Asef Ghabeli
email: asef.ghabeli@savba.sk

Enric Pardo
email: enric.pardo@savba.sk

Mark Ainslie
email: mark.ainslie@eng.cam.ac.uk

Loïc Quéval
email: loic.queval@geeps.centralesupelec.fr

Modeling HTS dynamo-type flux pumps: open-circuit mode and charge of an HTS coil

Keywords: Dynamo-type flux pump, open-circuit DC voltage, full-circuit mode, REBCO high temperatuere superconductor

Dynamo-type flux pumps can be employed to energize high-temperature superconducting (HTS) magnets to maintain the steady current mode or in rotor windings of electrical HTS motors and generators to inject the current without the need for current leads. To understand the complicated operating working mechanism of the flux pump it is necessary to study both in opencircuit and connected to an HTS coil. In this work, we employ two models to analyze these configurations. The first one is the Minimum Electromagnetic Entropy Production (MEMEP). The second one is a segregated H-formulation finite-element model. For open-circuit mode, we investigate the effect of the airgap on DC voltage generation. Later, we also study the energizing process of an HTS coil modeled by a series resistance and an inductance. These models will also be compared to experiments.

I. INTRODUCTION

HTS dynamo-type flux pumps belong to the category of the traveling magnetic wave type, which uses a moving magnet to create an inhomogeneous traveling magnetic flux across an HTS tape in order to generate voltage. These kinds of flux pumps are simple and easy to maintain because they do not utilize any switches. For this reason, they have become very popular during recent years to be exploited in HTS magnets, motors and generators [START_REF] Walsh | Characterization of current stability in an hts nmr system energized by an hts flux pump[END_REF]- [START_REF] Bumby | Development of a brushless hts exciter for a 10 kw hts synchronous generator[END_REF]. Dynamo-type flux pumps can be employed to energize HTS magnets to maintain the steady current mode or in rotor windings of electrical HTS motors or generators to inject the current without using any brushes in a contactless way.

Among the different types of flux pumps, the dynamo-type is one of the most complicated ones in terms of understanding its mechanism. Modeling has showed to be an effective way to explain the flux pump mechanism. For this reason, several articles have been published in literature to explain the dynamo-type flux pump mechanism [START_REF] Campbell | A finite element calculation of flux pumping[END_REF]- [START_REF] Ghabeli | Modeling of airgap influence on DC voltage generation in a dynamo-type flux pump[END_REF].

This work is funded from the Slovak VEGA agency, under contract No. 2/0097/18.

To investigate the flux pump mechanism, studying both open-circuit and full-circuit mode is essential. The open-circuit mode is simpler compared to full-circuit mode considering the elimination of the effect of a net current flowing in the HTS circuit. In this work, we utilize mainly the Minimum Electromagnetic Entropy Production (MEMEP) [START_REF] Pardo | Electromagnetic modelling of superconductors with a smooth current-voltage relation: variational principle and coils from a few turns to large magnets[END_REF], [START_REF] Pardo | 3d computation of non-linear eddy currents: Variational method and superconducting cubic bulk[END_REF] to model a dynamo-type flux pump with realistic superconductor properties (E -J power-law relation with J c (B, θ) from critical-current measurements of single tapes) [START_REF] Ghabeli | Modeling of airgap influence on DC voltage generation in a dynamo-type flux pump[END_REF]. This variational method solves the local current density as the state variable, only inside the superconducting region, with minimization of a functional, which makes the model fast and efficient. In addition, the results are compared with another one implemented in COMSOL based on a segregated Hformulation finite-element model [START_REF] Quéval | Superconducting magnetic bearings simulation using an h-formulation finite element model[END_REF]. This model solves the magnetic field strength H only inside the superconductor and a thin air layer around it, which allows us to reduce the number of degrees of freedom and the computing time.

This work investigates the effect of airgap on DC voltage generation in a dynamo-type flux pump in open-circuit mode.

In addition, we study the charging procedure by the dynamotype flux pump of an inductance and resistance in series, representing a typical REBCO coil with joint resistance.

II. AIRGAP DEPENDENCE OF OPEN-CIRCUIT VOLTAGE

Fig. 1 shows an overview of the modeling configuration. The flux pump rotates in the xy-plane and the magnetic field B exist in the same plane. The current density J flows in the z direction. Therefore, the vector potential A and electric field E are parallel to the z axis.

