flow-rate sensor is shown in Figure 2345. The fabrication process of the bridge configuration is quite similar to the pillar structure flow-rate sensor.

The pillar pattern was obtained by an etching sequence including anisotropic etching and passivation followed by anisotropic etching step to release the bridge.

According to the fabrication details, it appears that the pressure sensor has a strong relation in terms of the substrate material choice and different versions of the MPSC. The evolution of different MPSC versions is largely influenced by the pressure sensor co-integration with the remaining sensors. Therefore, it is pertinent to present the pressure sensor here in this chapter. The conductivity sensor will be presented here after the pressure sensor as well. The reason for the inclusion of the pressure and conductivity sensor in this chapter is due to their easy comprehensible physical structure and functionality with no intrinsic novelty.

Only co-integration constraints are the main issues to deal with during the design phase and the corresponding elaboration on the fabrication process compatibility, allowing full integration of those sensors with all other sensors. Before presenting more details on these two sensors, it is reasonable to discuss first how the sensor chip was electrically connected in order to measure their responses. Different electronic circuits and software interfaces were used to conduct the experiments for different sensors. Besides, a single PCB was used to connect the multi-sensor chip to the electronic circuits. The PCB was designed especially for the MPSC, which provides the option to connect the sensors separately. The detail of the PCB is presented in the section below.

Packaging of the MPSC into a Printed Circuit Board (PCB)

The physical shape of the PCB is like the English alphabet 'T'. The PCB along with the sensor chip is presented in Figure 23456. The aim of this particular design of the PCB is to make it compatible with the different experimental setups since the MPSC contains several sensing elements and the test setup is differing for each sensor. The height of this T-shaped PCB including the base is 7.2 cm and the width of the body part is 1.18 cm. The base is 0.89 cm in height and 3.82 cm in width. There are two sets of connection holes in the base of the PCB for connecting the sensors and each set contains 24 holes. The upper set is dedicated to connecting the lower part's sensors of the MPSC and the beneath holes are for connecting the sensors which are fabricated on the top part of the chip. On the top part of the PCB, a squareshaped space is created to place the sensor chip on the PCB and the dimension of this space is 1 x 1 cm. An adhesive is used to glue the chip on the PCB. The electrical connection between the chip's connection pads and the PCB's wire is done by gold wire bonding and a UV-cured epoxy is used to protect the wire bonding connection against water.

First Phase of the Characterization: Non-Thermal Aspects

Regarding the design details and characterization results of each type of sensor that we integrated on the MPSC platform, we have chosen to present in what follows, first, those sensors in which thermal aspects are neither fundamental nor critical for their operation principle, although the temperature may affect their response. These sensors are the pressure sensor and the water conductivity sensor. Later, another class of sensors will be considered, where thermal aspects are fundamental and require in-depth elaboration. Those are the temperature sensor, presented in Chapter 3 and the flow-rate sensor, presented in Chapter 4.

Pressure sensor design and characterization

The pressure sensor is well known in the MEMS area. It has diverse use in different fields for different applications i.e., automobile, aeronautics and industrial process control in the high-pressure industrial environment; besides in the field of bio-medicals under low-pressure environment [START_REF] Eaton | Micromachined pressure sensors: review and recent developments[END_REF][START_REF] Pramanik | Design optimization of a high performance silicon MEMS piezoresistive pressure sensor for biomedical applications[END_REF] . Other than the aforesaid applications, it has another promising suitability to monitor the water distribution network for reducing the non-revenue water loss ii . Water loss due to the distribution network defect is a paramount issue for the respective authority in order to reduce the operational cost and saving the precious natural resource.

According to the Energy and Water Department of the World Bank, the expenditure of the water

ii Non-revenue water (NRW) means the amount of water that does not generate any revenue. It is defined by the difference between the amount of the water supply to the distribution network and the amount of the water for that the consumers pay the bill [START_REF] Kingdom | What is non-revenue water? How can we reduce it for better water service? The Water Blog[END_REF] .

Chapter 2 29 adaptabilities due to the NRW is around fourteen billion dollars all over the world; one-third of that cost take place in the developing countries [START_REF] Grundfoss | The advantages of water pressure management[END_REF] .

The water wastage due to the leakages in the distribution network and overflow from the reservoir can be minimized significantly by proper pressure management hence, monitoring the water pressure. The conventional technique of tracing the leakages in the distribution system is replacing the pipe or repairing.

Due to the long period (a few weeks or a month) of such process completion causes significant amount water loss [START_REF] Landeros | Detection using pressure sensors and mathematical models Water Loss[END_REF] . Therefore, to avoid such unwanted losses proper pressure monitoring in the water distribution system is the most fruitful solution.

Numerous techniques and measurement devices have been developed for regulating the pressure management in the water distribution network. Among the existing devices and techniques, micromachined devices are popularized due to their low-cost production and large-scale positioning. Different transduction principles are followed by the micro pressure sensing devices i.e., capacitive, resonance, optical etc. [START_REF] Eaton | Micromachined pressure sensors: review and recent developments[END_REF] . Piezo-resistive sensors are the smart choice among the prior mentioned micro-sensors for this specific application because of their convenient operation mode and moderate sensitivity to a wide pressure range with high resolution [START_REF] Pramanik | Design optimization of a high performance silicon MEMS piezoresistive pressure sensor for biomedical applications[END_REF] .

The realized pressure sensor of the MPSC is a piezo-resistive pressure sensor, based on silicon thin membrane. On the top of the membrane, there are four piezo-resistive strain gauges which placement is according to the Wheatstone bridge. The functionality of the membrane is involved in transforming the hydrostatic pressure into mechanical stress on the piezo-resistors. The thin-film membrane was created using the single-crystalline silicon of the upper layer of an SOI wafer. Strain gauges/resistors were made of thin-film polycrystalline silicon deposited by LPCVD on top of this membrane. In this case, the resistors were fabricated using polysilicon instead of the usual material because of their higher piezo-resistive sensitivity [START_REF] Sanchez | Semiconductor strain gauge[END_REF] . The layout of the considered pressure sensor is illustrated in Figure 234567.

Chapter 2 The related results of the numerical study are reported in Annex 1.

Experimental setup

In the experiment, the sensors' responses were extracted as a function of the air pressure. The sensor was inserted into a glass bottle and the variable hydrostatic pressure was generated inside the bottle with the Fluigent SA device. The Fluigent SA can produce a pressure level up to 7 bars. The experiments were conducted until 3 bars maximum for the safety issue. The principal aim of the experiment is to inspect the initial response of the fabricated sensor. The measurements were obtained under hydrostatic air pressure;

hence it is more reasonable to conduct the measurement underwater environment. Evaluation under the water can be performed using an intermediate water reservoir. The air pressure will be generated in the closed water reservoir, therefore the sensor' response will be measured. The photo of the test setup is demonstrated in Figure 2345678. The data reading circuit was built with an instrumentation amplifier AD620an. A DC voltage source was used to supply the power to the piezo-resistor Wheatstone bridge and the bridge output was connected to the instrumentation amplifier input. The bridge output will be varied linearly according to the applied hydrostatic pressure since the strain gauges are chained due to the pressure. The final output was collected from the amplifier end. A 5 kΩ gain resistor was used to fix the gain at 10, but it can be changed to any other values according to the desired gain level. The schematic of the circuit is reported in Figure 23456789. From the plots, it can be observed that both the sensors exhibit high linearity where the coefficient of determination values are higher than 0.99 with respect to the pressure increment. The output voltage magnitude differs for the two versions due to variation in the fabricated piezo-resistors' values.

Chapter 2 33

Conductivity sensor design and characterization

Conductivity

Conductivity is the ability of a substance i.e., metal, liquid and gas to conduct the electricity. The electricity is conducted through a metal by its free electrons. Hence, electricity cannot pass through an insulator since it does not contain any free electron. Likewise, the electricity passes through a fluid medium by the cations and anions of the corresponding fluid.

From this last statement, it appears that water conductivity is an efficient mean of measuring the ionic concentration of water. Hence it gives a rough idea of the ionic contaminants of water. Water conductivity can be regarded as an indicator of water quality, among other indicators, such as water turbidity for instance, which can be assessed by visual/optical manner.

Conductivity and conductance differ from each other. Conductance is related to conductivity, it is dependent on the length of related substance as like the term resistance [START_REF] Nave | Resistance and Resistivity[END_REF] . On the other hand, conductivity is related to the resistivity. Conductivity is correlated to resistivity but reciprocal. Conductance is measured in mho (or Ohm -1 , the reciprocal of Ohm) also named Siemens. The link between conductance and conductivity involves a surface to length ratio, similar to the link between resistance and resistivity.

Conductivity is measured with the unit of the conductivity is S/cm [START_REF]YSI Model 3200 Conductance, Resistance, Salinity, Total Dissolved Solids and Temperature Instrument[END_REF] .

Some basic equations are recalled hereafter, which are useful to understand the experimental measurement protocol.

Conductance (G) is defined as the reciprocal of the electrical resistance (R) of a solution between two electrodes: G = 1/R. On the other hand, electrical conductivity σ is defined as the ratio between the current density (J) and the electric field intensity (E) and it is the reciprocal of the resistivity ρ, whose unit is Ohm.cm. J/E = σ = 1/ρ. The SI unit of conductivity σ is Siemens per meter (S/m). However, practically, it is not straightforward to measure current density J and electric field E. Therefore, the water conductivity ρw is derived from measurements done on the water resistance Rw, which is more easily attainable through the measurement of both voltage V and electrical current I. Indeed, from Ohms' law: V= Rw I, where V is the voltage difference across the water plug, Rw is the electrical resistance of this water plug and I is the current flowing through the electrodes placed at the borders of the water plug.

We also know the following relation between resistance Rw and water resistivity ρw: Rw =ρw L/S. Here L and S is the length and surface area of the water plug, respectively. Now this equation can be rewritten as:

Chapter 2 34 Rw =1/σw (L/S) ( 2-1 )
Replacing the resistance Rw by the ratio V/I, we obtain V=1/σw (IL/S) that can be finally rewritten in, the following form:

1/V= σw (S/IL) ( 2-2 )
In the latter equation, 1/V is the reciprocal of the voltage V. It appears directly proportional to the water conductivity σw, with a proportionality factor (S/IL) that can be seen as a scale factor, which can be determined through a calibration phase. In such a calibration phase, we need to use calibrated water samples, which means the whose value of σw-calibrated is known a priori. Then, plotting the curve (1/V) versus σw-calibrated gives a linear response, whose slope is (S/IL). After completion of such sensor's calibration, it can be used to read water samples of unknown conductivity.

Water conductivity

It is mentioned in the previous section 2.7.1, that the conductivity of a fluid depends on the ions concentration. So, the conductivity hence the ability of electrical conduction of water depends on its ions concentration [START_REF]Monitoring and Assessing Water Quality -Volunteer Monitoring | Monitoring & Assessment[END_REF] . The ions in the water are produced by the dissolved salts. Moreover, some inorganic materials such as-chlorides, alkalis, sulfides and carbonate compounds also increase the ions in the water [START_REF] Miller | Specific conductance; theoretical considerations and application to analytical quality control[END_REF] . These dissolved compounds are known as electrolytes. If a water sample contains a high concentration of ions, then its conductivity will be higher. For this reason, the conductivity of the sea water is comparatively high. On the other hand, distilled or deionized water behaves like an insulator due to the low ions present in the water 43 with a lower theoretical limit of the conductivity equal to 0.04 µS/cm at 20°C and even down to 0.01 µS/cm at 0°C for ultrapure water [START_REF] Light | The Fundamental Conductivity and Resistivity of Water[END_REF] , in an ideal situation where the only ions are H3O + and OH -.

Typical water conductivity values

There is no standard value of the water conductivity for different water sources. Each of the natural sources possesses different conductivity ranges. Usually, the conductivity of a specific waterbody depends on the geological surrounding. Due to the geological effects, a freshwater source has a wide conductivity range. Some lakes exhibit high salinity or conductivity because of the restricted outflow and the presence of particular ionic compositions [START_REF] Wetzel | Limnology: Lake and River Ecosystems[END_REF] . In the case of a river, the conductivity varies a lot since the river water consistently influenced by the flow of the fresh and salt water. On the other hand, the conductivity of the seawater depends on the salinity and the temperature of the water [START_REF]Properties of seawater[END_REF] Industrial wastewater 10000

Importance of conductivity

In order to determine the water quality, the conductivity hence the specific conductance is one of the most commonly measured parameter [START_REF] Miller | Specific conductance; theoretical considerations and application to analytical quality control[END_REF] . Besides, the conductivity information of a water body is used to calculate the salinity and total dissolved solids (TDS). Generally, a water body has a stable conductivity level which can be considered as a reference value for comparison with future measurement [START_REF]Monitoring and Assessing Water Quality -Volunteer Monitoring | Monitoring & Assessment[END_REF] . There are several factors that can affect the conductivity of the water. Rapid change in conductivity of a water source may occur if the water flows over a soil which is rich in salt and minerals. In addition, natural flooding, evaporation, and manmade pollution can also vary the normal conductivity of water body.

The deviation of the conductivity by any portion from the normal value of a water body indicates the occurrence of a contamination. Addition of any chemical compounds, particularly the chloride, phosphate and nitrite ions due to the sewage leakages or agricultural runoff will increase the conductivity of the water [START_REF]Monitoring and Assessing Water Quality -Volunteer Monitoring | Monitoring & Assessment[END_REF] . Moreover, an amalgamation of the elements those are not decomposed into ions in the water or accidental oil spill over to the water will actually decrease the conductivity [START_REF]Water quality indicators[END_REF] . Hence, any increment or decrement of the usual conductivity level has a negative impact on the water quality.

Introduction of the conductivity sensor and its working principle

The considered conductivity sensor consists of four probes, where two of them for injecting current to the operation medium and remaining two for measuring the wafer voltage. The conductivity information σ 𝑤 is obtained according to the equation below (as discussed in section 2.6.1)- Based on the detection mode, the conductivity sensor can be categorized into two categories-contact mode and contactless mode [START_REF] Pumera | Contactless conductivity detection for microfluidics: Designs and applications[END_REF] . In contact mode, the electrodes are directly in contact with water and in the contactless mode, the electrodes are isolated from the water with a thin coating layer of few nanometers Chapter 2 37 made of a high dielectric permittivity material. This coating layer works against the oxidation of the electrodes and biofouling during the continuous measurement under the water. Thus, this thin coating layer enhances the lifetime of the sensor. [START_REF] Huck | Capacitively coupled electrolyte-conductivity sensor based on high-k material of barium strontium titanate[END_REF] Three different versions of the conductivity sensors were fabricated, where they differ according to their electrode dimensions. The electrodes were realized with different dimensions (taking into account the parameters L and S in the above equations and also some edge effects) in order to study their impact on the sensors' sensitivity. Chapter 2

σ 𝑤 = ( L S ) I/V ( 2-3 )

Experimental setup

The realized conductivity sensor was characterized by different reference conductivity solutions. The reference solutions were prepared using a commercial portable conductivity meter named Mettler Toledo FG3 FiveGo. Five solutions of different conductivity levels ranging from ~500 µS/cm to 2500 µS/cm with a step of nearly 500 µS/cm were prepared. The preparation of the conductivity solutions was involved in adding salt to distilled water and then measuring the resulting conductivity using the reference conductivity sensor Mettler Toledo FG3 FiveGo, which was previously calibrated using a set of calibration standards.

For the purpose of preparing the different solutions, distilled water was taken in a small glass beaker and different conductivity solutions were obtained by varying the amount of dissolved salt. The sensor was connected to the voltage data reading electronic circuit using the particular PCB and the reading circuit was connected to the computer through the NI DAQ system. In this experimental setup, NI DAQ system not only uses for voltage (V) data acquisition, but also it generates the required AC current supply (I) for the Chapter 2 40

Results and Discussion

In section 2.7.2 three different configurations (Figure 2-13) of the conductivity sensor were illustrated.

The experiments were conducted with these three devices within the mentioned conductivity range (from ~500 µS/cm to ~2500 µS/cm) at different AC supply frequencies. The AC power supply was chosen in order to avoid the electro-erosion and redox of the electrodes and the optimum operating frequency range was determined after behavioural modelling (See Annex 2).

The supply source frequencies were 5 kHz, 10 kHz and 15 kHz while determining the sensors responses' in terms of different reference solutions. These three frequencies were determined from the frequency sweep analysis. In this case, the sensor was submerged under a specific reference solution and the frequency of the supply source was varied from 30 Hz to 49 kHz. The extracted response expresses the operating frequency range for linear operation of the corresponding sensor. The comparison between the sensors was studied in different perspectives like-the linearity with respect to the reference conductivity solution, coated ALD material, with/without ALD layer.

In Figure 2 It can be seen from the above figure that the sensor S1 exhibits a completely inconsistent response to different conductivity solutions under the three frequencies. On the other hand, the fitted regression values for the sensors S2 and S3 indicate excellent linearity within the whole conductivity range for the three operating power supply frequencies. From Figure 2-17(a), it obvious that the S1 cannot be used to determine the conductivity of a sample. Therefore, this version will be skipped in the forthcoming discussion. Hence, the sensors S2 and S3 express almost similar sensitivity under identical condition; so for comparison sake i.e., in terms of the ALD coating layer, sensor S2 will be considered. Indeed, there is no specific physical structure difference besides the electrodes arrangement between these two sensors.

The presented results in Figure 2-17 are involved with the silicon-based device coated using ALD with Al2O3. In order to investigate the impact of the ALD layer on the sensor's sensitivity, the obtained response of S2 with ALD was compared with the same device response that was fabricated without ALD coating.

The comparison plot is showed in the figure below. Here it can be observed from the plot (Figure 2-18) that, the coating layer reduces the sensor's sensitivity. Under at a certain frequency which is 5 kHz here in this case, the magnitude of the sensor's response decreases gradually as the magnitude of the reference conductivity increases. The apparent measured conductance is around 0.62 mS at the maximum conductivity when there is no ALD layer on the sensor. Hence, under a similar situation, the magnitude of the measured conductance obtained by the same device is decreased to 43.55 % of the previous value at the maximum reference conductivity with ALD layer. Indeed, the sensor will be implemented in the water distribution network; therefore, there is a high possibility of biofouling generation for a long run implementation. So, the ALD coating layer is potentially important to protect the sensor from the biofouling hence increase the lifetime of the device. But definitely, this will be at the expense of a reduced sensitivity, which was expected due to the serial impedance introduced by the ALD coating.

In order to distinguish the most favourable ALD coating material, another prototype of the same device was coated by TiO2 with same thickness (10nm). The extracted responses of the device at different The conductivity sensor S2 with TiO2 ALD layer also exhibits higher sensitivity with respect to the reference solutions under the three operating frequencies. The coefficients of the determination from Figure 2-19(a) denote that the sensor S2 has higher sensitivity with TiO2 ALD layer. From Figure 2-19(b), it can be observed that the TiO2 ALD layer discloses the higher sensitivity than the ALD layer Al2O3 even better than the device without ALD, which is an astonishing result, not yet understood. The measured data of the device without ALD shows an intermediate response between the TiO2 and Al2O3 ALD layer. Therefore, either S2 or S3 can be determined as the final version of the conductivity sensor with TiO2 ALD coating layer.

Evaluation of Antifouling Properties of Al2O3 / TiO2 ALD-coatings

In order to evaluate the effectiveness of the ALD coating layer in terms of anti-fouling, real field tests were conducted with the sensor chips incorporation with the collaborator SMAS from Portugal. ALD has the main advantages of (i) conformal deposition, hence ensuring that all the surface of the sensitive parts of the multi-parameter sensor chip is properly covered by the coating material (ii) the thickness can be atomic up to a few tens of nanometer, hence not introducing significant electrical perturbations to the sensor's operation. The material TiO2 was selected for its well-known photocatalytic properties, which can help to deteriorate any organic material that accumulates to the surface. Al2O3 was also found in the literature as a potentially good candidate for an antifouling coating. For the test, the samples with TiO2 at 10 and 30 nm thickness and Al2O3 at 10 nm thickness were considered. Tests were conducted with different water like-wastewater, treated drinking water and raw water.

The testing protocol was established to compare the growth of biofilm on the surface of different types of toppings of the sensor node, being used as a reference board with no cover, and three others with alumina (10 nm) and titanium oxide (10 to 30 nm). The table below summarizes chip characteristics: In order to replicate the real scenario, a few dummy PVC tubes were prepared with a flat head similar to the sensor node for mounting the sensor chip. The chip was attached to the PVC head by epoxy resin.

Prior to this, the chip was cleaned with IPA solvent. The considered four chips were attached to the PVC head and afterwards, the PCV tube along with chips was put inside the oven at 65 °C to ensure the proper adhesion. Luminometer EnSure (model V.2, Hygiena, US) was used for measuring the growth of biofouling on the surface of the chip. Compatible calibration probe was used along with the device and ATP swab used for collecting the samples from the sensor chip (See Annex 3).

Test in Wastewater

The tests were mainly aiming at evaluating the capability of the sensor to continue to carry out measurements in residual water, considering that there should be a rapid deposition of substances on the surface of the device, preventing it from effective readings. Obtained results under the wastewater environment are reported in the table below.

From the obtained results, it can be observed that whatever the coating on the sensor chips, in such wastewater conditions, the deposit quickly covers the entire testing device. This first result with dummy sensor chips highlights the difficulty of measurement with microsystems in wastewater. It can be also seen from the data that there are no significant differences observed when comparing the devices that have the anti-fouling coating with those that did not have such a coating.

Chapter 2 47 Therefore, the sensor chips are subjected to biofouling in wastewater and that a nanometric thickness of either TiO2 or Al2O3 is not efficient enough to prevent biofouling, or even may increase biofouling.

Test in Drinking water (treated and raw)

This test aims at testing for biofilm growth and limestone incrustation, depending on whether water with biocide (hypochlorinated acid) or water still without a sanitary barrier is considered.

The test results undertreated and raw drinking water condition show inconsiderable growth of deposit.

For the maximum test duration (173 days) only the sensor chip with TiO2 30nm thick ALD exceeds 100 RUL and rest of the cases it exhibits only 2 orders of magnitude.

Hence, for both the cases, no significant biofouling is observed in comparison with the case of wastewater. Therefore, there is no need for an anti-fouling protection layer at least for periods up to 6 months (173 days). Moreover, the reported results in

Introduction

Temperature is among the most commonly measured parameters, whatever the medium. Precise and localized temperature measurement is required for thermal monitoring and processes control 52 . It is worth mentioning that temperature measurement is performed not only to extract the temperature as an intrinsic environmental parameter, but also to have a better accuracy on other temperature-dependent parameters.

Indeed, a large variety of physical parameters depend on temperature and they require very often a correction, based on the value of temperature. This is true in particular for all water parameters considered in this work on multi-parameter water sensing probe, namely: pressure, conductivity, flow-rate and concentration of the various chemical contents.

Besides their primary function of giving information on the actual temperature, it appears that temperature sensors are also important building blocks in many other kinds of sensors. They indirectly give access to many other parameters. Among others, they are used in Pirani gages for vacuum measurement, in bolometers for infrared radiation energy measurement and in flow-rate sensors based on convective heat transfer. All those indirect measurement schemes involve heat transfer mechanisms.

Therefore, based on what precedes temperature sensors appear as important, multi-purpose building blocks.

Accordingly, a water temperature sensor is included in the MPSC to determine the temperature of water, which also enables accounting for other parameters temperature dependence. Furthermore, such temperature sensor will also serve as the basis of the architecture of a flow-rate sensor as described in chapter 4.

In this chapter, thermometry and its fundamental principles will be reviewed first along with different temperature measurement techniques. A special attention will be paid to temperature measurement based on resistive elements: Resistance Temperature Detectors (RTD), Thermistors and Thermocouples. The manifold applications of a thin-film RTD are also reported here; where it has been shown how fascinating can be the use of a simple resistor, as it can be employed for different applications with a simple modification in its operation scheme. The chapter will be ended with a discussion on temperature sensor used as a part of the MPSC to measure water temperature.

Water temperature variations and its impacts

Natural and anthropogenic factors impacting water temperature

In the case of natural water source or water networks, water temperature is affected by both natural causes, which include Sunlight, Turbidity, Atmosphere and Confluence. It is also affected by anthropogenic factors, such as Impoundments, Deforestation and Thermal pollution.

Sunlight is the main source of heat which increases water temperature of different natural sources i.e., river, ocean, pond, lake. Sunlight easily increases water temperature since water has very low albedo iii [START_REF]Solar Radiation at Earth -Windows to the Universe[END_REF] .

Therefore, high absorption of solar energy leads to the rapid temperature increase of water. Hence, the temperature of a waterbody varies daily depending on the amount of absorbed sunlight. Among the different layers of the water in a source, the light only passes through the photic zone iv . Generally, the depth of the photic zone depends on the presence of solid and other light-scattering elements in water. For instance, an ocean can have a 200 m deep photic zone [START_REF]How far does light travel in the ocean? Available at[END_REF] . Almost more than half of sunlight is absorbed by the first 2 meter of water [START_REF] Wetzel | Limnology: Lake and River Ecosystems[END_REF] . As a consequence, the superficial bodies of water become warm faster than the deeper parts.

Turbidity-The amount of suspended solid particles in water is known as turbidity and it has an impact on the water temperature as well. Heat absorption capacity of the solid particles is higher than the water and the absorbed heat is then directly transferred from the floating particles to the water [START_REF]Monitoring and Assessing Water Quality -Volunteer Monitoring | Monitoring & Assessment[END_REF] . So, turbid water tends to warm rapidly compared to pure water.

Atmosphere-atmospheric temperature also plays a role to alter the temperature of the natural water sources and reservoirs. If the surrounding air temperature is higher than the water, then heat will be transferred to water. On the other hand, water will lose heat and become colder if the adjacent air temperature is lower than the water. In this case, the heat transfer depends on the thermal inertia and on the specific heat of the water [START_REF] Wetzel | Limnology: Lake and River Ecosystems[END_REF] .

Among these three anthropogenic factors, impoundments and deforestation do not cause significant water contamination. Instead, these two phenomena have a role in water temperature variation of natural sources that affect the local aquatic life. In some cases, the dam without flow controlling sliding gate may increase the downstream water temperature. Further, the unusual release of hot and cold water from the dam also affect the normal temperature of the corresponding aqua environment [START_REF]Temperature -Water Properties[END_REF] . On the other hand, plant shadow protects the water surface from direct sunlight that prevents the water become hot. Deforestation

iii The term Albedo refers the ability of a surface to reflect incident solar light mainly by diffuse reflection. iv Light reaching zone or the uppermost surface of the river or lake which expose to the intense sunlight.

increases the temperature of the pond, lake in the forest region [START_REF] Wetzel | Limnology: Lake and River Ecosystems[END_REF]56 . Hence, impoundments and deforestation hinder the spawning and hatching process. Besides, thermal pollution has a significant effect on water quality. The river or water ducts close to the residential or industrial area are often confronted with thermal pollutants that increase the temperature of those waterbodies. The sudden change in the water temperature reduces the amount of dissolved oxygen and aquatic animal i.e., fish [START_REF] Wetzel | Limnology: Lake and River Ecosystems[END_REF]56 .

The impact of water temperature on other parameters governing water quality

Temperature is one of the governing parameters that should be considered prior to evaluating water quality since several physical and chemical water properties strongly depend on temperature. A small alteration of water temperature may bring a significant variation of its normal characteristics. Water electric conductivity and Water Temperature: Conductivity depends on the concentration, charge and mobility of ions. Ions' mobility depends on the viscosity of the corresponding liquid [START_REF]Definition of VISCOSITY[END_REF] . In a high viscosity liquid, ions mobility is very low. Besides, viscosity is temperature dependent. More specifically, it is inversely related to temperature. As temperature increases, viscosity decreases. As a result, ions mobility increases. Hence, a high water temperature will induce a high electric conductivity in comparison to standard conditions due to the variation of viscosity [START_REF] Miller | Specific conductance; theoretical considerations and application to analytical quality control[END_REF] . In general, a 1 °C increase in temperature induces an electric conductivity increase by 2 ~ 3 %, and 5 % in the specific case of pure water [START_REF] Miller | Specific conductance; theoretical considerations and application to analytical quality control[END_REF] .

Chapter 3

54

Total Dissolved Solids (TDS) and Water Temperature: TDS refers to the number of solutes smaller than 2 microns dissolved in a solution [START_REF]Monitoring and Assessing Water Quality -Volunteer Monitoring | Monitoring & Assessment[END_REF][START_REF] Hayashi | Temperature-electrical conductivity relation of water for environmental monitoring and geophysical data inversion[END_REF] . It is also related to electrical conductivity of the water solution. Since conductivity depends on temperature, TDS is also temperature-dependent. Since high temperature enhances the solubility of different salts [START_REF]Temperature Effects on Solubility[END_REF] , as a consequence, when salts are dissolved, they decompose as ions.

Therefore, hot water is likely to have higher ionic concentration than cold water. Hence, TDS is affected by temperature, and the latter effect has a second-order impact on conductivity as well.

pH and Water Temperature: pH indicates the concentration of H + ions in a solution or water, which is an indication about the acidity or basicity of the solution. pH is temperature dependent since the formation of hydrogen ions from water is an endothermic process:

𝐻 2 𝑂 ⇌ 𝐻 + + 𝑂𝐻 - ( 3-1 )
Dissolved Oxygen (DO) and Water Temperature: the presence of dissolved oxygen and other gases depend on the corresponding water temperature. The solubility of Oxygen varies inversely to temperature [START_REF]Temperature -Water Properties[END_REF] . Cold water contains more Oxygen than hot water. If water temperature is significantly high, then local aquatic life suffers from low Oxygen; and longtime insufficient Oxygen may lead to its extinction.

Compound Toxicity and Water Temperature: Temperature alteration can change the water toxicity level also. As it was discussed above that the solubility of the present elements increases with the increase of temperature. So, some heavy metals such as-Zn, Pb, Cd and compound like ammonia (NH3) dissolved in the water at a high temperature which increases the toxicity of the water [START_REF] Bhadja | Effect of temperature on the toxicity of some metals to Labeo bata (Hamilton, 1822)[END_REF][START_REF]Ammonia Toxicity and pH Changes[END_REF] . Temperature above 25 °C significantly increases the mortality rate for Zn in comparison with a temperature less than 20 °C [START_REF] Bhadja | Effect of temperature on the toxicity of some metals to Labeo bata (Hamilton, 1822)[END_REF] . At high pH level, NH3 becomes toxic; but high temperature can modify the intense and chronic criteria concentrations as well [START_REF]Final Aquatic Life Ambient Water Quality Criteria For Ammonia-Freshwater 2013[END_REF] .

𝑁𝐻 3 + 𝐻 2 𝑂 ⇌ 𝑁𝐻 4 + 𝑂𝐻 - ( 3-2 )
At neutral pH, the above chemical reaction remains shifted to the left, but for every 10 °C increase in temperature the ration of unionized ammonia to ammonium become two times higher [START_REF]Final Aquatic Life Ambient Water Quality Criteria For Ammonia-Freshwater 2013[END_REF] . Water temperature not only responsible for increasing the toxicity, it decreases the tolerance margin of the local aquatic entities [START_REF] Bhadja | Effect of temperature on the toxicity of some metals to Labeo bata (Hamilton, 1822)[END_REF] . Since, high water temperature enforces the Oxygen consumption, metabolic rate and tissue permeability [START_REF] Bhadja | Effect of temperature on the toxicity of some metals to Labeo bata (Hamilton, 1822)[END_REF] .

Oxidation Reduction Potential (ORP) and Water Temperature:

The ORP of a solution also depends on temperature. Indeed, the solution temperature has an impact on chemical species i.e., atoms, molecules and Chapter 3

55 ions that exist in the solution which alter the ORP value [START_REF]Measuring ORP on YSI 6-Series Sondes: Tips, Cautions and Limitations[END_REF] . Therefore, temperature reading should be recorded and considered while measuring and analyzing the ORP data of a water body.

Density and Water Temperature:

There is a direct relationship between temperature and the water density. The density of pure water reduced to around 9% when it becomes ice [START_REF]Water Density (water properties[END_REF] . As a consequence, ice can float on the water. The pure water obtains its maximum density (1 g/ml) at 4 °C [START_REF]Water Density (water properties[END_REF] , so water with temperature other than 4 °C including the super-hot or super-cold water will float on the water with 4 °C. Density is an intrinsic property of the water and it can be related to its temperature based on the water types such as-Freshwater, Saltwater, Ice.

Metabolic Rate and Photosynthesis and Water Temperature: The metabolic rates and the biological activities of aquatic organisms are directly related to water temperature [START_REF] Wetzel | Limnology: Lake and River Ecosystems[END_REF] . This happens since the activeness of different cellular enzymes increases at high temperature [START_REF]LabBench Activity Key Concepts II: Temperature and Metabolic Activity[END_REF] . Indeed, the local temperature variation of a water body influences the habitation of aquatic life. Some fishes i.e., Trout, Salmon are comfortable with the colder environment, on the other hand, some aquatic plants prefer warm temperature to flourish [START_REF]Monitoring and Assessing Water Quality -Volunteer Monitoring | Monitoring & Assessment[END_REF] . For most of the fish, their physiological function rate becomes double to 10 °C increase in temperature [START_REF] Di Santo | Effect of rapid temperature change on resting routine metabolic rates of two benthic elasmobranchs[END_REF] . Plant respiration and photosynthesis also depend on the temperature [START_REF] Wetzel | Limnology: Lake and River Ecosystems[END_REF] . Different species of trees required a different level of temperature for favourable photosynthesis activity. Any deviation of the standard temperature value can enhance or diminish the photosynthesis rate.

Fundamentals on temperature and its measurement

Basics on temperature

Temperature is a physical quantity which provides objective information at the macroscopic scale, about how "hot" or "cold" an object or a medium is. It has close relations with heat, which is a form of energy related to the microscopic motion or vibration energy of the molecules or the particles in a substance. Upon heating, particles gain higher energy and move faster in a fluid medium or vibrate vigorously in a solid medium. In a closed system, temperature is increased when the system gains heat from its surroundings.

Temperature can be seen as an indicator of the microscopic kinetic energy of particles inside a system. At non-zero absolute temperature, and from a microscopic point of view, particles are continuously moving.

All particles do not have the same velocity and direction. Measuring temperature actually provides information on those particles average kinetic energy. A high temperature is equivalent to a fast average movement of the particles in the system.

Temperature is also related to the potential of heat transfer between two objects by different means likeconduction, convection and radiation. A temperature difference between two objects leads to a heat flow in Chapter 3

56 the manner a potential difference at the two terminals of a conductor leads to a current flow. According to the second principle of thermodynamics, heat always flows from the hot body to the cold one. Heat flow continues till the two bodies eventually reach the same temperature which is known as thermal equilibrium.

This thermal equilibrium concept is used when measuring temperature by a thermometer. In general, to measure the temperature of an object, a thermometer is placed in contact with it. The system composed of the thermometer and the considered body reaches thermal equilibrium after certain duration. Under these conditions, the temperature read on the thermometer is equal to the object temperature.

The most commonly used scale for temperature measurement is Centigrade or Celsius scale [START_REF] Rusby | The Beginner's Guide to Temperature Measurement[END_REF] . This

Celsius unit system is proposed based on the fundamental temperature interval of 100 degrees, from the melting point of ice to the boiling point of water under standard atmospheric pressure. The symbol of this Centigrade unit is '°C'. There is another temperature scale called Fahrenheit (symbol: °F) which was named after a physicist Daniel Gabriel Fahrenheit and invented before the Celsius scale. In Fahrenheit scale, the lowest temperature point is defined as 0 °F by taking as reference the temperature of the brine solution. At present, the Fahrenheit scale range is defined by two fixed temperature points-melting point of ice and boiling point of water under standard atmospheric pressure at sea level corresponding to 32 °F and 212 °F, respectively. In the case of physical science, another temperature scale is used, the Kelvin scale, named after its inventor, Lord Kelvin. Kelvin scale is known as absolute temperature scale. It is defined by ascribing the value 273.16 K to the triple point of the water. According to the definition of the Kelvin scale, the absolute zero or 0 K is equal to -273.15 °C and -459.67 °F [START_REF] Rusby | The Beginner's Guide to Temperature Measurement[END_REF][START_REF] Wikipedia | [END_REF][START_REF] Fahrenheit | [END_REF] . This scale does not admit negative temperature values. This ultimate limit of 0 K also corresponds to the zero-level of thermally-induced molecular vibrations.

Brief history of temperature measurement

The history of thermometry is quite ancient. The first thermometry application was found by a Greek physician named Claudius Galenus (AD 129 -200) [START_REF] Quinn | The foundations of thermometry[END_REF] . He used temperature measurement for clinical purposes and the basis of his thermometry technique was based on Aristotle postulation on the four basic qualities: hot, cold, dry and wet [START_REF] Quinn | The foundations of thermometry[END_REF][START_REF] Medicine | THE FOUR BASIC QUALITIES -Yin and Yang, Greek Style[END_REF] . Galenus proposed a standard neutral temperature point as well that can be made by mixing tantamount of ice and boiling water [START_REF] Quinn | The foundations of thermometry[END_REF][START_REF][END_REF] . More than thousand years after Galenus, in 1578, Hasler used thermometry to create a mixture of different drugs based on the Galenus concept. He wrote a medical book De Logistica Medica in which he explained his temperature measurement method.

In the second century BC, Greek physicist and engineer Philo Mechanicus (BC 280 -BC 220) designed an instrument which demonstrates experimentally the expansion of air on heating, but the proposed device could not provide the temperature reading. [START_REF] Quinn | The foundations of thermometry[END_REF] Chapter 3

57

A complete design description of a temperature measurement device was disclosed by Santorio in his paper Commentaries on Galen [START_REF] Quinn | The foundations of thermometry[END_REF] . Although Galileo is acquainted as the inventor of the thermometer, in reality, he is not. He proposed a device which is called a thermoscope [START_REF] Quinn | The foundations of thermometry[END_REF]73 . The advancement in temperature measurement devices has a long history and the current elevated form of those devices has been achieved by a lengthy evolution process.

Huge progress has been accomplished since those ages. Nowadays, temperature measurement methods can be divided into three types-a. Contact/Invasive, b. Semi-invasive and c. Non-contact/non-invasive temperature sensing [START_REF] Childs | Review of temperature measurement[END_REF] .

Measurement process depends on different constraints such as the operation media and principle, temperature range, level of accuracy, measurement duration, cost, size and toughness. Existing methods of temperature measurement can be classified under the above mentioned three categories.

In this report, we will not elaborate on the numerous measurement techniques for temperature measurement. Instead we will concentrate on those techniques which are based on the use of an electrical resistance, since this is in line with our primary choice of having all sensors of the MPSC based on a resistive read-out. This is particularly sound as such kind of temperature sensor can also be re-used as the building block of other types of sensors involving heat transfer, as described in this section, with further detailed description in chapter 4 dealing specifically on flow-rate sensors.