A useful and practical quantity measured experimentally is the output voltage difference between the tape at 77 K (superconducting mode) and at 300 K (normal mode), ∆V = V 77K -V 300K . This quantity ∆V contains the DC component of the open-circuit voltage, ∆V oc . The amplitude of ∆V oc decreases with increasing airgap, which is the result of the reduction in magnetic field density on the tape surface. Fig. 2 shows the calculated results of reduction of ∆V oc for the modeled flux pump from 2.4 mm up to 50 mm. By calculating the ∆V oc signals in Fig. 2 for the airgaps ranging from 2.4 to 50 mm, the stationary average DC voltage per cycle is

III. CHARGING PROCESS OF HTS COIL

Studying the full-circuit mode with transport current is essential to understand the flux pump mechanism that energizes superconducting windings. Fig. 4 shows the circuit diagram of the flux pump in open-circuit and connected to an HTS coil, respectively. In Fig. 4(left) the net transport current is zero, and hence the generated voltage by the flux pump V F P is equal to V oc as calculated in the previous section. However, in the full circuit of Fig. 4(right), the output voltage for the flux pump is related to the transport current by

V F P (I(t), t) = RI(t) + L coil dI(t) dt (1)
where R denotes the joint resistance and L coil is the load inductance. Solving the non-linear differential equation above requires numerical methods. Using similar arguments as in [START_REF] Li | Coupling loss at the end connections of rebco stacks: 2d modelling and measurement[END_REF], the full circuit mode in MEMEP can be solved by minimizing the functional in [START_REF] Pardo | Electromagnetic modelling of superconductors with a smooth current-voltage relation: variational principle and coils from a few turns to large magnets[END_REF] for the current density at each time step with the additional term RI 2 /2+L coil (∆I) 2 /2, where ∆I and ∆t are the time and current increments between two time steps, and ∆I is found during minimization. For the segragated H-formulation model, ( 1) is solved together with the H-formulation flux pump model using a bidirectional circuit coupling scheme. Modeling results will also be compared to experiments.

Fig. 1 .

 1 Fig. 1. This 2D cross-sectional model configuration is sufficient to describe the main features of a dynamo-type flux pump.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Calculated results showing the trend in the reduction of ∆Voc with increasing airgap up to 50 mm for the case with Jc(B, θ) dependence and rotating frequency of 12.3 Hz. (∆Voc as calulated in [7])..

Fig. 4 .

 4 Fig. 4. Circuit diagram of a dynamo-type flux pump in open-circuit mode (left) and in full-circuit mode (right). Voc is the open-circuit voltage and R denotes the joint resistance. L coil denotes the load inductance.


Rafael Coelho-Medeiros

Loic Queval

Jean-Claude Vannier

Jing Dai

Philippe Egrot

Modeling of high-temperature superconducting pancake coils using the axisymmetric partial element equivalent circuit method

Keywords:

The partial element equivalent circuit (PEEC) method has great potential to solve electromagnetic problems coupled with an electric circuit. Such capability is useful for modeling the integration of superconducting devices with power electronics converters. In this article we implement an axisymmetric PEEC model of pancake coils made of high-temperature superconducting (HTS) coated tapes fed by an arbitrary voltage source.

I. INTRODUCTION

Superconducting devices are expected to operate into complex environments. For example, superconducting fault current limiters (ScFCL) have already been successfully integrated into high-voltage grids with voltages ratings going from 10.5 kV to 220 kV [START_REF] Hobl | Superconducting fault current limiters -A new tool for the "Grid of the future[END_REF], [START_REF] Moyzykh | First Russian 220 kV superconducting fault current limiter for application in city grid[END_REF]. In such environments, the superconducting device may interact with power transformers, transmission lines, circuit breakers, power electronic converters, and electric machines. Other examples of possible future integration of superconducting devices in complex environments can be found in the literature. In [START_REF] Queval | Feasibility Study of a Superconducting Power Filter for HVDC grids[END_REF], the author proposes the integration of a superconducting power filter for HVDC grids, [START_REF] Trillaud | Superconducting Power Filter for Aircraft Electric DC Grids[END_REF] presents the integration of such filter into an aircraft electric DC Grid and in [START_REF] Schmitt | Fault current limiters report on the activities of CIGRE WG A3.16[END_REF] possible applications of ScFCLs in transmission and distribution systems are studied. From a modeling perspective, the main challenge is to model the superconducting device coupled to an electrical circuit.

The partial element equivalent circuit (PEEC) method [START_REF] Ruehli | Circuit Oriented Electromagnetic Modeling Using the PEEC Techniques[END_REF] is an electric circuit oriented approach to model electromagnetic problems based on the integral form of Maxwell's equations. It is well suited to model devices within electrical circuits and it has been widely used for problems having a small number of degrees of freedom due to its good performance in computation time compared to finite element (FE) methods.

However, the disadvantages of the PEEC method start to appears for medium and large numerical problems since the circuit matrices are dense due to inductive and capacitive coupling [START_REF] Ruehli | Circuit Oriented Electromagnetic Modeling Using the PEEC Techniques[END_REF]. Thus, using the classical PEEC rectangular cells [START_REF] Ruehli | Circuit Oriented Electromagnetic Modeling Using the PEEC Techniques[END_REF] to model pancake coils would generate a rather large system of equations. To overcome this problem, we use the PEEC method with axisymmetric cells with rectangular crosssections.

The article is organized as follows. In Section II, we derive the axisymmetric PEEC method and we explain its implementation for high-temperature superconducting pancake coils using an EJB constitutive law [START_REF] Kim | Critical persistent currents in hard superconductors[END_REF]. In Section III, we calculate current distribution and losses in AC and DC operation for an 8-turn HTS pancake coil and compare them with a FE model.

II. AXISYMETRIC PEEC

The full content of this section will be integrated in the final version of this article.

III. SIMULATION RESULTS FOR AN 8-TURN PANCAKE COIL

We consider an 8-turn pancake coil wound with 1G powderin-tube BSCCO tape of 4.5 mm width and 0.36 mm thickness. We model the BSCCO region and approximate it by a rectangular layer of 4.5 mm width and 45 µm thickness divided into 50 current cells. The central layer of BSSCO is coated on both sides by a rectangular silver layer of 4.5 mm width and 90 µm thickness followed by a rectangular copper layer of 4.5 mm width and 45 µm thickness, each one of these layers is modeled by a single current cell. The geometric parameters of the application are listed in Table I and its electrical parameters are listed in Table II.

The PEEC model of this arrangement leads to a 372-th order system of ODEs solved using the built-in ode23s solver of MATLAB. Here we assume that the HTS pancake coil is fed by an arbitrary voltage source V s (t).

To study current distribution on the HTS pancake coil in AC and DC, let us consider the coil to be fed by a DC voltage source V s (t) = 0.142 and an AC voltage source V s (t) = 0.47 cos(2π50t). Both voltages sources will generate a peak current of 120 A (Fig. 1). The resulting normalized current densities of the pancake coil in DC and AC are respectively plotted in Figs. 2 and3. Notice that the space between layers is intentionally reduced plotting. The full content of this section will be integrated in the final version of this article.

IV. CONCLUSION

The full content of this section be integrated in the final version of this article.

Fig. 1 .

 1 Fig. 1. Coil's current for voltage source excitation in AC and DC.

Fig. 2 .Fig. 3 .

 23 Fig. 2. BSSCO layer normalized current density for DC with 120 A peak at t = 0.0638 s.


Loïc Quéval
email: loic.queval@centralesupelec.fr

Wenjiao Yang

Kun Liu

Guangtong Ma
email: gtma@swjtu.edu.cn

Modeling of the non-uniform J c distribution along the c-axis of seeded melt growth (RE)BCO bulks

Keywords: HTS bulk, critical current density, levitation force

RE)BCO bulks prepared by seeded melt growth process have a non-uniform local critical current density distribution along the c-axis. To clarify its impact, we measured the levitation force of such bulk above a permanent magnet in two configurations: bottom seed and top seed. This is only by considering a non-uniform J c distribution, that we were able to reproduce both measurements with an H-formulation finite element model.

I. INTRODUCTION

High temperature superconductors (HTS) have the potential to trap or shield large magnetic fields at 77 K. This is particularly attractive for a variety of high field permanent magnet applications, including electrical machines and magnetic bearings. In an attempt to fabricate practical materials, (RE)-Ba-Cu-0 bulks ((RE)BCO where RE = rare earth element) have been developed by melt process techniques. The seeding requirement for the controlled fabrication of these material [START_REF] Cardwell | Processing and properties of large grain (RE)BCO[END_REF] is the root of several distinct features of seeded melt growth (RE)BCO bulks.