Electrical Resistance Devices

Resistive temperature measurement is one of the most widely used processes for measuring the temperature at which a resistor is used as a temperature detector. The electrical resistance of a resistor depends on its physical temperature and this fundamental concept is used to operate numerous temperature detecting devices [START_REF] Childs | Review of temperature measurement[END_REF] . For a specific metal, the electrical resistance generally depends on temperature.

Therefore, it is very easy and convenient to use metallic resistors for temperature measurement by measuring the resistance value. However, several issues need to be considered prior to manufacturing such devices such as oxidation resistivity, cost, temperature coefficient resistance, and compatibility with the operation medium [START_REF] Childs | Review of temperature measurement[END_REF] .

Different metals such as Gold, Platinum, Nickel, Silver, Titanium and Copper have been widely used to produce resistive thermometers [START_REF] Kaanta | High Sensitivity Micro-Thermal Conductivity Detector for Gas Chromatography[END_REF][START_REF] Hsiai | Micro Sensors: Linking Real-Time Oscillatory Shear Stress with Vascular Inflammatory Responses[END_REF] . A great number of resistive temperature sensors are made of platinum.

The temperature coefficient of resistance (TCR), defined as the relative change of resistance due to temperature variation ∆𝑇, that is 𝑇𝐶𝑅 = (∆𝑅 𝑅 0 ⁄ ) ∆𝑇 ⁄ , is the most important parameter when considering a material for a resistive temperature sensor. The higher the TCR value the higher the sensitivity of the Chapter 3 58 sensor. Platinum has a high TCR value and excellent linearity of it resistivity over a wide temperature range.

Some other materials have higher TCR value than platinum (Pt), but none of them show high reproducibility and chemical stability at high temperature [START_REF] Mailly | Anemometer with hot platinum thin film[END_REF] . Besides, this material has corrosion resistivity and outstanding accuracy with approximately one order of magnitude uncertainty [START_REF] Nguyen | Micromachined flow sensors-a review[END_REF][START_REF] Honschoten | Application of a microflown as a low-cost level sensor[END_REF][START_REF] Dittmann | Low-Cost Flow Transducer Fabricated with the Amanda-Process[END_REF][START_REF] Kreider | Platinum/palladium thin-film thermocouples for temperature measurements on silicon wafers[END_REF] . The value of the resistivity at ambient temperature and the TCR values of widely used materials are reported in Table 3-1. The main electrical resistive temperature sensing devices are Resistive Temperature Detectors (RTD)

and Thermistors. RTD is more significant than the thermistor in terms of this dissertation context. Indeed, two sensing elements: Flow-rate and Temperature of the MPSC have been fabricated based on RTD concept. Therefore, RTD will be reported explicitly after exploring different temperature measurement techniques; and thermistor is presented briefly in the following section.

Thermistor

The word 'Thermistor' comes from two words Thermal and Resistor. The thermistor is a resistor which can be used as temperature sensing device. It measures the temperature by measuring a resistance change due to a temperature variation. Different materials are used to fabricate thermistors like metallic compounds including oxides or single crystal semiconductors like Silicon, Germanium [START_REF] Peeters | Temperature Sensors[END_REF][START_REF] Stephenson | Mechanical Variables Measurement -Thermal[END_REF] . Usually, a thermistor is made of non-metallic solid state materials. Two types of thermistors are available, depending on their temperature coefficient, which is an important parameter as it determines the working range and the sensitivity of the resulting sensors. The resistance value is related to temperature variation of a thermistor as follows:

Chapter 3

59

∆𝑅 𝑅 0 ⁄ = 𝛼∆𝑇 ( 3-3 )
Here, ∆𝑅 : resistance variation, 𝑅 0 : initial resistance value, 𝛼 : fist-order temperature coefficient of the resistance (TCR),

∆𝑇 : temperature variation.

The temperature coefficient of a thermistor can be either positive or negative. Negative temperature coefficient thermistors are more common. In fact, the sign of 𝛼 depends on the used material. In the case of a positive temperature coefficient, the resistance of a thermistor increases when the temperature increases. The opposite occurs in the case of a thermistor with negative temperature coefficient. The value of temperature coefficient varies up to several % per degree Celsius. Therefore, thermistors are very sensitive and are able to detect very small temperature variations. In addition, data reading can be obtained easily since thermistors have a high ambient resistance value in the range of kΩ ~ MΩ. As a consequence, small connection wires have an insignificant impact on the measured data. Thermistors are commonly used in temperature monitoring systems and as switches.

Noise thermometry

Noise thermometry is an elegant process of temperature measurement based on the basic characteristics of thermal fluctuation within an electrical resistance. In 1927, J. B. Johnson observed first thermal noise.

Later, after Nyquist's explanation of this phenomenon in 1928, a new technique became popular for temperature measurement [START_REF] White | The status of Johnson noise thermometry[END_REF] . Thermal noise is a randomly generated voltage due to conduction electrons Brownian motion [START_REF] Johnson | Thermal Agitation of Electricity in Conductors[END_REF][START_REF] Nyquist | Thermal Agitation of Electric Charge in Conductors[END_REF] . Albert Einstein was the first to predict the existence of the thermal fluctuation in electronic circuits, known today as Johnson noise. It is described by the Nyquist theorem as follows: The noise thermometry is compatible with a wide temperature range from a few mK to 1500 °C [START_REF] Childs | Review of temperature measurement[END_REF] .

𝑑𝑉 𝑇 2 ̅̅̅̅ = 4ℎ𝑓𝑅𝑒(𝑍) [ 1 2 + 1 𝑒 ( ℎ𝑓 𝑘𝑇 )-1 ] 𝑑𝑓 (
Practically, the most straightforward method for extracting temperature value from noise consists of concentrating in the frequency range where equation ( 3-4 ) takes its constant asymptotic value, called white noise, in which the mean square fluctuation of the voltage is directly proportional to temperature and to the resistance value:

𝑑𝑉 𝑇 2 ̅̅̅̅ = (4𝑅kT)𝑑𝑓 ( 3-5 )
Where 𝑅𝑒(𝑍) has been also replaced by R.

Resistance Temperature Device (RTD)

RTD is the Resistance Temperature Detector often referred as Resistance Temperature Device. RTD is a very common way to measure the temperature in different fields, such as industrial application, laboratory, food manufacturing [START_REF] Qian | Application of RTD Sensor in the Real Time Measurement and Wireless Transmission[END_REF] . Temperature is deduced from the resistance variation of the sensing element as a function of temperature.

While thermistors can exhibit both negative and positive TCR (Temperature Coefficient Resistance),

RTDs have only positive TCR and are generally made of metal [START_REF] Stephenson | Mechanical Variables Measurement -Thermal[END_REF] . In addition, thermistors are more sensitive while RTDs are more accurate [START_REF] Peeters | Temperature Sensors[END_REF] . The sensitive part of an RTD, generally a highly pure small diameter metallic wire, is often made of materials such as Platinum, Copper and Nickel.

In order to measure temperature, the RTD can be used by supplying a constant current. Since the resistance value of an RTD depends on temperature, then, when the medium temperature changes, the resistance value changes after thermal equilibrium is reached. Knowing the value of injected electric current, a measurement of the voltage value enables the calculation of the resistance value, hence temperature value. Generally, a very small biasing current is chosen to operate the RTD in order to avoid Joule self-heating, which would affect the reading by introducing a severe bias. As a consequence, temperature value provided by the thermometer may be somewhat higher than actual temperature. This discrepancy is known as self-heating error. Self-heating error may occur for different reasons, mainly: i.

excessive operating current and ii. low heat transfers between the thermometer and the operating medium.

If R the resistance value for a given temperature T, then the relation between these two quantities can be expressed as follows:

Chapter 3

61 𝑅 = 𝑅 0 (1 + 𝛼 1 . 𝑇 + 𝛼 2 . 𝑇 2 + ⋯ ⋯ ⋯ + 𝛼 𝑛 . 𝑇 𝑛 ) ( 3-6 )
Where, 𝑅 0 : resistance value at temperature 𝑇 0 𝛼 1 : temperature coefficient resistance (TCR); the TCR value is related to the material used for the sensing element of the RTD. For instance, the standard TCR value for platinum is 3.850 × 10 -3 °C-1 84 .

The number of terms of equation ( 3-6 ) depends on several parameters such as the temperature range, expected precision of measured data, material. But the equation can be reduced as below when the temperature span is short and normal accuracy, in the order of 0.1° is desired:

𝑅 = 𝑅 0 [1 + 𝛼(𝑇 -𝑇 0 )] ( 3-7 )
Different RTDs are available nowadays for different applications especially based on Pt, which are called Platinum Resistance Thermometer (PRT). The PRTs can be divided into three main categories-i.

Standard Platinum Resistance Thermometer (SPRT), ii. secondary Standard Platinum Resistance

Thermometer (secondary SPRT), and iii. Industrial Platinum Resistance Thermometer (IPRT). Among these PRTs, some of them are compatible to perform the measurement in immersion mode, which is useful to measure the temperature in water distribution pipelines, tanks and air conditioning systems. Particular

RTDs are designed for long-term complete immersion under an aqua environment like-sewers, rivers and cooling ponds. Application fields of different PRTs, their working temperature span and operating medium are reported in Table 3-2.

RTD-based Temperature Sensor

Our MPSC contains two kinds of sensors based on RTDs; they were designed and fabricated with distinct configurations, optimized to their different goals. Here we introduce the temperature sensor first. The resistivity of platinum in its bulk form is in the order of 𝜌 𝑒 = 1 × 10 -7 Ω. 𝑚 (more precisely, reported values range from 0.98 × 10 -7 Ω. 𝑚 to 1.11 × 10 -7 Ω. 𝑚 at room temperature). However, it is also well-established that such value of resistivity might vary in a significant manner when considering thin-film deposited materials, as their properties might differ from those of their counterparts in the bulk format. This statement holds true for the electrical resistivity of thin-film platinum layers. In our case those thin-films were obtained by DC sputtering and we found that the corresponding value of resistivity is 3.10 × 10 -7 Ω. 𝑚, which appear three times larger than the bulk value. It is worth mentioning that this value of resistivity was evaluated based on measurements done on a large number of resistors across the wafer.

Other geometrical parameters of the resistance are determined as follows:

The resistor's thickness is given by the value that was selected during fabrication for the thin-film platinum, which is 0.34 µm. As for length and width, they are defined according to our design as follows: Length = 12255 µm; Width = 11 µm.

Those dimensions have been carefully selected so as to target a nominal value of 1 kΩ for the resistor.

Eventually the nominal resistor was found to be 1.015 kΩ, this value being the result of statistics presented in section 3.5.

It worth-mentioning length of the resistor is rather big value (more than 12 mm) for a micro-scale device.

Therefore, the resistor had to be folded so as to minimize its total foot-print. The experiment was carried out using a very simple electronic circuit, which enables the control of the injected DC current through the 2 outer ports and recording the resulting voltage difference across the 2 inner ports. Constant current supply was ensured using an IC LM317. LabView software interface was used to read and record data from the electronic circuit. A small supply current of typically 3 mA was used for sensor operation, in order to avoid Joule self-heating. A photography of the experimental setup is shown in The sensor response was extracted at different water temperatures under constant current supply whose value is checked using a multimeter. A water boiler was used to warm the water and the hot water poured in a glass beaker, where a reference thermometer (a K-Type thermocouple probe connected to KIMO AMI 310 multi-purpose sensors) was also submerged for calibration purposes. The measurement was performed within the temperature range from 25 °C to 90 °C with a step of 5 °C. Among the four probes, the two inner probes were connected to the LabView data acquisition system NI USB 6216 along with SCB-68. Thirty

Chapter 3 65 measurements were taken at each temperature step in order to calculate the uncertainties for measured value.

The resistance variation as a function of temperature is measured at different water temperatures. The sensing element is a thin-film Pt resistor; which can be considered as a thin-film RTD. Due to the low supply current, no significant Joule self-heating is generated. Therefore, the probe is at thermal equilibrium with its surrounding medium and its resistance value corresponds to the temperature of the surrounding medium. When the sensor is introduced in water at a given temperature, the resistance value varies accordingly.

The temperature sensor response exhibits excellent linearity within the considered temperature range Chapter 3 66

Joule Self-Heating

Because electrical current fed into a resistor leads to Joule heating, it may have a more or less severe impact on the actual temperature of the resistor. From this point-of-view, it is important to make sure that a temperature sensor is not affected by this phenomenon, which is called Joule self-heating. On the contrary, Joule self-heating is desired in case of other kinds of sensors based on heat exchange (for instance the thermal flow-rate sensors as well as the Thermal conductivity detectors, as discussed later). An experimental illustration of Joule self-heating is given in Figure 3-5 below. Let's consider a resistor of rectangular cross section, of length L, width w and thickness t in which we inject an electrical current I. The problem is simplified by considering that heat transfer consists only in thermal conduction along the resistor main axis (say x-axis), which also allow us to simplify the situation into a 1D problem. Heat is therefore allowed to flow towards the resistors borders, where the temperature is considered equal constant and equal to the ambient temperature T0. Solving the problem starts considering the following equation:

𝜕 2 𝑇 𝜕𝑥 2 = 1 𝜆 𝑄 𝑔𝑒𝑛 ( 3-9 )
Where 𝑄 𝑔𝑒𝑛 = 𝐼 2 𝑅 𝑤𝑡𝐿 is the generated (electrical) power per unit volume (J.K -1 .kg -1 ), where R can be rewritten as 𝑅 = 𝜌 𝑒 𝐿 𝑤𝑡 , with 𝜌 𝑒 being the electrical resistivity of the resistor's material.

𝛼 = 𝜆 𝜌𝑐

⁄ is the thermal diffusivity coefficient (m 2 .s -1 ), with λ being the thermal conductivity (W.K - 1 .m -1 ), ρ the density (kg.m -3 ) and c the specific heat ( J.K -1

) of the resistor's material.

Solving this equation, considering the boundary conditions of constant temperature at the resistor's borders x=±L/2, and assuming a constant value for as a first order approximation, one obtain the following temperature variation:

𝛿𝑇(𝑥) = 𝛿𝑇 𝑚𝑎𝑥 [1 -( 𝑥 𝐿 2 ⁄ ) 2 ] ( 3-10 ) with 𝛿𝑇 𝑚𝑎𝑥 = 𝐿 2 𝜌 𝑒 8𝜆𝑤 2 𝑡 2 𝐼 2
This leads to an average temperature variation 〈𝛿𝑇〉 = 5 6

𝛿𝑇 𝑚𝑎𝑥 , which can be rewritten as :

〈𝛿𝑇〉 = 5𝐿 2 𝜌 𝑒 48𝜆𝑤 2 𝑡 2 𝐼 2 ( 3-11 )
It is worth-mentioning that when operating at constant voltage U, and considering that 𝑈 = 𝑅𝐼 = 𝜌 𝑒 𝐿 𝑤𝑡 𝐼 the latter equation simplifies as:

〈𝛿𝑇〉 = 5 48𝜆𝜌 𝑒 𝑈 2 ( 3-12 )
It appears from this equation (3-11) that the average temperature variation 〈𝛿𝑇〉 scales merely with 𝐼 2

and is also dependent on the resistor's dimensions and its material properties. More surprisingly, it also appears from equation (3-12) that when considering operation at fixed voltage, we have no more dependence on the resistor's dimensions. It appears indeed that only the quantity 𝑈 2 𝜌 𝑒 ⁄ is governing the temperature increase 〈𝛿𝑇〉.

A more standardized way is to express the temperature variation 〈𝛿𝑇〉 as a function of the electrical power 𝑃 𝑒 = 𝑅𝐼 2 . One then have from both (3-11) and (3-12), the following unified relation:

Chapter 3 68 〈𝛿𝑇〉 = ( 5 48𝜆 𝐿 𝑤𝑡
) 𝑃 𝑒 ( 3-13 ) which can be rewritten in the form:

〈𝛿𝑇〉 = 𝑅 𝑡ℎ 𝑃 𝑒 ( 3-14 )
where we introduce the thermal resistance as follows:

𝑅 𝑡ℎ = 5 48𝜆 𝐿 𝑤𝑡 ( 3-15 )
Now if we consider that the temperature dependence of electrical resistance R equation (3-7) where the quantity (𝑇 -𝑇 𝑂 ) is nothing else than 〈𝛿𝑇〉, then repeating the calculations starting from equation (3-9), we obtain the following refined model for 〈𝛿𝑇〉:

〈𝛿𝑇〉 = 𝑅 0 𝑅 𝑡ℎ 𝐼 2 1 -𝛼𝑅 0 𝑅 𝑡ℎ 𝐼 2 ( 3-16 ) 〈𝛿𝑇〉 = (𝑅 𝑡ℎ 𝑅 0 ⁄ ) 𝑈 2 1 + 𝛼(𝑅 𝑡ℎ 𝑅 0 ⁄ ) 𝑈 2 ( 3-17 )
depending whether we operate at constant current or at constant voltage. It is worth mentioning that we obtain a negative sign in the denominator only in equation (3-17) corresponding to constant current operation.

From the above analytical models, one can therefore plot in Figure 3 69 can conclude that the analytal models give only qualitative trends but they are not enough to give quantitative evaluation of the temperature variation due to self-heating. This is understandable as this discrepancy is due to the numerous assumptions made in our theoretical analytical model (1D modeling, ignoring heat conduction in the surrounding medium, which includes the fluid and the substrate. The effect of the substrate is clear in the experimental results shown in Figure 3-5 since it appears that Joule heating is reduced when using silicon as a substrate, compared to a glass substrate. Better accuracy in the simulations requires numerical modeling.

Evaluation of fabrication process uniformity in terms of resistance variations and TCR at wafer-level

Nominal values of the sensor's resistance as well as their spread at the wafer level have been evaluated.

This study was not limited to temperature sensors. It has been extended for the 3 types of purely resistive physical sensors, considered in the MEMS platform, namely, temperature sensors, flow-rate sensors and pressure sensors.

A summary of the measured data is presented in Table 3-3, Table 3-4 and Table 3- uniformity across the wafer with dispersion in the order of 1% up to 2%. Note that such statistics cannot be carried out for the conductivity sensors, as it is designed not to be conductive in air. It is also worth mentioning that the nature of the material used to fabricate the sensing resistors matters a lot. This holds true with respect to the TCR values, which is in the order of 2220 ppm/°C for platinumbased resistors (used for temperature and flow-rate sensors), while it is (only) 472 ppm/°C for polysiliconbased resistors (used for pressure sensors). The latter, though smaller than for platinum, is still significant enough to not be ignored, necessitating some temperature compensation for pressure sensors in the calibration phase. In this respect, it is also worth mentioning that not only the resistance value is temperature-dependent. Indeed, the piezo-resistive coefficient has also some temperature dependence, although limited, but not evaluated in the frame of this work.

Pressure Sensor

Resistance values @20°C Resistance values @100°C sensor. On the contrary, significant Joule self-heating is desirable for the flow-rate sensor. The initial selfheating temperature is an important parameter for a thermal flow-rate sensor. High self-heating temperature leads to a highly sensitive sensor and a wide turn-down ratio. Hence, if optimal self-heating is ensured with moderate current supply, an RTD can be used as a thermal flow-rate sensor.

Application of RTD as Thermal Conductivity Detector (TCD)

A thermal conductivity detector is a device which detects the variation of the thermal conductivity of its surrounding medium. Such device is commonly used for gas chromatography (GC). TCD becomes a key part of miniaturized GC -or micro GC (µGC). µTCD is actually a non-specific and non-destructive detector for GC, therefore it can detect several compounds including-air, carbon monoxide, hydrogen, sulfur oxide, nitrogen and so on 91 . In GC systems, TCD is used to sense the variation of the thermal conductivity of samples (gases) which enables determining their composition. The generic system configuration contains two channels: one is used as reference and other for measurement. The reference channel contains the pure carrier gas flow. On the other hand, the second one contains the sample to be analyzed in addition to the carrier gas. TCDs are placed in both channels. Both RTDs and thermistors can be used for this purpose.

The difference in gases composition of the two channels induces a thermal conductivity difference. This thermal conductivity difference leads to different equilibrium temperatures of the two TCDs, hence, to a voltage difference if RTDs of identical initial resistance are used and operated under the same constant current. The generated signal, voltage difference for instance, is proportional to the sample concentration in the measurement channel. 92 Commercially available TCDs have different configurations. Some of them are based on thin-film RTD while others are based on small bead thermistors 93 .

RTD Transient Behavior

In the above two examples of sensors involving an RTD, besides the needs for temperature measurements, there is a crucial role of the different heat transfer mechanisms, that is convection and conduction in both cases. The dynamics of those heat transfer mechanisms involve a time constant to account for the overall thermal behavior of the device. Such time constant will have important consequences in the sensor performance and its response time will be affected. It is therefore important to study the transient behavior of the TCD seen as a building block that can be used for a variety of sensors.

As a first guess the thermal time constant will strongly depend on the device architecture and its constitutive materials. It is likely that the device environment will be an important factor. Not only will matter the nature of the surrounding fluid, but also its velocity and initial temperature. All those parameters will be considered in what follows through simulations and experiments.

Simplified theoretical model of thermal time constant accounting for heat conduction

An analytical formula can be derived for the thermal time constant, through simplified theoretical modeling. To this end, we will make the same assumptions as in section 3.4.3, considering a 1D model for the resistor of rectangular cross-section, considering that heat transfer is done only by conduction through the resistor's main axis. In this case, we need to take into account the time dependence of temperature in the heat equation, as follows:

𝜕 2 𝑇 𝜕𝑥 2 - 1 𝛼 𝜕𝑇 𝜕𝑡 = 1 𝜆 𝑄 𝑔𝑒𝑛 ( 3-18 )
The thermal time constant can be obtained by solving the equation without right-hand part, also corresponding to a cooling process:

𝜕 2 𝑇 𝜕𝑥 2 - 1 𝛼 𝜕𝑇 𝜕𝑡 = 0 ( 3-19 )
We can also capitalize on part of the results obtained in the steady-state regime and detailed in section 3.4.3. In particular, we saw that the temperature distribution has a parabolic shape with a maximum value given by equations (3.10) and (3.11). Here, for better convenience when solving equation (3.19), instead of a parabolic shape, we will approximate the temperature profile by a cosine function. The general solution of (3.19) can then be written as:

𝛿𝑇(𝑥, 𝑡) = 𝛿𝑇 𝑚𝑎𝑥 cos 𝜋𝑥 𝐿 . 𝑒 -𝑡 𝜏 ( 3-20 )
Then, the time constant 𝜏 can then be obtained by injecting equation (3.20) into equation (3.19).

Recalling that 𝛼 = 𝜆 𝜌𝑐 ⁄ , one finally obtain :

𝜏 = 𝜌𝑐𝐿 2 𝜋 2 𝜆 ( 3-21 )
It appears that resistor's length L is the major factor affecting the time constant, besides thermal diffusivity, at least based on our assumptions made in this simplified model.

Similarly, to the thermal resistance introduced in equation (3-14), one can also introduce the thermal capacitance by considering that the time constant can be written as an RC product:

𝑅 𝑡ℎ 𝐶 𝑡ℎ = 𝜏 = 𝜌𝑐𝜋𝐿 2 𝜋 2 𝜆 ( 3-22 )
Which leads to:

Chapter 3

76

𝐶 𝑡ℎ = 48 5𝜋 2 𝜌𝑐𝐿𝑤𝑡 𝜆 ( 3-23 )
Finally, one can also build a second-order model, similarly to the method used in section 3.4.3, taking into account the temperature dependence of the resistance. After calculation, one eventually obtain:

𝜏′ = 𝑅 𝑡ℎ 𝐶 𝑡ℎ 1 -𝛼𝑅 0 𝑅 𝑡ℎ 𝐼 2 = 𝜏 1 -𝛼𝑅 0 𝑅 𝑡ℎ 𝐼 2 ( 3-24 ) 𝜏′ = 𝑅 𝑡ℎ 𝐶 𝑡ℎ 1 + 𝛼(𝑅 𝑡ℎ 𝑅 0 ⁄ ) 𝑈 2 = 𝜏 1 + 𝛼(𝑅 𝑡ℎ 𝑅 0 ⁄ ) 𝑈 2 ( 3-25 )
Depending whether one operates under constant current I or under constant voltage V. Here again it is also worth mentioning that the time constant can be either decreases or increased severely, when taking into account the negative sign in the denominator of equation (3-25).

Numerical simulations

Numerical simulations can take into account several factors that have been ignored in the previous simplified analytical model. First, instead of 1D modeling, one can have a better representation of the resistor shape as schematized in In order to illustrate the scenario depicted in Figure 3-11(c), which considers a forced convection heat transfer, we performed additional simulations, where we introduced fluid velocity of different magnitudes.

Chapter 3

79

The corresponding results of the simulated transient response are shown in Figure 3-13. It is interesting to note that it is not only the steady state temperature which is affected by fluid velocity -which was expected by due to the additional convective heat transfer, due to a large velocity. Indeed, it also appears that the time constant is also affected by the velocity. This important observation suggests new possible operation modes for flow-rate sensors operating in transient mode, rather than in steady state. Indeed, besides the novelty of such approach, such operation in transient mode may reduce the time required for measurement since there will be no need to wait for the steady-state. Hence, as time decreases, the total energy consumption of the sensor can be reduced accordingly.

Experimental measurement of RTD transient response

In what follows, we present experimental results of the transient response of resistor's temperature operated under different conditions. Recall that temperature is acquired directly through reading the resistance value. Experimental results are consistent with previously presented numerical trends. In addition to the effect of velocity and substrate material, we highlighted an additional dependence of transient response to the device structure and geometry.

Chapter 3 81

Conclusion

In this chapter, we introduce the needs for water temperature measurement and highlight the importance of this parameter as it affects many others. Aiming to develop a temperature sensor, we introduced the Daniel Bernoulli (1700-1782), a Swiss physicist was a pioneer to develop the scientific concepts for determining flow-rate. He succeeded to establish a first relation between pressure loss and velocity.

An important subsequent contribution was provided by British scientist Michael Faraday (1791-1867) in 1831 97 . His law on electromagnetic induction is involved in the working principle of the magnetic flowrate sensor, also referred to as flowmeter in what follows. He discovered that, if a copper disk is rotated inbetween the two poles of a permanent magnet then it generates electricity. Using the same principle, the velocity of a fluid can be determined. If a conductor is rotated by a liquid in a magnetic field through a pipe whose diameter is known, then the voltage produced by induction process can be used to access the corresponding information about fluid velocity.

Using the Doppler effect another flow measurement technique was developed, which is known as Doppler flowmeter. Christian Doppler (1803-1853) observed that, if a sound source is moving towards the sound receiver then the frequency appears higher. Hence, flow-rate can be calculated using the shift in frequency as a function of the average travelling velocity. Many other kinds of flowmeters were developed since that time.

An important parameter has to be considered while measuring the flow-rate or the velocity of a fluid; it is the velocity regime of the corresponding fluid. There are mainly two fluid flow regimes that can be distinguished: Laminar flow and Turbulent flow. The British mechanical engineer Osborne Reynolds proposed a dimensionless parameter called after him, the Reynolds number denoted by Re and defined by

𝑅 𝑒 = 𝐿𝑉 𝑣 𝑒
, where L is the characteristic length of the medium, V is the fluid velocity and 𝑣 𝑒 is the kinematic Chapter 4

84 viscosity of the fluid (m 2 /s). It can be also written as 𝑅 𝑒 = 𝜌𝐿𝑉 𝜇 , where μ is the dynamic velocity and 𝜌 the density of the fluid medium. In the latter formulation one can notice that the Reynolds number reflects which effect dominate among inertial (proportional to 𝜌) and frictional (proportional to 𝜇) depending on fluid velocity V and characteristic length L. The value of Re actually provides information about the flow regime whether it is laminar or turbulent. It is usually admitted that when Re < 2300, the flow is considered as laminar and when Re > 4000, the flow is turbulent. When the value of Re ranges between 2300 and 4000 then the flow is in the so-called transient regime.

Nowadays, advanced technologies have enabled sophisticated flow-rate measurement devices based on the above-mentioned principles and others, which can provide accurate measurement within a wide flowrate range. In particular, the micro and nanotechnology bring a significant advancement in the case of miniaturized flow-rate measurement devices. There are numerous advantages of such miniaturized devices in terms of production cost, easy installation and maintenance, large-scale implementation 98 as well as their suitability for measurement in confined environments including microscale channels. Due to the smaller size of this sort of micro-machined flow-rate devices, they are being used in the field of biology 99,100 , and medical applications [START_REF] Hsiai | Micro Sensors: Linking Real-Time Oscillatory Shear Stress with Vascular Inflammatory Responses[END_REF]101,102 as well as applications of microfluidics at large. Besides, accurate microscale flow-rate measurement is important in some other branches of science and engineering like-gas chromatography [START_REF] Kaanta | High Sensitivity Micro-Thermal Conductivity Detector for Gas Chromatography[END_REF]103,104 , environmental monitoring 105,106 , weather forecasting 107 , aircraft monitoring [START_REF] Nguyen | Micromachined flow sensors-a review[END_REF] and industrial process control 108 .

Commercial flow-rate sensors

As mentioned in the previous chapter, the aim of this work is to develop a multi-parameter sensing module for an application to water networks monitoring. In the proposed module, a micro-machined thermal flow-rate sensor is used to measure the water flow-rate. Several commercial flow-rate sensors are already available in the market in order to respond similar need, but they are mostly based on single devices and their operating principle is not always applicable in micro-scale, neither they can be found in a cointegrated format with other kinds of sensors. All these flow-rate devices are realized based on different operation principles and applicable in different fluid media-gas, air, water and oil. For instance, OMEGA ® is a specialized company in flow-rate meter manufacturing and they produced a wide variety of flowrate meters for various kinds of fluid media. the concept how a fish senses the surrounding flow field 111 . The goal here was to mimic the fish flow sensing system. A fish uses its neuro-mast which is composed of hair cilium and nerve cells to sense the water displacement. But this neuro-mast does not have the direct contact with the water. It is a part of the lateral line under the epidermis and this lateral line has canal on it to sense the water displacement. A sensor array can be used to perform a similar action where each sensor contains in-plane fixed cantilever and a fabricated cilium will be attached vertically at the free end of the cantilever (Figure 4567). Therefore, the external flow has an effect on the cilium and due to the rigid connection between the cantilever and the cilium a mechanical bending is sensed by the cantilever beam. The bending magnitude can be captured by the piezoresistive sensor. In order to interpret accurate flow field and access the information about the flow field structure evolution, an array of individual sensor nodes can be implemented in the operation media with systematically varying the position and orientation of cilium with different heights. In spite of the availability of different sensors in macro or micro scales for measuring the water flowrate, still there is a need of a complete monitoring system which will not only measure the water flow but also provide information about other parameters of the water environment. Hence, the novelty of our flowrate sensor is that it is not a single sensing device rather it is a part of a multi-parameter sensing system (MPSC) which is a smart and complete multipurpose module to monitor the water distribution network.

Moreover, such micro-machined flow-rate sensor is not well-acquainted with the conventional water distribution system. The considered flow-rate sensor can cover more than the usual flow-rate range of the distribution system. The normal water velocity of a water distribution network ranges between 0.1 m/s and 10 m/s. From the tabulated information, it can be seen that the majority of micro-machined flow-rate devices are compatible with micro and nano fluidic applications and their turn-down ratio is limited to µL or nL.

A comparative study is reported below in

None of them can be employed in the targeted medium (water distribution network) of flow-rate sensor of the MPSC. Among mentioned devices, reference device 11 can be implemented in a domestic environment in a gaseous media. Hence, two versions of the considered flow-rate sensor are highlighted here based on the glass and silicon substrate. These two flow-rate sensors cover a wide velocity range in comparison with existing devices under moderate power consumption. It can be noticed from the table that the proposed silicon substrate pillar device consumes more power than the glass based device. The reason behind this higher power consumption of the silicon substrate device is explained explicitly in the result and discussion section.

Thermal Flow-rate sensors

At the beginning of this chapter, we discussed the different operation principles of a flow-rate sensor.

In order to access the flow-rate information of a fluid, a flowmeter can be developed a priori based on any working principle. It can be observed that most of the operating mechanisms are based on either mechanical or thermal transduction method. For micro-machined flow-rate sensors, the thermal-based working procedure is the most widely used 110,111,124 . The flow-rate sensor of the MPSC is a thermo-resistive flowrate sensor and its working principle relies on heat energy transfer to the surrounding medium. A platinum thermistor is used as a heating element of the flow-rate sensor, which is heated by injecting an optimum amount of current. When the sensor is subjected to the fluid flow then the heat will be taken away by the fluid flow leading to cooling of the heater. The magnitude of this cooling is proportional to the fluid velocity. Thermal flow-rate sensors can be categorized into three categories based on their operation principle-(i). Anemometric (Hot-wire), (ii). Calorimetric and (iii). Time-of-Flight (TOF) [START_REF] Kuo | Micromachined Thermal Flow Sensors-A Review[END_REF] . A detailed description of these three working principles is presented in the following section.

Chapter 4

(i) Anemometric operation principle

An anemometric flow-rate sensor or Hot-wire measures the effect of flowing fluid on a hot body.

Normally, in an anemometric flow-rate sensor, a metallic resistor is used as a heater [START_REF] Mailly | Anemometer with hot platinum thin film[END_REF]125 . When the sensor is submerged in the flowing fluid, heat is transported from the heater, which causes a variation of its temperatures. Heat is transferred from the heater to the substrate by thermal conduction. However, between the heater and the fluid, heat transfer is governed mainly by convection which strongly depends on the fluid velocity. Consequently, the fluid velocity can be deduced from the heater temperature variation. For a given electrical power supply, the heater resistor reaches its maximum temperature under zero velocity.

Afterwards, temperature gradually decreases as the fluid velocity increases. Therefore, the velocity information can be simply extracted by reading the voltage or the resistance change of the heating resistor since the resistor value depends on temperature.

An analytical theoretical model can be established in a quite straightforward manner if we neglect the conduction losses to the substrate and consider only the heat transfer by convection, a situation which reflects better the case of a suspended hot wire. In such situation, the thermal equilibrium leads to:

𝐼 2 𝑅 𝑤 = ℎ. 𝐴 𝑤 (𝑇 𝑤 -𝑇 𝑓 ) ( 4-1 )
Where 𝐼 is the electrical current, 𝑅 𝑤 is the heater resistance, 𝑇 𝑤 its temperature, 𝐴 𝑤 its projected surface area. 𝑇 𝑓 is the fluid temperature and ℎ is the heat transfer coefficient of the wire. The so-called King's law 126 gives ℎ as a function of fluid velocity 𝑣 as follows:

ℎ = 𝑎 + 𝑏𝑣 𝑛 ( 4-2 )
where a, b and n are constants, with n being in the order of 0.5 while a and b are determined by calibration.

Taking into account that 𝑅 𝑤 depends on temperature as follows:

𝑅 𝑤 = 𝑅 𝑟𝑒𝑓 [1 + 𝛼(𝑇 𝑤 -𝑇 𝑓 )] ( 4-3 )
Then Then injecting equation (4.2) and (4.3) into (4.1), one finally obtains:

𝑎 + 𝑏𝑣 𝑛 = 𝐼 2 𝑅 𝑟𝑒𝑓 [1 + 𝛼(𝑇 𝑤 -𝑇 𝑓 )] 𝐴 𝑤 (𝑇 𝑤 -𝑇 𝑓 ) ( 4-4 )
The latter equation can be rewritten as follows so that to derive the velocity: When considering the contribution of heat conduction to the substrate as well as the real geometry of the heater and its surrounding environment, one need to proceed with numerical simulations as described in the following sections. However, based on equation (4.5) one can better understand some general statements as exposed here below.

The hot-wire flow-rate sensor can be operated under three operation schemes i.e., constant current, constant temperature difference and constant power mode 106,127 .

In constant current mode operation, a constant current is supplied to the heater resistor of a thermal flowrate sensor. Under no flow, a first equilibrium temperature is reached. When the sensor is submerged in a moving fluid, the temperature of the resistor decreases which will lead to a resistance variation. The temperature decrease magnitude increases with the fluid velocity. Thus, the fluid velocity can be deduced based on the heater resistance variation as a function of the fluid flow. On the other hand, constant temperature difference operation scheme involves in operating a thermal flow-rate sensor by maintaining a constant temperature difference between heater and fluid. Fluid flow causes a diminution of the heater temperature. As a result, temperature compensation is needed for the heater resistor in order to maintain the constant difference. The amount of power consumption that is required to reach the initial tantamount temperature depends on the fluid flow speed. The velocity of the corresponding fluid can be determined using the feedback signal required to maintain such constant temperature. The third operation method of a thermal flow-rate sensor is the constant power mode. In this method, the fluid velocity is measured based Chapter 4

94 on the temperature variation of the sensing element while maintaining a constant power supply for heating.

For the same power supply, the larger the fluid velocity, the lower the temperature.

(ii) Calorimetric operation principle

The calorimetric flow-rate sensor measures the temperature profile around the heater. In general, the turndown ratio of a calorimetric sensor is small. This kind of sensor is more suitable to measure a very low velocity [START_REF] Nguyen | Micromachined flow sensors-a review[END_REF] . A calorimetric sensor can provide information regarding velocity until the temperature difference reaches zero between the heater and the sensing resistor. At very high velocity, the upstream or downstream sensor cannot sense heat dissipated from the heater resistor. In this case, the sensitivity range of the corresponding sensor can be extended by varying the distance between the heater and the sensing resistor. The relationship between the maximum velocity that can be sensed by a calorimetric sensor and the distance between the heater and a sensing element can be given by 106 :

𝑉 𝑚𝑎𝑥 = 2𝐷 𝑡 ∆𝑆 ( 4-6 )
Here,

95

𝑉 𝑚𝑎𝑥 : maximum velocity (m/s) 𝐷 𝑡 : thermal diffusivity of the fluid (m 2 /s) ∆𝑆: heating and sensing resistor distance (m) Therefore, the distance between the heater and the sensing resistor is a governing parameter for a calorimetric flow-rate sensor. The sensitivity can be enhanced to low velocities range by decreasing this distance; and for large velocities by increasing the heater-sensor separation [START_REF] Kuo | Micromachined Thermal Flow Sensors-A Review[END_REF]106,128 . The heater is isolated thermally from the substrate in order to avoid thermal leakage. This is recommended in general for all thermal flow-rate sensors. To determine the travel time of the short thermal pulse from the heater to the detector, several parameters have to be considered: thermal diffusivity and conductivity of the fluid, the ratio of the heater-detector distance and the average flow velocity. Generally, velocity is calculated by dividing the heater and sensor distance by the travel duration of the thermal pulse The distance 𝛥𝑆 has to be chosen carefully so as to avoid excessive thermal diffusion during the flight time ∆𝑡. Indeed, as depicted schematically in Fig. 4.13, an ideal square-shaped thermal pulse will gradually evolve into a Gaussian-like pulse during the flight path and it will eventually vanish due to lateral heat diffusion. This phenomenon is modeled as follows. If we consider the heater as a line source the thermal distribution can be expressed as a function of distance and time as [START_REF] Kuo | Micromachined Thermal Flow Sensors-A Review[END_REF] -

(
𝑇(𝑥, 𝑡) = 𝑞 0 4𝜋𝑘𝑡 𝑒 [-(𝑥-𝑣𝑡) 2 4𝑎𝑡 ] ( 4-8 )
Here, The flow-rate meter based on TOF operation principle is mainly used to measure liquid flow-rate. The TOF flow-rate sensors are not widely used in gaseous media due to the large thermal diffusivity of gases [START_REF] Kuo | Micromachined Thermal Flow Sensors-A Review[END_REF]106,119 .

𝑇

Materials and Methods

A study based on numerical simulations and subsequent comparison with experimental results, is carried out to investigate the performances of the different flow-rate sensors under consideration, depending to different parameters, especially the device geometry, the substrate material and power consumption. This study is also intended to anticipate for unexpected behaviors as will be discussed in the next section of results and discussion. width is an important parameter as will be shown letter in the next section of results and discussion. It is worth mentioning that for ensuring long lifetime, we have to keep the flowrate sensor mechanically robust enough. Hence we did not consider fully-suspended thin-film resistors as an option for designing our flowrate sensor. The main reason is that, contrary to microfluidic devices, water networks not only involve quite large velocities, in the order of m/s, but they also might carry solid particulate matter of various sizes, which can impact the resistor and destroy it if it is too fragile.

Due to lack of time, and in order to facilitate comparison between all versions, all simulation and experimental results presented in the present work are based on anemometric operation mode solely.

Numerical simulation method

The different device configurations presented in Figure 4-11 are considered first for numerical simulations. Computational Fluid Dynamics (CFD) simulations are carried out based on Finite Element Method (FEM) and using COMSOL Multiphysics 5.2a. Material properties are imported from COMSOL material library and conjugate heat transfer physics is used to account for heat transfer both in the solid and fluid media. As shown in Figure 4-11, the substrate thickness is 500 µm for all cases. When glass is used as substrate material, no insulation layer is introduced between the heater and the substrate, since glass has a low thermal conductivity compared to other device materials. When the substrate is not made of silicon, a 450-nm thick SiO2 insulation layer is added under the heater resistor in order to reduce thermal conduction leakage. For membrane structure, the platinum resistor is fabricated on the top of a silicon membrane. The cavity depth under the silicon membrane is 430 µm and the empty space inside the cavity is considered as vacuum. The membrane is considered as a possible solution to reduce thermal leakage. The device first version, based on a glass substrate, shows a better thermal efficiency due to glass low thermal conductivity.

As already discussed in Chapter 2, because silicon is necessary for co-integration of the different sensors on the same chip, similar thermal performance is then targeted, even when using high thermal conductivity materials like silicon. Indeed, silicon's thermal conductivity is approximately 100 times larger than that of glass 129,130 . Other configurations, such as a heating resistor on top of a thin silicon pillar, are also considered for the same purpose, in this case through a reduction of the resistor-substrate contact area. The pillar is located in the middle of a cavity. A parametric study is conducted to optimize the pillar, resistor and cavity aspect ratio. The cavity depth and the width are varied from 50 µm to 300 µm and from 100 µm to 1000 µm, respectively. The length and height of the silicon pillar is equal to the cavity length and height. The resistor and pillar have the same length and width since the resistor is deposited on top of the pillar. The length is fixed at 100 µm and the width is varied from 5 µm to 10 µm. In order to experience the maximum velocity, the sensor is located at the pipe symmetry axis because of the parabolic velocity profile. A large fluidic domain, compared to the sensor size, is considered in the simulation so as to avoid edge effects.

Experimental Setup for calibration of the Flow-Rate Micro-sensors

Regarding the experimental part of our study, we already presented in Chapter 2, the fabrication details of the different versions of the MPSC, which includes the flow-rate sensors. In the present section, we will focus on the description of the experimental setup that we developed and used for the purpose of measuring the characteristics of the fabricated flow-rate sensors.

(i) Fluidic circuit

The setup is built using a usual rigid plastic pipe in which the flow-rate sensor is inserted. The inner diameter of the pipe is 22 mm. The flow input and output is connected to the same water reservoir, which can be considered as a closed loop. Several pieces of a similar pipe are used to build the closed water loop.

For avoiding turbulent flow at the sensor vicinity, the length of the pipe piece in which the sensor is inserted, is chosen as ten times of its diameter in order to avoid the edge effects and ensure a fully developed stablished flow regime around the sensor. To connect different parts of the pipe, several 90°-angle elbowshaped pipes are used and the sensor is placed at the middle position between the two edges of the corresponding pipe. A variable speed water pump is submerged in the water tank and connected to the loop The water reservoir capacity is 20 litres. The same water level, reservoir and loop positions are respected for both pump characterization and sensor response measurement in order to avoid any variation of head loss during the different steps of experimental characterization.

(ii) Electrical circuit

A constant current source is built using an adjustable three-terminal positive-voltage regulator IC device (LM317). The current supply of the constant source is tuned at different intensities by varying the value of a variable resistor, which is placed in series with the reference and load (sensor) resistor. The reference resistor is used to determine the amount of injected current through voltage measurement. For a fixed value Chapter 4

102 of the variable resistor, the circuit will provide a constant current continuously while the resistance of the sensor's resistor changes according to its temperature. For a given fluid velocity, the resistance temperature drop with respect to zero velocity situation is calculated using same equation (4-9). It is related to the fluid velocity by a calibration process.

Consequently, a measurement of the resistance value, hence temperature, enables to access the fluid velocity. Note that another initial calibration of the sensor is also required so as to determine the values of both R(T0) and T0.

Numerical Results and Discussion

In this section, numerical and experimental results will be presented. The numerical parametric study enables the device design optimization before fabrication. Then the optimally designed device is fabricated and experimentally characterized. First, numerical simulation results of each design of the flow-rate sensor will be presented. Then, the corresponding experimental measurement results will be presented for each design. Finally, a comparative discussion will be performed.

Glass substrate based flow-rate sensor

The first version of the flow-rate sensors is based on a glass substrate. The whole simulation domain, including the fluidic domain consists of three different materials-water, glass and platinum. Boundary conditions have to be defined for both the solid and fluid domains. For heat transfer, the left side of the fluid domain is set at a constant temperature, ambient temperature for instance (293.15K). A continuity condition is set at the other boundaries ("outflow" in COMSOL terminology). The platinum resistor is defined as the heat source with a volumetric power density. The heat source volumetric power density q is calculated using the equation:

𝑞 = 𝑃 𝑉 [ 𝑊 𝑚 3 ] ( 4-10 )
Where, 𝑃 : dissipated electric power (W)

𝑉 : the heating resistor volume (m 3 ) (estimated as 106 × 10 × 0.34 µm 3 )

Chapter 4

105 Default values of P and V are 10 mW and 360,4 µm 3 , respectively.

For mass transfer physics, a laminar flow is considered. The left edge of the fluid domain is defined as the flow inlet with a constant velocity profile and the right edge as flow outlet. The top and bottom sides are defined as symmetry axes. Exterior edges of the sensor chip are defined as walls with no-slip conditions.

Simulations are done at 10 mW power supply with a velocity ranging from 10 -6 m/s to 10 m/s. The steady state temperature of the heater resistor is calculated as a function of the fluid velocity. In the following, results will be presented using a normalized temperature 𝑇 ̅ . The normalized temperature is defined as the ratio between the temperature difference of the heater and the ambient temperature under flowing and nonflowing condition. The heater and ambient temperature difference under no-flow condition, i.e for zero fluid velocity, is called ΔTmax and the same temperature difference for a given velocity is defined as ΔT.

Consequently, 𝑇 ̅ = Δ𝑇/Δ𝑇 𝑚𝑎𝑥 ( 4-11 ) According to this definition, 𝑇 ̅ ranges between 0 and 1. Normalized temperature is used in order to have comparable values for all considered sensors designs independently of the absolute temperature reached by each device. Indeed, the maximum temperature of each design under a given power supply due to Joule self-heating can vary in a wide range. almost linear trend (with the logarithmic scale on the velocity axis). From 10 -5 m/s to until 10 -3 m/s the nondimensional temperature value decreased largely. In this whole range, a monotonous temperature decrease is observed. For certain velocity values, around 0.7 m /s, the sensor exhibits inconsistent thermal behavior with respect to velocity, which can be seen anomalous a priori. Indeed, instead of a temperature decrease due to a fluid velocity increase, the heater temperature increases. This discrepancy is marked with a blue circle on Figure 4-16. A detailed calculation of the fluid velocity field around the sensor chip provides an explanation of this apparent inconsistency. Indeed, due to the sensor chip geometry, at this velocity range, a reverse flow is generated at the resistor vicinity. A detailed explanation regarding this reverse flow and its effects on the sensors thermal response is presented at the end of this section.

Silicon substrate based flow-rate sensor

The physical structure of the flow-rate sensor based on bulk silicon is very similar to that based on glass, except for a thin insulation layer. Indeed, a 450-nm thick SiO2 layer is inserted between the platinum resistor and the silicon substrate in order to reduce the conduction heat leakage. The velocity profile and temperature distribution around the sensor depend on the flow velocity. For a non-flowing, stationary fluid, i.e. when v = 0 m/s, the temperature profile around the heater is symmetric.

The calculated temperature profile for bulk silicon flow-rate sensor is presented in Figure 4-18 under zero velocity. We observe no distortion of temperature distribution.

Silicon membrane based flow-rate sensor

In this version, a silicon membrane is used as the substrate for the platinum heater. Then the membrane is deposited on a glass substrate. The thickness of the membrane is 70 µm. Contrary to bulk silicon device, no SiO2 insulation layer is inserted between the platinum resistor and silicon substrate. The cavity below the membrane is considered as vacuum to enhance the thermal insulation between the heater and the substrate, which reflect reality actually, since this cavity is obtained from silicon-to-glass wafer bonding under vacuum environment. The physical structure of the membrane-based flow-rate sensor is shown in Chapter As mentioned previously, a parametric study is conducted to enhance further this device performance.

First, the heating resistor width is varied while all other properties are unchanged. The cavity depth and width are fixed at 300 µm and 150 µm, respectively. Resistor widths from 1 to 10 µm are considered for all configurations (pillar, glass, silicon and silicon membrane devices). For all devices, a sensitivity increase is observed when the resistor width is decreased. In the following, for the sake of simplicity, the parametric study is reported for the most promising configuration only, namely for the pillar device. This parametric study is carried out for a 10 mW power supply and a velocity range from 10 -6 m/s to 10 m/s. We show in Temperature decreases as a function of velocity for all wdith values. In addition, the temperature drop for a given velocity is smaller for smaller sensor width. This means the small width sensors will be saturated only at larger velocities than 10 m/s, thus sensitive to the largest veolcity ranges. Consequently, from a thermal point of view, the smaller the sensor width the better. However, the considered devices are subject to other constraints, such as mechanical and fabrication constraints as well as robustness requirements.

Although 1 µm and 2 µm-wide resistors exhibit better thermal responses, their small size is less convenient for the considered fabrication process and this decreases their robustness for instance in case of waste water operation medium which can contain solid particles. Consequently, the 5-µm-wide resistor is preferred and will be considered in the following.

At this stage, the cavity width is varied between 150 and 1000 µm, while its depth and the heating resistor width are fixed at 300 µm and 5 µm, respectively. We plot in Figure 4 We observe that, for a given velocity, a deeper cavity leads to a smaller temperature drop thus to a larger turndown ratio. For the fabricated prototypes, a 100-µm-deep cavity is considered.

Chapter 4 117

Reverse Flow

We have already noted on Figure 4 In order to have a consistent sensor response, the reverse flow has to be suppressed or, at least, reduced.

This can be done through a geometric optimization of the sensor node. The height and the edge curvature of the sensor node are modified accordingly. Different CFD simulations are performed to find a satisfactory geometry. A representation of the schematic simulated sensor node and a real sensor node are shown in Therefore, the reverse flow effect can be fully eliminated, at least within the explored velocity range, by respecting the proposed geometry and tilted inserting position, of the sensor node in the water distribution pipeline.

Chapter 4

122 For deeper analysis, the numerical study was conducted for different values of the inlet velocity-0.0001, 0.001, 0.01, 0.1, 1 and 10 m/s and the comparison has been made for 0.1 m/s only; since this velocity is relevant to the practical application. It has been observed from the simulation that at inlet velocites lower than 0.1 m/s, the primary sensor node experiences less negative flow and at 0.0001 m/s, there is no observed reverse flow. As the inlet velocity exceeds 0.1 m/s, reverse flow within the considered region increases significantly. However, at maximum velocity 10 m/s, the negative flow has been observed for both sensor nodes. Since this is relatively high velocity in contrast with the real employment medium, therefore the potential issue at 10 m/s can be ignored in practice. In addition, the inclined position of the node reduces reverse flow notably. Hence, higher incidental angle magnitude will lead to a fallacious response from the flow-rate sensor. In order to obtain a reasonable measurement, the velocity should be parallel to the resistor width. The high incident angle of the sensor node with respect to the normal would result in a situation where the velocity cannot be considered anymore as tangential to the sensing element of the sensor.

Therefore, the angle should not exceed a certain margin (15º or 20º as a maximum).

Numerical results comparison

At this stage, we undertake a comparison of the different versions of the flow-rate sensor. We plot on 123 temperature for a given velocity than the bulk silicon device within the velocity range from 0.001 m/s to 10 m/s, this difference is negligible compared to the pillar and glass devices. Among the three silicon-based devices (bulk, membrane and pillar), the pillar structure flow-rate sensor is the only configuration that competes with the glass-based sensor. It even surpasses the glass device response in the velocity range from 0.5 m/s to 10 m/s.

Experimental Results

For a thermal flow-rate sensor, the resistor's Joule self-heating is an important parameter. The maximum temperature of the heater resistor under no-flow condition determines the turn-down ratio and the sensitivity of the sensor with respect to velocity. Moreover, the relation between the initial temperature and the power supply indicates the device power efficiency. A large temperature increase at low power consumption leads to a low power consuming thermal flow-rate sensor. Therefore, to determine the considered sensors' energy efficiency, both Joule self-heating and power consumption tests are realized under zero velocity. Results are presented in the following section.

Joule self-heating and Power consumption

Information on Joule self-heating at the resistor level under no-flow condition helps estimating the sensor's response to a flow velocity and the minimal power supply needed. The sensor is submerged under non-flowing water and supply current is varied from 10 mA to 30 mA with a step of 5 mA. Results of Joule self-heating tests are presented in Figure 4-35(a). The glass sensor exhibits the largest temperature increase, that is, 20.12 °C at 30 mA current supply. Besides, among the silicon-based devices, only the pillar structure device exhibits a significant temperature increase, 2.57 °C at 30 mA supply current. On the other hand, bulk silicon and silicon membrane devices show small temperature increases. These two sensors temperature increase by less than 0.8 °C for 30 mA current supply. Although the three sensors are fabricated on the same substrate material, pillar configuration flow-rate sensor exhibits a significantly larger temperature increase than the silicon and membrane due to an optimized geometry.

Power consumption of the three devices is also calculated for the same current supply range. It is illustrated in Figure 4-35(b). The pillar device consumption is relatively high, up to 43.78 mW for 30 mA current supply. On the other hand, glass, silicon and silicon membrane sensors are three times less power consuming than the pillar configuration at the same current supply. Amon these four sensors, glass devices are lowest power consuming within the whole supply current range. This is due to the low initial resistance value of glass device and the high initial resistance value of the pillar device, which are 12.92 Ω and 43.6 Ω, respectively. According to Ohm's law, dissipated power is proportional to the resistance for a given Chapter 4

124 electrical current (30 mA is considered here as a maximum value). Therefore, the larger the resistance value the larger the power consumption. However, the pillar device heater resistor's width is reduced only by two times (not three) compared to bulk silicon and membrane resistors, which causes a larger resistance value at room temperature and hence the increased power consumption for a given current. Consequently, pillar device is expected to have power consumption two times larger than the other device, since the resistor width has been reduced by factor of Chapter 4 125 two, not three. This is not what is observed in Figure 4-35(b). Indeed, at the same current supply, a larger resistance value leads to larger Joule heating, hence to a larger equilibrium temperature. Then, additional heating occurs. Joule heating is amplified by two phenomena: i) the temperature increase due to Joule heating induces an increase of the resistance value, hence, to an additional second-order Joule heating and temperature increase; ii) The pillar device resistor contact with the substrate due to the pillar structure reduces heat leakage through the substrate and increases the device equilibrium temperature. This double amplification of Joule self-heating for the pillar device induces a larger increase of the resistance value which strongly depends on temperature, hence leads to larger power consumption. This amplification can be deduced from the variation of the sensors' resistance values reported in Table 4-4. For a current supply between 0 and 30 mA, we observe a relative variation of the pillar device resistance of 11.5 % versus a variation of 1 % for the two other silicon devices. The relative resistance value variation, hence the Joule self-heating, is larger by one order of magnitude for the pillar device.

Sensor's response to the velocity

As previously mentioned, velocity can be estimated from the cooling magnitude of an anemometric flow-rate sensor. Therefore, we perform an experiment where we record the sensor's response within the velocity range from 0 m/s to 0.9 m/s (Figure 4

Comparison between numerical and experimental results

Both numerical (Figure 4

Conclusion

Several versions of a micro-machined thermal flow-rate sensor, as a part of a multi-parameter sensing chip (MPSC), have been presented and studied in this chapter. It is demonstrated that the thermal flow-rate sensor's performance largely depends on the thermal conductance of the substrate material, which has a great impact on the device sensitivity and power consumption. Low thermal conductance can be achieved even when using a high thermal conductivity material such as silicon, by a geometric design and optimization when the choice of the substrate material is constrained. In addition, the resistor dimensions are also a governing parameter of the sensor's response. We demonstrated the successful fabrication and optimization of a thermal flow-rate sensor on a silicon substrate, where silicon is absolutely required as the substrate material for the co-integration of other MEMS sensors. This was successfully achieved through micro-structuration of the silicon substrate using a pillar-like structure and reducing the substrate overall thermal conductance. Such micro-structuration leads to a silicon-based micro-machined thermal flow-rate sensor with performances comparable to state-of-the-art devices based on low conductivity materials such as glass.

Chapter 5 129

Conclusion and Prospects

General conclusion

In this thesis, the process of design, fabrication, optimization and characterization of a miniature multiparameter sensing module for monitoring water networks is reported. Different sensors which cover nine physical and chemical parameters relevant for water networks monitoring are integrated on a single chip.

Successful co-integration enables the measurement of the desired parameters simultaneously. The final sensing module is fully autonomous and self-powered using an energy harvester for power supply. Data transmission electronics are also part of the module. The proposed device is not only designed for water quality monitoring but also for water flow rate measurement at different nodes of the network. This last parameter is of paramount importance for leakage detection and prevention and can also be used for resource demand monitoring.

Several versions of the device have been designed, fabricated and characterized before reaching the final operational prototype of the MPSC including all nine physical and chemical sensors. The different sensors of the MPSC have been separately tested in the lab and in the field. Obtained results validate target performances.

The electric conductivity sensor at ambient temperature was tested for different reference conductivity solutions. Since the sensor operates at AC current supply, appropriate supply intensity and frequency are required for an accurate response. The pressure sensor, made of piezoresistors under a polysilicon membrane, also shows a relevant response in the target measurement range, up to 6 bar. The output signal, hence the sensitivity of the device, can be tuned through the gain resistor variation which enables the device tuning for different measurement ranges. Among all physical sensors, the flow-rate sensor has been subject

to the largest number of modifications before stabilization of the final prototype. Several versions have been developed: the first prototype targeted a separate operational flowrate sensor. It was made on a glass substrate. The second one addressed the constraint of co-integration with the pressure sensor which required the use of a silicon substrate. Such material increased power consumption and reduced the device sensitivity and turndown ratio. Consequently, several geometric and thermal optimization solutions have been implemented to ensure low power consumption, good sensitivity and turndown ratio while using silicon.

Perspectives of future developments of the MPSC

In the proposed MPSC, nine sensors have been co-integrated on a small foot-print. During test and validation phase, MEMS based physical sensors were run individually. This is a possible scenario in real operation conditions. However, simultaneous operation can also be targeted to increase sampling rate. In this case, cross-interactions between the different sensors cannot be excluded, especially when the sensor operation alters the observed medium properties. This is, for example, the case for the flowrate sensor which locally increases water temperature and can affect other parameters measurements.

For instance, it was shown in Chapter 3 how temperature affects water conductivity. As a consequence, the conductivity measurement should be executed under stable temperature.

More generally, cross-interactions between the different sensors have not been investigated. Proper scenarios to investigate such possible interactions should be defined and systematically explored to ensure proper interpretation of measured signals in real operation conditions. The flowrate sensors generated heat can also affect temperature measurement. To avoid additional perturbations by the temperature sensor which is made of a platinum resistor, particular attention should be paid to this sensor power supply to avoid any additional Joule self-heating.

According to reported characterization tests, the stability of supply current is the main constraint that should be respected to guarantee a consistent response of the device. Since the device relies on an energy harvester for power supply, critical cases where harvested energy is not sufficient should be taken into Finally, the presented multi-parameter sensing device has been initially developed as part of a sensors network to monitor water grids. However, the device can be used efficiently for a similar application in a more innovative way. Instead of fixing several MPSC at different nodes of the network, the MPSC can easily be turned into a portable device. Some typical features like compactness and wireless communication make such portability more realistic. Instead of large-scale rollout, the MPSC can be used as a mobile device to cover a wide area at lower cost. The device can be moved by an operator or embedded in a mobile robot (small boat, submarine, etc.) which carries the device through the medium of interest. Additional location data provided by an embedded GPS system would enable the mapping of a large area with a fine spatial mesh at very low cost. Such a solution can be very interesting for situations where high temporal sampling rate is not required.

The present thesis takes place in the framework of a European research project, PROTEUS, which addresses these different problems by designing and fabricating a multi-parameter sensor chip (MPSC) for water resources monitoring. The MPSC enables the measurement of 9 physical and chemical parameters, is reconfigurable and self-powered. The present thesis addresses more precisely physical sensors, their design, optimization and co-integration on the MPSC. The developed device exhibits state of the art or larger performances with regard to its redundancy, turn-down ratio and power consumption.

The present manuscript is split into two main parts: Part-I and Part-II. Part-I deals with non-thermal aspects of the MPSC, the pressure and conductivity sensor for instance, as well as the fabrication process of the whole device (Chapter 1 and 2). The background of environmental monitoring is presented in Chapter 1 along with the State of Art review. Chapter 2 describes fabrication methods of the MPSC. Preliminary characterization results of non-thermal sensors are also reported in this chapter. Chapter 3 and 4, included in Part-II, deal with thermal sensors (temperature and flow-rate). Chapter 3 describes the many possible uses of electric resistances for sensing applications. Finally, in chapter four, we focus on flowrate sensors before concluding and making a few suggestions for future works. Résumé L'eau est une ressource vitale, indispensable à la vie sur terre. A l'instar de nombreuses autres ressources naturelles, l'eau propre à la consommation est soumise à une forte pression à cause de l'impact de l'activité humaine d'une part et de l'augmentation continue de la population mondiale d'autre part. Une pression tellement forte que l'eau propre représente l'un des 17 objectifs de développement durable des Nations Unies. Dans ce contexte, une gestion rationnelle et durable de la ressource s'avère indispensable. Dans ce but, un système intelligent de supervision des réseaux d'eau potable peut s'avérer très utile. Les systèmes existant sont toutefois peu intégrés et compacts, nécessitent souvent une alimentation externe, et restent relativement chers pour un déploiement massif sur les réseaux. La présente thèse s'inscrit dans le cadre d'un projet de recherche européen, PROTEUS, visant à pallier ces différents problèmes en mettant au point un système de mesure pour la supervision de la ressource en eau permettant la mesure de 9 paramètres physico-chimiques, reconfigurable, et énergétiquement autonome. La contribution de la présente thèse à ce projet porte sur la conception et l'optimisation des différents capteurs physiques (conductivité électrique, pression, température et débit) ainsi qu'à leur cointégration sur une même puce. Le système proposé montre des performances au moins égales à celle de l'état de l'art en ce qui concerne la robustesse, assurée par la redondance de nombreux éléments sensibles, le domaine de sensibilité et la consommation énergétique.

Le présent manuscrit est par conséquent construit comme suit : le premier chapitre est une introduction générale à la supervision de grandeurs environnementales et à la puce multi-capteurs. Le second chapitre décrit la structure de la puce multi-capteurs ainsi que les méthodes de fabrication utilisées, avec une attention particulière accordée aux capteurs de pression et de conductivité électrique. Le troisième chapitre porte sur l'utilisation de résistances électriques pour la mesure de diverses grandeurs physiques, notamment la température. Le dernier chapitre s'attarde plus particulièrement sur l'utilisation de ce type de résistances pour la mesure de débit avant de conclure et de proposer des perspectives pour des travaux futurs.

iv 2 .

 2 7.1 Conductivity...................................................................................................................... 2.7.2 Introduction of the conductivity sensor and its working principle ................................... 2.7.3 Experimental setup ........................................................................................................... 2.7.4 Results and Discussion ..................................................................................................... 2.7.5 Evaluation of Antifouling Properties of Al2O3 / TiO2 ALD-coatings ............................. 2.8 Conclusion ................................................................................................................................ 3 Chapter 3: Electrical resistance as a key building block for measurement of temperature and related water parameters ................................................................................................................................... 3.1 Introduction ............................................................................................................................... 3.2 Water temperature variations and its impacts ........................................................................... 3.2.1 Natural and anthropogenic factors impacting water temperature ..................................... 3.2.2 The impact of water temperature on other parameters governing water quality .............. 3.3 Fundamentals on temperature and its measurement ................................................................. 3.3.1 Basics on temperature ....................................................................................................... 3.3.2 Brief history of temperature measurement........................................................................ 3.3.3 Electrical Resistance Devices ........................................................................................... 3.3.4 Resistance Temperature Device (RTD) ............................................................................ 3.4 RTD-based Temperature Sensor ............................................................................................... 3.4.1 Design of the temperature sensor ...................................................................................... 3.4.2 Characterization ................................................................................................................ 3.4.3 Joule Self-Heating ............................................................................................................. 3.5 Evaluation of fabrication process uniformity in terms of resistance variations and TCR at waferlevel ........................................................................................................................................... 3.6 The electrical resistance as a temperature sensor and multi-purpose building block for sensors based on Heat transfer ............................................................................................................... 3.6.1 Application of RTD as Flow-rate sensor .......................................................................... 3.6.2 Application of RTD as Thermal Conductivity Detector (TCD) ....................................... 3.7 RTD Transient Behavior ...........................................................................................................

v 3 .

 3 7.1 Simulation of RTD transient behavior .............................................................................. 3.7.2 Experimental measurement of RTD transient response .................................................... 3.8 Conclusion ................................................................................................................................ 4 Chapter 4: Flow-Rate Sensor Co-integration ....................................................................................... 4.1 Background and State of Art ..................................................................................................... 4.1.1 Flow-rate measurement ..................................................................................................... 4.1.2 Commercial flow-rate sensors .......................................................................................... 4.1.3 Micro flow-rate sensors .................................................................................................... 4.1.4 Thermal Flow-rate sensors ................................................................................................ (i) Anemometric operation principle ............................................................................................ (ii) Calorimetric operation principle ............................................................................................. (iii) Time-of-flight (TOF) operation principle .............................................................................. 4.2 Materials and Methods .............................................................................................................. 4.2.1 Design summary of thermal Flow-rate sensors with co-integration constraints ............... 4.2.2 Numerical simulation method ........................................................................................... 4.2.3 Experimental Setup for calibration of the Flow-Rate Micro-sensors ............................... 4.3 Numerical Results and Discussion .......................................................................................... 4.3.1 Glass substrate based flow-rate sensor ........................................................................... 4.3.2 Silicon substrate based flow-rate sensor ......................................................................... 4.3.3 Silicon membrane based flow-rate sensor ...................................................................... 4.3.4 Pillar structure flow-rate sensor ...................................................................................... 4.3.5 Reverse Flow .................................................................................................................. 4.3.6 Numerical results comparison......................................................................................... 4.4 Experimental Results .............................................................................................................. 4.4.1 Joule self-heating and Power consumption ..................................................................... 4.4.2 Sensor's response to the velocity .................................................................................... 4.4.3 Comparison between numerical and experimental results .............................................. vi 4.5 Conclusion .............................................................................................................................. 5 Conclusion and Prospects ................................................................................................................... 5.1 General conclusion .................................................................................................................. 5.2 Perspectives of future developments of the MPSC ................................................................. 1 Appendix for Chapter 2 ...................................................................................................................... 1.1 Annex 1-Pressure sensor behavioral simulation .................................................................... 1.2 Annex 2-Conductivity Sensor-Part A: Electrical Modelling ................................................ 1.2.1 The electronic model of the sensor for numerical analysis ............................................. 1.2.2 Frequency response ......................................................................................................... 1.3 Annex 3-Conductivity sensor-Part B: details of the experimental setup .............................. 1.3.1 Data acquisition .............................................................................................................. 1.3.2 Tools used for Anti-fouling test: ..................................................................................... References ................................................................................................................................................ List of publications ...................................................................................................................................

Figure i- 1 : 2 Figure i- 2 : 3 Figure 1 - 1 : 11 Figure 1 - 2 : 12 Figure 1 - 3 : 12 Figure 1 15 Figure 1 - 5 : 16 Figure 1 - 6 :

 1223111112121312115151616 Figure i-1: Schematic representation of water distribution monitoring network .......................................... 2

Figure 1 -

 1 Figure 1-7: Detection Micro-biological water contamination alert systems, based on optofluidic technologies. (a) Parasitometer from Water Optics Technologies and (b) E-Alert system from FLUIDION .................................................................................................................................................................... 17

Figure 2 - 1 : 21 Figure 2 - 2 : 23 Figure 2 - 3 : 24 Figure 2 - 4 : 25 Figure 2 - 5 : 26 Figure 2 - 6 : 28 Figure 2 - 7 : 30 Figure 2 - 8 : 31 Figure 2 - 9 : 31 Figure 2 - 10 : 32 Figure 2 - 11 : 36 Figure 2 - 12 : 36 Figure 2 - 13 : 37 Figure 2 - 14 : 38 Figure 2 - 15 : 39 Figure 2 - 16 : 41 Figure 2 - 17 : 42 Figure 2 - 18 :Figure 2 - 44 Figure 2 - 46 Figure 3 - 1 : 53 Figure 3 - 2 : 62 Figure 3 - 3 : 64 Figure 3 - 4 : 65 Figure 3 - 5 : 66 Figure 3 - 6 : 69 Figure 3 71 Figure 3 - 8 : 72 Figure 3 - 9 :Figure 3 - 10 :Figure 3 - 11 : 77 Figure 3 - 12 : 78 Figure 3 - 13 :Figure 3 - 14 : 80 Figure 4 - 1 : 85 Figure 4 - 2 : 86 Figure 4 - 3 : 87 Figure 4 - 4 : 87 Figure 4 - 5 : 88 Figure 4 - 6 : 88 Figure 4 89 Figure 4 - 8 :Figure 4 - 9 :Figure 4 - 10 :Figure 4 - 11 :Figure 4 -Figure 4 - 13 :Figure 4 - 14 :Figure 4 - 15 :Figure 4 - 16 :Figure 4 -Figure 4 - 18 :Figure 4 - 19 :Figure 4 - 20 :Figure 4 - 21 :Figure 4 - 22 :Figure 4 - 23 :Figure 4 - 24 :Figure 4 - 25 :Figure 4 - 26 :Figure 4 - 27 :Figure 4 - 28 :Figure 4 -Figure 4 -Figure 4 -Figure 4 - 32 :Figure 4 - 33 :Figure 4 - 34 :Figure 4 -Figure 4 - 36 :Figure 4 - 37 :

 21212223232424252526262827302831293121032211362123621337214382153921641217422182442463153326233643465356636693713872393103117731278313314804185428643874487458846884894849410411441341441541644184194204214224234244254264274284444324334344436437 Figure 2-1: (a) Cross-section view of the multi-parameter sensor chip based on a glass substrate and (b) fabricated multi-parameter sensor chip on a glass substrate ....................................................................... 21

Figure I- 1 :

 1 Figure I-1: Schematic representation of water distribution monitoring network

  Figure I-2(c)

Figure I- 2 :

 2 Figure I-2: PROTEUS multi-parameter sensor chip for water network monitoring-(a) sensor chip co-integrated 4 MEMS physical sensors along with 5 chemical sensors, (b) capsule board with the sensor and CMOS chip with electronics and (c) sensor head.

Figure 1 - 1 :

 11 Figure 1-1: WSNS based IAQ monitoring device

Figure 1 - 2 :

 12 Figure 1-2: The Mobile sensor node

Figure 1 - 3 :

 13 Figure 1-3: Experimental design of Multi-sensor module for indoor climate monitoring

Figure 1 -

 1 Figure 1-3 illustrates the experimental design of the multi-sensor module for indoor climate monitoring.

Figure 1 -

 1 Figure 1-4: s::can multi-parameter water quality monitoring device based on UV-VIS spectroscopy

Figure 1 - 5 :

 15 Figure 1-5: Commercially available multi-parameter sensing probes from-(a) Intellitect, (b) Six Sense and (C) Endetec.

Figure 1 - 6 :

 16 Figure 1-6: Laser-based water particle monitoring system BioSentry 27 .

Figure 1 - 7 :

 17 Figure 1-7: Detection Micro-biological water contamination alert systems, based on optofluidic technologies. (a) Parasitometer from Water Optics Technologies and (b) E-Alert system from FLUIDION

Figure 2 - 1 :

 21 Figure 2-1: (a) Cross-section view of the multi-parameter sensor chip based on a glass substrate and (b) fabricated multi-parameter sensor chip on a glass substrate

Figure 2 -Figure 2 - 2 :

 222 Figure 2-2: Different fabrication phases of the silicon-based MPSC. (a) Front side patterning of the device-pressure sensor fabrication together with the other sensors, (b) hard mask patterning on the back side and back-side DRIE for creating the membrane, (c) removal of the hard mask for silicon-glass anodic bonding and (d) micrograph of a co-integrated multiple sensing elements on silicon at the chip scale, after wafer dicing.

Figure 2 - 3 :

 23 Figure 2-3: Cross-section view of the membrane-based flow-rate sensor

Figure 2 - 4 :

 24 Fabrication sequences of the pillar structure flow-rate sensor. (a) Patterning of the front-side cavity and etching of the front side deposited layers, (b) front-side DRIE, (c) SEM photo of the resulting pillar flow-rate sensor

Figure 2 -

 2 Figure 2-4 illustrates the fabrication sequences of the pillar structure flow-rate sensor, where Figure 2-4(a) shows the patterning of the cavity and etching the front side materials for creating the cavity and Figure 2-4(b) reports the fully etched cavity by DRIE with the silicon pillar.

Figure 2 - 5 :

 25 Figure 2-5: 2D cross-section view of the bridge structure flow-rate sensor

Figure 2 - 6 :

 26 Figure 2-6: The PCB for connecting the sensors with an electronic circuit. (a) The whole PCB labelled with dimensions, (b) the zoomed image of the chip containing part, where the connection pads are wire bonded (dotted orange circle) to the PCB and protected hermetically with glue against the water. The other part of the chip is not connected to the PCB.

Figure 2 - 7 :

 27 Figure 2-7: Typical layout of the MEMS pressure sensor. Different versions were considered in order to investigate the sensitivity with respect to the pressure.

Figure 2 - 8 :

 28 Figure 2-8: Experimental setup of the pressure sensor. (a) The sensor is inserted into the glass bottle and the surroundings of the insertion whole glued in order to make it leakage proof. A transparent plastic pipe is used to transmit the hydrostatic air pressure from the Fluigent SA to the glass bottle. (b) A schematic diagram which replicates the experimental setup of the pressure sensor.

Figure 2 - 9 :Figure 2 -Figure 2 - 10 :

 292210 Figure 2-9: Schematic diagram of the data reading circuit. Here, the Wheatstone bridge consists of four resistors R1, R2, R3 and R4 represent the four piezoresistors of the pressure sensor.

Figure 2 -

 2 Figure 2-11: Schematic illustration of ions migration in a solution due to the electricity The water conductivity is measured using considered sensor by injecting an AC current I to the outer two electrodes. Thus, the electricity passes through the water and two inner voltage electrodes determine the voltage drop V hence the resistivity of the water after calibration (see details in section 2.6.1). The reciprocal value of this resistivity gives the corresponding conductivity. A schematic diagram of the sensor's electrodes is presented in Figure 2-12 below in case of a 4-probe terminal configuration.

Figure 2 - 12 :

 212 Figure 2-12: Current and voltage electrodes of the conductivity sensor

Figure 2 - 13 :

 213 Figure 2-13: Different versions of the conductivity sensor

Figure 2 -

 2 Figure 2-14: Different parts of the conductivity sensor electrodes labelled with nomenclature

  photo of the experimental setup is demonstrated in Figure 2-15.

Figure 2 - 15 :

 215 Figure 2-15: Conductivity sensor experimental setup. The sensor is submerged under a solution. There are five beakers with different conductivity solutions. The input-output of the sensor is connected to the NI DAQ board for supplying the power and reading the measured data.

  -16, the frequency sweep analysis of the three versions of the conductivity sensors is presented. The notations S1, S2 and S3 stand for sensor 1, sensor 2 and sensor 3 respectively.For each sensor type, the responses were measured with respect to the five reference solutions within the above-mentioned frequency range. A monotonous trend under a certain frequency range for a specific sensor represents the linear operating frequency of the corresponding sensor. In Figure2-16(a), the first version of the conductivity sensor with square-shaped electrodes shows irregular responses within the whole velocity range for each reference solutions. These results denote that the conductivity sensor S1 will exhibit almost no linear sensitivity for measuring the conductivity within this frequency range, probably due to the square shape of its electrodes, leading to excessive border effects. On the contrary, the subsequent two versions S2 and S3 (Figure2-16 (b) and (c)) -both with rectangular electrodes, show uniform horizontal response under a certain frequency range starting after 200 Hz to until 10 kHz. This frequency range can be referred to as the linear operating frequency range and the gap magnitudes between the different conductivity solutions indicate the sensor's sensitivity to different conductivity level.Based on the frequency sweep experiments results, the considered three sensors were operated at 5 kHz, then 10 kHz and then 15 kHz. For each frequency, the sensor was submerged under the five solutions in a successive manner and the measured data were recorded. For each time, 20 measurements were performed in order to determine the uncertainties. The obtained results are presented in Figure2-17 below.

  Figure 2-16: Responses of the three sensors in frequency sweep analysis (a) sensor 1, (b) sensor 2 and (c) sensor 3 under the frequency range ~500 µS/cm to ~2500 µS/cm. Linear frequency range Linear frequency range No specific frequency range is observed (a) (b) (c) Figure 2-17: The response of the S1, S2 and S3 sensors under the considered five conductivity solutions at 5 kHz, 10 kHz and 15 kHz supply frequencies. Error bars are added in the plots, but not visible due to the small uncertainties.

Figure 2 - 18 :

 218 Figure 2-18: Conductivity sensor response at 5 kHz with and without coating layer made of Al2O3

  frequencies and the comparative results of this device with the others (Al2O3 and without ALD) are reported in Figure2-19.

Figure 2 -

 2 Figure 2-19: (a) The measured response of the S2 sensor with TiO2 ALD layer at 5 kHz, 10 kHz and 15 kHz and (b) comparison among the measured response of the same sensor with TiO2, Al2O3 ALD layer and without ALD layer.

Figure 2 -

 2 Figure 2-20: (a) Two prepared PVC tubes for the test where the sensor chips are mounted on their head, after oven dried at 65 °C, each tube contains four chip denoted by A, B, C and D. No ALD coating on-chip A, chip B is coated by Al2O3 with 10 nm thickness and chip C and D are coated by TiO2 with 10 and 30 nm layer thicknesses respectively. (b) The prepared prototype for the field test.

Figure 3 - 1 below

 31 illustrates the main properties of water that are related by temperature. More details on those relations are given in what follows.

Figure 3 - 1 :

 31 Figure 3-1: Main water quality parameters that are affected by the water temperature (TDS: Total Dissolved Solids, ORP: Oxidation Reduction Potential).

Chapter 3 62 3 . 4 . 1 Figure 3 - 2 :

 334132 Figure 3-2: (a) Mask Layout of the temperature sensor; 1, 2, 3 and 4 represent the connection wires where 1 and 4 are used for power supply and 2 and 3 to read data, (b) physical dimensions of sensing resistor of the temperature sensor and (c) real temperature sensor.

Figure 3 - 63 ( 8 )

 3638 Figure3-2 shows the geometry of the temperature sensor. The sensing resistor is connected to four wires enabling four probes measurement. The two outer wires, which are also the widest, are used for current injection through the corresponding pads, while the two inner wires, which also the thinnest, are used for voltage readouts. This 4-probe arrangement allows deducing of the exact resistance of the temperaturedependent sensing element, with minimum influence of the wires.

Figure 3 -

 3 2(d) gives a close view of the resistor design along with some guidelines used to evaluate its total length.

Figure 3

 3 Figure 3-3.

Figure 3 - 3 :

 33 Figure 3-3: Experimental setup for the temperature sensor Calibration.

(

  from 90 °C to 25 °C). The measured response, its resistance variation namely, is plotted as a function of temperature in Figure3-4. Using the fit equation from Figure3-4, and given an experimental resistance value within the range shown in Figure3-4, the corresponding temperature can be calculated in a straightforward manner.

Figure 3 - 4 :

 34 Figure 3-4: Temperature sensor response at different water temperature under 0.5 mA current supply.

Figure 3 - 5 :

 35 Figure 3-5: Illustration of Joule self-heating phenomenon. Depending on the current magnitude injected into a resistor, the local temperature is more or less affected, leading to 2 regimes: (i) low current, typically for operation of the temperature sensor resistor, (ii) High current, which is conducive to operate the flow-rate sensor resistor.

  -6 similar curves as the experimental curve presented in Figure3-5, corresponding to a resistor whose dimensions are (L= 106 µm, w=10 µm, t=0.34 µm) and whose nominal resistance if 9,7 Ω at room temperature. Material properties of platinum are taken as follows: electrical resistivity 𝜌 𝑒 = 3.10 × 10 -7 Ω. 𝑚, thermal conductivity 𝜆 = 71.8 𝑊𝐾 -1 𝑚 -1 and 𝑇𝐶𝑅 = 2219 𝑝𝑝𝑚/𝐾. It is worth-mentioning that, contrary to electrical resistivity, it appears from the literature that there is no significant difference between bulk and thin-film values of thermal conductivity. It varies only from 71.8 to 80.7 𝑊𝐾 -1 𝑚 -1 in case of thin-films 90 .

Figure 3 -

 3 Figure 3-6 gives the calculated temperature variation versus current, based on the first order model (equation 3-11) and the second-order model (equation 3-16). The latter illustrates a clear divergence after 10 mA (while at lower current values, the curves are almost the same). The effect of divergence (due to the negative sign in the denominator of equation 3-16), is very significant at the large current values, since at 30 mA, we reach 1200° while it is only 300° with the first order model. Now comparing those modeling results with the experimental results shown in Figure 3-5, (which show a maximum heating of 65°), one

Figure 3 - 6 :

 36 Figure 3-6: Theoretical Joule self-heating on the same resistor as the one considered for the experimental result shown in Fig. 3-5. Both first order model (equation 3-11) and second order model (equation 3-16) have been used.

Figure 3 - 9 and

 39 Figure 3-9 and Figure 3-10. It is worth mentioning at this stage that all recorded values show excellent

Figure 3 -Figure 3 - 8 :

 338 Figure 3-7: Wafer-level mapping of resistance values recorded for temperature sensors and flow-rate sensors recorded at 3 temperatures: 20°C, 60°C and 100°C.

Figure 3 - 9 :

 39 Figure 3-9: Wafer-level mapping of Resistance values recorded at 20°C and 100°C for pressure sensors

1

 1 Simulation of RTD transient behavior 3.7.1.

Figure 3 -

 3 11 (a). Second, as depicted in Figure3-11 (b), one can take into account the surrounding environment, which consists of a substrate material (either silicon or glass) and a surrounding fluid (either air or water).

Figure 3 - 11 :

 311 Sketch of 3 different models accounting for heat exchange phenomena on a heated resistor. (a) heat transfer only within the resistor's material, (b) additional heat exchanges with the substrate material and surrounding fluid medium and (c) heat transfer through convection due to the fluid velocity. Those materials both solid and fluidic will lead to additional heat conduction. Finally, in case the fluid is moving with a given velocity, one can also take into account the resulting additional heat transfer by convection, as illustrated in Figure 3-11 (c). Those different scenarios have been evaluated in the simulation results presented in what follows. The numerical simulations have been performed on COMSOL Multiphysics based on the same resistor described before for the temperature sensor, made of platinum and with dimensions given in section 3.4.1.First, we considered the effect of the substrate material (either glass or water) as well as the effect of the surrounding fluid (either air or water). The corresponding results are Figure3-12 and summarized in Table

Figure 3 - 12 :

 312 Figure 3-12: Numerical simulations of the transient temperature response, considering different substrate materials (Silicon versus Glass) and surrounding fluid media (Air versus Water).

Figure 3 - 13 :

 313 Figure 3-13: Numerical simulations of the transient temperature response of a heated resistor, considering the effect of velocity (in this case substrate material is Glass and surrounding fluid is Air).

Figure 3 - 14 :

 314 Figure 3-14: Experimental measurement of the transient temperature response of 3 different resistor designs based on silicon substrate, operated under 3 different water velocities.

4 . 1 . 1

 411 platinum-based Resistance Temperature Device (RTD) as a suitable solution for operation under water environment, which eventually appears also as a convenient building block for other kinds of sensors based on heat transfer, which include the flow-rate sensor, presented in the next chapter. The RTD has been studied in detail through analytical models, numerical simulations and experimental measurements. Both steady-state and transient behavior have been investigated. Flow-rate measurement Flow-rate measurement is important in different aspects of human life. From the very beginning of the human being civilization until now, flow-rate measurement is involved in different fluid media. The history of flow-rate measurement is quite primordial. Since the early ages, cognitive perceptions about the direction and the velocity of different fluids i.e., air, water helped the helmsman for navigation purposes. In the ancient times, the Egyptians used flow measurement of the Nile river's water in order to predict their crops harvesting condition94,95 . From historical perspective, the first water flow measurement was used by the ancient Sumerians cities of Ur and Kish near to the Tigris and Euphrates rivers in 5000 BC for supplying water in the cities' aqueducts 96 .

Figure 4 - 1 : 4 86Figure 4 - 2 :

 41442 Figure 4-1: Flowmeters manufactured by OMEGA company

Figure 4 - 3 :

 43 Figure 4-3: Fluigent flow-rate platform, (a) adjustable pressure generator (b) flow units connected to the flow board

Figure 4 - 4 :Figure 4 - 4 : 4

 44444 Figure 4-4: Cross-section view of a micro-machined flow-rate sensor 109

Figure 4 - 6 : 4 89

 464 Figure 4-6: Fabricated flow-rate sensor (a) microchannel fabricated on the substrate and (b) a suspended microchannel 110

Figure 4 - 7 :

 47 Figure 4-7: Schematic representation of single hair cell biomimetic flow-rate sensor 111

Figure 4 - 8 :

 48 Figure 4-8: Schematic illustration of Hot-wire anemometer flow-rate sensor 82

  A fluid flow in a given direction induces an asymmetry of the temperature profile. The profile also depends on the fluid velocity. Measuring the temperature profile hence enables the determination of the fluid velocity. Such sensor contains at least two temperature sensors upstream and downstream of a heating resistor. These two sensors detect the distorted thermal profile around the heater resistor due to the flow velocity. At zero velocity, temperature profile is symmetric in the vicinity of the heater and there is no significant temperature difference between the two sensing resistors. When velocity is introduced, temperature profile around the heater is distorted and the temperature difference between the two upstream and downstream temperature sensors increases. The calorimetric sensor can not only detect the fluid flow-rate but also the direction of the flow as well. A schematic representation of a calorimetric flow-rate sensor is shown in Figure 4-9.

Figure 4 - 9 :

 49 Figure 4-9: Schematic representation of calorimetric operation mode of a thermal flow-rate sensor 82

  iii) Time-of-flight (TOF) operation principle In a Time-of-Flight flow-rate sensor, flow-rate is determined by calculating the travel time of a thermal pulse in a fluid medium over a known distance. The TOF sensor consists of one heater and (at least) one sensing element. The heat pulse is generated from the heater resistor in the surrounding moving fluid and detected by the sensing resistor. A schematic description of the TOF operation mode is presented in Figure 4-10.

Figure 4 - 10 :

 410 Figure 4-10: Schematic representation of Time-of-Flight operation principle of a thermal flow-rate sensor 82

  (m/s) 𝛥𝑆 = distance (m) ∆𝑡 = time duration between the pulse generation and detection

  = temperature distribution at time t (K) 𝑥 = distance between the heater and the considered position (m) 𝑡 = time (s) 𝑞 0 = pulse signal input strength 𝑘 = thermal conductivity of the fluid (W/m.K) 𝑎 = thermal diffusivity (m 2 /s) 𝑣 = average flow velocity (m/s)

Chapter 4 97 4 . 2 . 1 4 Figure 4 - 11 :

 44214411 Figure 4-11: The 4 different configurations of the flow-rate sensor-(a) glass substrate, (b) bulk silicon, (c) silicon membrane and (d) pillar structure It can be observed from Figure 4-11 that the first three versions of the flow-rate sensor have three Platinum (Pt) resistors and the silicon pillar structure has only one Pt resistor. First three prototypes contain

Figure 4 -Figure 4 -

 44 Figure 4-12(a) and (b) illustrate the whole experimental setup, which can be described in two parts: a fluidic circuits and an electrical circuit.

  velocity can be varied by varying the supply voltage of the pump. The different magnitudes of the flow-rate generated by the pump, for the considered setup and circuit, is first calibrated at different voltage intensities by volumetric measurement. Under a certain voltage supply, the required time was recorded to fill a certain volume of water. Since the diameter of the pipe was known; therefore, the velocity was calculated from the obtained flow-rate value, accordingly. Table4-2 presents the values of the generated flow-rates obtained at different voltage levels (as well as the corresponding average velocities estimated as the ratio of flow-rate over the pipe cross-sectional area). After this calibration, the actual flowrate was determined during the experiments simply from the data of the pump input voltage. During the measurements, the process was repeated several times in order to reduce the uncertainty level of the recorded time

Figure 4 -

 4 13, shows a schematic of the constant current source circuit. To increase the accuracy of measured data, a 4-probe measurement scheme is used so as to eliminate the parasitic wiring resistance which may vary due to local heating. The two outer ports are used to inject the current and remaining two inner ports are used for data reading of the voltage across the sensor's heater resistance, which is an indication of its temperature and hence of the flow-rate.

Figure 4 - 13 :

 413 Figure 4-13: Schematic of the constant current circuit source.

Figure 4 - 14 showsFigure 4 - 14 :

 414414 the simulation domain of the glass substrate flow-rate sensor. Geometry of (a) the whole simulation domain, (b) the sensor chip and (c) the platinum sensing element on top of a glass substrate.

Figure 4 - 15 :

 415 Figure 4-15: The picture of (a) velocity profile and (b) temperature profile of the glass-based flow-rate sensor exported from COMSOL

Figure 4 -

 4 Figure 4-15(a) and (b) illustrates the velocity and temperature profiles for the glass substrate device. We observe a zero velocity at the top and bottom edges of the fluidic domain (Figure 4-15(a)). This is consistent with no-slip condition at the pipe boundaries Velocity is maximal at the sensor chip vicinity and the fluidic domain outlet. On the other hand, the maximum temperature is observed at the heater resistor position and the temperature field around the heater is distorted according to the flow direction (here to the right).

Chapter 4 107Figure 4 - 16 :

 4416 Figure 4-16: Normalized temperature of the glass based sensor versus fluid velocity.

  108

Figure 4 -Figure 4 -

 44 Figure 4-17: (a) Geometry of silicon substrate flow-rate sensor, and a (b) zoom picture of the platinum resistor and SiO2 insulation layer

Figure 4 - 18 :

 418 Figure 4-18: Temperature profile of the silicon based flow-rate sensor at v = 0 m/s.

Figure 4 - 19 :

 419 Figure 4-19: Silicon substrate flow-rate sensor's response versus the water velocity.Silicon-based sensor exhibits a similar response profile to glass based sensor with respect to velocity.The normalized temperature variation of the silicon device reported in Figure4-19 shows a similar trend but significantly different values for a given velocity. Indeed, the device temperature decreases more rapidly for silicon base device. For instance, within the velocity range from 0.1 m/s to 10 m/s the thermal variation is less than 0.1. The sensor is almost saturated for a fluid velocity of 0.1 m/s and hence not very sensitive

Figure 4

 4 Figure 4-20.

Figure 4 - 20 :

 420 Figure 4-20: Membrane structure flow-rate sensor

Figure 4 - 21 :

 421 Figure 4-21: Temperature field around the membrane flow-rate sensor for a fluid velocity of 0.001 m/s.

Figure 4 - 22 :

 422 Figure 4-22: Membrane-based flow-rate sensor response with respect to the velocity

Chapter 4 112 4 . 3 . 4 Figure 4 - 23 : 4 113

 44344234 Figure 4-23: The pillar based flow-rate sensor geometric configuration.

Figure 4 - 24 : 4 114Figure 4 - 25 :

 4244425 Figure 4-24: The temperature field of the pillar flow-rate sensor at 10 mW power supply at a velocity of 0.04 m/s.In this case, the cavity dimension is 150 x 100 µm and the resistor width is 5 µm.

Figure 4 - 4 115Figure 4 - 26 :

 44426 Figure 4-26 the sensor response as a function of velocity for different resistor's widths.

Figure 4 - 27 :

 427 Figure 4-27: Pillar structure flow-rate sensor response as a function of fluid velocity for different cavity depths. Numbers between parentheses in the legend indicate the pillar height (equal to the cavity depth) and width then the cavity width, respectively.

Figure 4 - 28 :

 428 Figure 4-28: Pillar flow-rate sensor response as a function of veocity for different cavity depths.

  -25 a discrepancy of the sensor response, indicated by black dashed circle. The same type of discrepancy is observed in Figure4-26, Figure4-27 and Figure4-28. Instead of a monotonic temperature decrease when the fluid velocity increases, we observe an increase of the sensor temperature for fluid velocities between 0.1 and 1 m/s. We revealed through additional numerical simulations that this apparent inconsistency happens due to "a reverse flow" observed locally at the heater vicinity in this velocity range. The main water flow is heated by the heating resistance after a first pass. For a specific velocity range, and due to the device geometry, hot water is carried back from the heater downstream and induces an increase of the sensor's temperature. As a result, a velocity increase induces a heater temperature increase rather than the opposite. At 0.3 m/s and 0.4 m/s, the pillar and glass based flowrate sensors show this inconsistent response. The heater temperature of the glass substrate flow-rate sensor is tabulated in Table4-3 for a velocity range from 0.2 m/s to 0.5 m/s. It can be seen that when the velocity increases from 0.2 m/s to 0.3 m/s, the heater temperature value is increased rather than decreased. The heater temperature recovers the usual behavior, i.e. a decreasing temperature, when velocity exceeds 0.4 m/s. The reverse flow can be observed on the simulated velocity field around the device. We show in Figure4-29 (a) and (b) both the normal flow velocity field and the reverse flow velocity field in the case of pillar structure flow-rate sensor. We also show in Figure4-29(c) the temperature profile in the presence of a reverse flow. We clearly observe that the temperature profile is distorted in the opposite direction to the main flow. It is worth noting at this stage that the main stream direction is left to right. In the absence of a back flow, the temperature profile is distorted accordingly and the largest temperatures are observed downstream, i.e. on the right side of the heater. A similar reverse flow is observed for bulk silicon and membrane-based sensors. However, in these cases, reverse flow fluid is not hot enough to increase the heater temperature because of lower initial Joule self-heating and larger thermal leakage through the substrate.

Figure 4 -

 4 Figure 4-29: (a) Normal velocity profile for different fluid velocities, (b) reverse flow at the resistor vicinity causing the anomalous thermal behaviour within the velocity range highlighted by black circles in Figure 4-16 and Figure 4-25, (c) normal temperature profile and (d) the effect of reverse flow on the sensor temperature profile (flow direction is from left to right).

Figure 4 -Figure 4 -

 44 Figure 4-30(a) and Figure 4-30(b), respectively. The circle in Figure 4-30(a) indicates the location of the sensor chip with respect to the node head.

Figure 4 -

 4 Figure 4-31: (a) The picture of the round-edged sensor node tilted at 15° with respect to the normal and inserted into a water pipe, (b) the sensor node head chamfered with a 0.5 mm radius and (c) 1 mm radius.

Figure 4 - 32 :Figure 4 - 33 :

 432433 Figure 4-32: Illustration of the sensor node head and the region that was considered for extracting the velocity field in X-coordinate. Parallel red lines (2-D cut line) represent the extent of the region and they have uniform distance (0.5 mm) between them.

Figure 4 -

 4 Figure 4-34 as a function of the main flow velocity, the numerically obtained temperature variation of the different versions of the flow-rate sensor. Here the pillar device response, with a 5 µm-wide resistor is compared with the bulk silicon, membrane and glass devices.

Figure 4 - 34 :

 434 Figure 4-34: Simulation results of glass, pillar, membrane and bulk silicon flow-rate sensor response to the main flow velocity.

Figure 4 -

 4 Figure 4-35: (a) Joule self-heating temperature of the heating resistors at different supply current in non-flowing fluid, and (b) power consumption of the four corresponding sensors.

  -36). A constant current 30 mA is supplied to the sensor heater. Then, the temperature variation of the resistor due to the flowing fluid is measured. At each velocity value, thirty measurements are taken to reduce uncertainties. Results are shown for 30 mA supply current on Figure4-36. We plot the sensor response to water velocity using the previously defined normalized temperature. We can see, as shown through numerical simulation, that the response of the pillar structure sensor is close to, and even better than, that of the glass sensor. Although the glass substrate flow-rate sensor exhibits highest Joule self-heating, the pillar flow-rate sensor shows a more linear response of the relative temperature variation as a function of velocity and it also covers a larger velocity range. In the case of the glass flow-rate sensors, the non-linear behavior can be ascribed to a back-flow phenomenon in the velocity range from 0.2 m/s to 0.4 m/s. On the other hand, bulk-silicon and membrane-based sensors show Chapter 4 126 inconsistent response in a significant part of to the considered velocity range. The recorded sensor responses to fluid velocity are consistent with Joule self-heating results. Here again, there could be an effect of backflow in the case of the membrane-silicon device in the velocity range from 0.3 m/s to 1 m/s.

Figure 4 - 36 :

 436 Figure 4-36: Glass, pillar, silicon membrane and bulk silicon flow-rate sensor measured response as a function of velocity at 30 mA current supply.

  -34) and experimental (Figure4-36) sensors responses to fluid velocities show that bulk silicon and silicon membrane sensors' responses are similar and not satisfactory for a velocity range up to 1 m /s. On the other hand, an apparent inconsistency arises from the comparison of numerical and experimental results for glass and pillar devices. In the experimental plot (Figure4-36), pillar flow-rate sensor shows better thermal response than glass. The opposite is observed on the numerical plot (Figure4-34). However, a comparison in the same velocity range for both experimental and numerical results shows that the pillar device provides lager normalized temperature than glass device for large velocities. Indeed, we observe on Figure4-37 that the pillar structure device generates larger normalized temperatures for velocities larger than 0.5 m/s. The remaining small difference between simulations and experimental results is very likely due to the approximated 2-D configuration used in the numerical study.

Chapter 4 127Figure 4 - 37 :

 4437 Figure 4-37: Numerically calculated response of the pillar and glass devices in the velocity range from 0 m/s to 1 m/s.

Final optimization has been

  carried out by exploring transient mode operation of the device instead of commonly used steady state operation to reduce power consumption and measurement time. Finally, the temperature sensor was based on the same technology than the flowrate sensor. It required fewer Chapter 5 130 developments and optimization since no joule self-heating is required which loosens constraints on the substrate material choice and power consumption.

  account and addressed by implementing a secondary power source. In the present work, alternative operation schemes based on the device transient response have been briefly mentioned. Such alternative methods have a great potential for power consumption reduction and can be explored more in detail in future work.Due to the target operation medium, water for instance, several electric precautions should be taken while performing experiments and measurements. Waterproofing is a critical constraint to prevent any damage to incorporated electronics. On the other hand, electrical connections between the sensor chip and the PCB is a second electric weak point that should be addressed carefully. The device mounting, packaging and waterproofing using protective glue should be performed meticulously. Long test campaigns should also be planned and executed to check waterproofing evolution with time. Long experimentation time can induce other problems such as biofouling. Indeed, in field test where water quality cannot be guaranteed, biological films deposition on the MPSC surface can occur. Such films would induce a shift in the device Chapter 5 131 response which is to be taken into account and corrected. Test scenarios to investigate the effect of such biofilms deposition should be considered in future works.

1 Chapter 1: Background on environmental monitoring (Air & Water) and related measurement tools 1.1 Fundamental natural resources for life and the necessity for environmental monitoring

  35conductivity value of the water sources, hence the typical conductivity ranges are presented in Table2-1 below proposed by different organization[START_REF] Eaton | Standard methods for the examination of water and wastewater[END_REF][START_REF] Swrcb | The Clean Water Team Guidance Compendium for Watershed Monitoring and Assessment State Water Resources Control Board[END_REF] .

. Although there is no defined Chapter 2

Table 2 -

 2 1: Conductivity ranges of different water

	Water source	Conductivity range (µS/cm)
	Ultrapure water	0.04 (@ 20°C)
	Distilled water	0.5 -3
	Tap water	50 -800
	Melted snow	2 -42
	Freshwater	100 -2000
	Portable water (US)	30 -1500
	Seawater	55000

Table 2

 2

-1.

Table 2 -

 2 1: Dimensions of the different parts of conductivity sensor electrodes

	Sensor Wc (µm)	Wv	* Lc (µm)	Wgc	Wgv	Wcv
			(µm)		(µm)	(µm)	(µm)
	1	500	50	500	800	640	30
	2	250	50	1000	800	640	30
	3	250	50	1000	3000	2840	30

Table 2 -

 2 2: Different versions of the conductivity sensor with different ALD coating material and thickness layer.

	chip version	ALD	thickness (nm) Quantity Comments
	1	Al203	10	2
	2	TiO2	30	2
	3	TiO2	10	2
	4	nothing	0	3	Si02,400nm

Table 2 -

 2 3: Anti-biofouling test results under wastewater medium.

		A	B	C	D
	Date	no ALD	Al203 10 nm	TiO2 30 nm	TiO2 10 nm	Comments
		(RLU)	(RLU)	(RLU)	(RLU)
	29/3/17 17:00	0	0	0	0	Fully cleaned before employment in the wastewater
	30/3/17 10:30	7832	7324	7130	7579	After 1 day
	30/3/17 11:00	0	0	0	0	Fully cleaned before employment in the wastewater
	3/4/17 10:45	3055	6747	5508	7648	After 3 days
	3/4/17 11:30	0	0	0	0	Fully cleaned before employment in the wastewater
	28/7/17 15:00	countless	countless	countless	countless	After 3 months

Table 2 -

 2 2 and 2-3 suggest that the addition of coating has no added value, or may even detrimental in comparison with the pure SiO2 surface.

	Chapter 2

Table 2 -

 2 4: Anti-biofouling test of the considered devices under treated drinking water environment.

		A	B	C	D
	Date	no ALD	Al203 10 nm	TiO2 30 nm	TiO2 10 nm	Comments
		(RUL)	(RUL)	(RUL)	(RUL)
	3/4/17 16:00	0	0	0	0	Fully cleaned before employment in water
	5/4/17 14:45	25	36	30	17	2 days, with free chloride > 0.5 mg/L
	5/4/17 15:00	0	0	0	0	Fully cleaned before employment in water
	20/4/17 9:45	0	0	1	55	15 days, with free chlorine > 0.5 mg/L
	20/4/17 10:30	0	0	0	0	Fully cleaned before employment in water
	5/5/17 9:45	0	0	2	2	15 days, with free chlorine > 0.5 mg/L
	5/5/17 10:30	0	0	0	0	Fully cleaned before employment in water
	28/7/17 15:00	11	24	130	82	84 days, with free chlorine > 0.5 mg/L

Table 2 -

 2 5: Anti-fouling test under raw drinking water

		A	B	C	D
	Date	no ALD	Al203 10 nm	TiO2 30 nm	TiO2 10 nm	Comments
		(RUL)	(RUL)	(RUL)	(RUL)
	28/7/17 16:00	0	0	0	0	Fully cleaned before employment in water
	17/1/18 15:2 0	60	91	70	88	173 days without free chlorine

Multi-Parameter Sensing Chip thermal sensors 3 Chapter 3: Electrical resistance as a key building block for measurement of temperature and related water parameters

Table 3

 3

	-1: Resistivity at 20 °C and TCR value of different materials 82,83
	Material	Resistivity (Ω.m)	TCR (10 -4 /K)
	Aluminum (Al)	2.69 × 10 -8	42.0
	Copper (Cu)	1.67 × 10 -8	43.0
	Gold (Au)	2.30 × 10 -8	39.0
	Nickel (Ni)	6.84 × 10 -8	68.1
	Palladium (Pa)	10.8 × 10 -8	37.7
	Platinum (Pt)	10.6 × 10 -8	39.2
	Silver (Ag)	1.63 × 10 -8	41.0
	Tungsten (W)	5.50 × 10 -8	46.0
	Iron (Fe)	9.71 × 10 -8	65.1

Table 3 -

 3

		2: Different Platinum Resistance Thermometer and their applications area 85
	RTD type	Application	Temperature range	Probe type
	SPRT	Calibration of secondary SPRT	-200 °C to 1000 °C	Immersion
	secondary SPRT	Laboratory use	-200 °C to 500 °C	Immersion, Air
	Wire-wound IPRT	Industrial use	-200 °C to 648 °C	Immersion, Air, Surface
	Thin-film IPRT	Industrial use	-50 °C to 260 °C	Immersion, Air, Surface

Table 3 -

 3 3: Wafer-level statistics for Temperature Sensor's Resistance Characterization.

		Temperature	Temperature	Temperature	TCR
		= 20°C	= 60°C	= 100°C	(ppm/°C)
	Mean Resistance value (Ω)	1015,5	1110,1	1195,5	2215
	Standard deviation (Ω)	12,9	14,7	15,7	21
	Ratio (%)	1,3%	1,3%	1,3%	0,9%

Table 3 -

 3 4: Wafer-level statistics for Flow-rate Sensor's Resistance Characterization.

		Temperature	Temperature	Temperature	TCR
		= 20°C	= 60°C	= 100°C	(ppm/°C)
	Mean Resistance value (Ω)	33,09	36,16	38,97	2219
	Standard deviation (Ω)	0,45	0,52	0,55	28
	Ratio (%)	1,4%	1,4%	1,4%	1,3%

Table 3 -

 3 5: Wafer-level statistics for Pressure Sensor's Resistance Characterization.

		Temperature =	Temperature	TCR
		20°C	= 100°C	(ppm/°C)
	Mean
	Resistance value	7772	8065	472
	(Ω)
	Standard deviation (Ω)	174	175	13
	Ratio (%)	2,2%	2,2%	2,8%

Table 3 -

 3 6: Thermal time constant considering different substrate materials and surrounding fluid media

		Glass substrate	Silicon substrate
	Surrounding fluid is Air	21 s	23 s
	Surrounding fluid is Water	507 s	662 s

Table 4

 4 90 parameters: power consumption and velocity/flow-rate range. The information about power consumption and velocity range are rarely available conjointly in most cases. Therefore, a few articles are enlisted here among numerous publications, which are mainly focused on micro-machined devices.

-1 between the considered flow-rate sensor of the MPSC and state-of-art devices. The collation of comparison is made upon focusing on two important Chapter 4

Table 4 -

 4 1: Comparison between the fabricated flow-rate sensor and the existing devices in terms of power consumption and velocity.

	Micro-machined	Power consumption	Velocity/flow-rate	Application
	Flow-rate sensor			domain
		Silicon substrate
	Ref. Dev. 1 109	> 30 mW	300 nL/min (in water)	Micro and nano
	Ref. Dev. 2 112	600 mW	25 µL -10 mL/min	Mass-flow
			(in water)
			0 -60 mL/min (in
			Nitrogen)
	Ref. Dev. 3 110	140 µW	< 10 nL/min (DI	Micro and nano
			water)
	Ref. Dev. 4 113	5 mW	0 -10 mL/min (in	Biomedical
			viscose sugar water)
	Ref. Dev. 5 114	< 5 mW	100 µg/h (in water)	Biology/Bio-
			100 nL/h (in gas)	MEMS
	Ref. Dev. 6 115	5.8 mW	0 -5 m/s (in air)	Autonomous
				navigation
	Ref. Dev. 7 116	1 mW/mm² (in gas)	0 -4 m/s (in liquid)	Thermal mass-
		4 mW/mm² (in	0 -180 m/s (in gas)	flow
		liquid)
	Ref. Dev. 8 117	< 10 mW	0 -8 m/s (in water)	Micro biology
				and micro chemical
	Ref. Dev. 9 118	380 µW	0 -1 m/s (in gas)	Microfluidic
	Our silicon based	43.78 mW	0 -0.91 m/s (in water)	Water
	pillar device			distribution network
		Glass substrate
	Ref. Dev. 10 119	3.3 W	10 -250 mm/s (in air)	Lab-on-chip

Table 4 -

 4 2: Supply voltage of the pump and the corresponding water flow-rates and average velocities generated by the pump in the considered experimental fluid loop.

	Supply voltage Vpump	Flow-rate	Average velocity
	(V)	(liter/minute)	(m/s)
	0	0	0
	2	1.1	0.05
	2.5	2.3	0.1
	3	3.4	0.15
	4	5.9	0.26
	5	7.5	0.33
	6	9.6	0.42
	7	11.6	0.51
	8	13.9	0.61
	9	15.3	0.67
	10	17.8	0.78
	11	18.7	0.82
	12	20.7	0.91

  -27 the sensor's response with respect to velocity for different cavity depths. No significant change in the thermal response is observed. At the low velocities, up to 0.001 m/s and velocities larger than 2 m/s, the different curves cannot

	be distinguished. A small but negligible difference is observed for intermediate velocities between, 0.001
	and 2 m/s, mainly for the 150-µm wide cavity. Consequently, sensors prototypes with two different cavity
	widths, 200 µm and 600 µm, have been fabricated.

Table 4 -

 4 3: Glass-based flow-rate sensor heater temperature variation with respect to velocity

	Velocity (m/s)	Theater (K)
	0.2	300.49447
	0.3	300.65392
	0.4	300.53829
	0.5	299.93127

Table 4 -

 4 4: Resistance variation with respect to the current supply

	Sensor	Resistance at room temperature	Resistance variation range (Ω)
		(Ω)
	Pillar	43.6	43.6 ~ 48.65
	Glass	12.92	12.92 ~ 14.68
	Silicon membrane	15.07	15.07 ~ 15.27
	Bulk silicon	15.78	15.78 ~ 15.97

Conclusion

The detailed physical description of the considered MPSC has been reported in this chapter. Different sensing elements of the multi-sensing device and their corresponding fabrication steps are discussed elaborately. A comprehensive pictorial illustration of the fabrication procedures involved in creating multiple resistive sensing devices on a single chip has also been demonstrated. Unification of different micro-sensors on the same chip is a challenging task in the perspective of manufacturing technology and the transduction fundamentals of each sensing component. Identical working principle (resistance variation) facilitates the coexistence of the assorted sensors. On the other hand, geometric optimization allows the cointegration of several realized sensors without degrading their performance. Prior to obtaining effectively functional co-integrated prototype of the multi-parameter sensor chip; several steps have been performed associated with the physical structure modification. The sensor that has been realized to determine the water pressure strongly influenced the co-integration issue. As a consequence, it has been included in this part with preliminarily obtained experimental measurements. The conductivity sensor has been introduced afterwards, where the different versions of the sensor were highlighted with their corporeal description and the measured responses under different operation conditions. The remaining sensors (flow-rate and temperature) will be discussed in the subsequent chapters comprehensively. They are sorted based on their operation principle since both of them are realized based on thermal transduction. The similarity of the flow-rate and the temperature sensor lies in their sensing elements. We have to carefully elaborate on those two sensors since thermal effects are involved in both with potentially critical impacts on the whole constituents of the MPSC. A thin-film resistor is used to determine two different parameters-water flow and temperature. Therefore, before introducing these two sensors, a chapter (Chapter 3) is dedicated to Resistance Temperature Devices (RTD), where the diverse use of a resistive element has been emphasized.

Pressure Sensor

The electrical resistance as a temperature sensor and multi-purpose building block for sensors based on Heat transfer

Resistance variation as a function of temperature enables RTD use in manifold purposes such as flowrate sensing and thermal conductivity detection (TCD) for gas chromatography (GC). In what follows, we introduce first two important applications of RTDs as a building block of two quite different sensors: (i) a flow-rate sensor and (ii) a thermal conductivity detector (TCD).

Application of RTD as Flow-rate sensor

As we will see more in detail in the next chapter, the flow-rate sensor of the MPSC is a thermo-resistive flow-rate sensor. Its working principle is based on heat transfer between the device and the operation medium. Different versions of the sensor have been fabricated in order to study different parameters. The early versions of the sensor contain three resistors to enable different operating modes: calorimetric, anemometric and time-of-flight. The latest versions contain only a single resistor and are optimized for anemometric operating modes. The resistors are made of platinum and fabricated on silicon or glass substrates. A detailed discussion of the resistors arrangement and their usefulness to different operation principles will be presented later. All resistors are identical. Each one of them is an RTD. Water flow is determined according to the cooling magnitude of the heating resistor. The resistor is heated by a constant current supply. Under a fluid flow, heat is transferred from the heater to the surrounding medium. The temperature of the resistor decreases gradually as the fluid velocity increases. The cooling magnitude provides information on the fluid velocity and flowrate. In the case of a temperature sensor, a low supply current is preferable in order to avoid Joule self-heating which is obviously not the case for the flow-rate 133 1 Appendix for Chapter 2

Annex 1-Pressure sensor behavioral simulation

The behavior of the pressure sensors was evaluated beforehand through numerical simulations based on finite element modeling (FEM). These simulations include the first set of structural simulations so as to evaluate the strain field when the membrane is deflected by a uniform pressure. Then coupled simulations, including both structural and piezoresistive, led on one hand, to an appropriate choice for the membrane lateral dimensions and thickness and, on the other hand, to the optimum placing of the resistors and appropriate choice of their dimensions. respectively. This resistance value depends on the used substrate material. Besides the Cp and Rs, there are 135 two high-value resistances at the two ends of the model Rcp1 and Rcp2. These two resistances are also parasitic resistance and they do not have any influence on the system. They are required for the simulation.

The current source is not the part of this model; hence it is an external power source.

The numerical values of different parts of the model were determined by mathematical calculations and to some extent by assumption. The capacitors are calculated using the conventional formula stated below:

Here, 𝜀 dielectric constant, A is the area and d is the distance between the electrodes. In order to calculate the mutual capacitance between the electrodes, the Eqn-2 is used, which is the formula of two parallel coplanar strip.

Where, 𝑙 is the length of the strips and 𝐾(𝑥) is the elliptical integral of 𝑥.

Frequency response

The frequency response analysis was performed in order to find out the effective operating frequency range, hence the linearity of the sensor. The same electronic model presented in Figure Apn-2 was used for the analysis. In the simulation, the water resistance value Rw was varied from 500 Ω to 5 MΩ with a step of 1 kΩ. The result is presented here only for the sensor 3. 136

The simulation was performed using PSpice. The flat region expresses the linear frequency range of the sensor, where it has to be operated. There are two kinds of the port on the DAQ board, where the one type is dedicated to input and the other for output. The input ports are for reading the data from the electronic circuit or from the sensor's reading port which is considered as an input to the DAQ board. On the other hand, the output ports are assigned to carry out the output or power from the DAQ board to the desired load. Both the output and input ports are programme controlled. LabView programme is used to visualize the input/output from the DAQ board. The programme that was used to characterize the conductivity sensor is provided below.

Annex 3-Conductivity

It can be observed from Figure Apn-5 that, the programme window contains different graph plots with some controlling switches. Each part has its own functionality. The region marked with 'A' is the inputregulating part of the programme. In this part, the intensity of the input current can be regulated along with frequency and sampling rate of the current source. The unit under 'B' is to display the output of the sensor.

The first graph plot displays the output before filtering and the middle one display after filtering. In the programme, there is a numerical filter in order to filter out the parasitic noise. The last plot under 'B' shows 137 the FFT peak. Indeed, the raw signals from the sensor pass through a spectral measurement process before filtering. The region 'C' is the linear regression unit, where the sensor's responses were measured with respect to the different conductivity level.

Tools used for Anti-fouling test:

Different tools were used in order to conduct the measurement of the biofilm growth on the surface of the considered sensor chips. The detail of those devices are provided below:

Equipment:

Luminometer EnSURE from hygiena model V.

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