One of them is the formation of growth sector boundaries (GSB) and growth sector regions (GSR). It has been reported that J c is higher along the GSBs than in the GSRs. For example, Fujishiro et al. characterized the inhomogeneity of a GdBCO bulk by measuring the magnetization of small samples cut from various positions in the bulk [START_REF] Fujishiro | Direct Jc measurements and trapped field profiles using identical superconducting bulks[END_REF]. They showed that J c is 20 % higher below the seed crystal, compared with that on the growth sector regions (GSR). It is also slightly enhanced on the growth sector boundaries (GSB). This non-uniformity of J c in the ab-plane can be modeled by a cosine function [START_REF] Ainslie | Modelling of bulk superconductor magnetization[END_REF]. In addition, J c also varies as a function of the position relative to the seed. It has been reported that J c is lower "at the edge with respect to the center and at the bottom with respect to the top" [START_REF] Ainslie | Modelling of bulk superconductor magnetization[END_REF]. This might be linked to the mechanism of the crystal growth of bulk superconductors [START_REF] Cardwell | Processing and properties of large grain (RE)BCO[END_REF], [START_REF] Krabbes | RE)BaCuO/Ag composites: fundamentals of processing, microstructure and control of properties[END_REF]. This non-uniformity of the local critical current density J c can play a significant role. For example it affects the maximum temperature rise, and the magnitude and shape of the trapped field profile during pulse field magnetization [START_REF] Fujishiro | Direct Jc measurements and trapped field profiles using identical superconducting bulks[END_REF], [START_REF] Ainslie | Modelling of bulk superconductor magnetization[END_REF]. We show here that it can affect the levitation performance as well.

To do so, we report and discuss the measurement and modeling of the levitation force of a (RE)BCO bulk above a PM for the two configurations shown in Fig. 1. In the bottom seed (BS) configuration, the seed is on the bottom surface of the bulk, near the PM. In the top seed (TS) configuration, the seed is on the top surface of the bulk, opposite to the PM.

II. MEASUREMENT

The experimental setup used to measure the levitation force has been described in [START_REF] Quéval | Superconducting magnetic bearings simulation using an H-formulation finite element model[END_REF]. The permanent magnet (PM) is a cylindrical Nd-Fe-B PM having a remanent magnetic flux density of 1.27 T. The bulk is a cylindrical single domain seeded melt growth YBCO bulk.

The levitation force is measured for a zero field cooling (ZFC) sequence: the bulk is cooled down at a distance of 100 mm above the center of the PM (the field is negligible); then the bulk is moved at a speed of 1 mm/s, first vertically downward until the gap between the bulk and the PM is 5 mm, then vertically upward to its initial position.

The measured levitation forces are plot in Fig. 2. The maximal levitation force measured in BS configuration is 30 % higher than the one measure in TS configuration. This result can only be explained by the non-uniformity of J c between the top and the bottom side of the bulk, ie. along the c-axis.

III. MODELING

The 2D axisymmetric finite element (FE) model used here to reproduce the levitation force experiment has been described in [START_REF] Quéval | Superconducting magnetic bearings simulation using an H-formulation finite element model[END_REF]. The idea behind it is to decouple the problems. First we calculate the magnetic field generated by the PM magnet using an A-formulation (magnetostatic) FE model. Second we calculated the induced current in the HTS domain with an Hformulation FE model. The relative movement between the PM and the HTS is modeled by time-dependent Dirichlet boundary conditions mimicking the motion. We underline that this model has been extensively validated.

For the HTS domain, the resistivity is modeled using an E-J power law model (E c = 10 -4 V/m, n = 21 [START_REF] Ainslie | Modelling of bulk superconductor magnetization[END_REF]). The dependence of the critical current density J c on the magnetic field B is described using an isotropic Kim-like model,

J c (B) = J c0 1 + |B| B 0 (1)
where J c0 is the local self-field critical current density to be determined, and B 0 is a material parameter set to 0.37 T [START_REF] Quéval | Superconducting magnetic bearings simulation using an H-formulation finite element model[END_REF].

A. Uniform J c model First we assume that J c0 is uniform in the bulk. To obtain its value, it is common practice to consider the ZFC sequence in BS configuration, and to identify it so that the simulated maximum levitation force is equal to the measured value. Accordingly, J c0 is set here at 2.4 • 10 8 A/m 2 . The simulated levitation force for both configuration have been added to Fig. 2. The model with uniform J c overestimates the force in the TS configuration.

B. Non-uniform J c model

Second we assume that J c0 is non-uniform in the bulk. For example, one can assume that it is a linear function of the distance to the seed along the c-axis,

J c0 (z) = J c0,S (1 -c 0 |z -z S |) (2)
where J c0,S is the local critical current density at the seed location (0,z S ) and c 0 describes the degradation of J c with the distance from the seed in the c-axis direction. Alternatively, one can assume that J c0 is a cubic function of the distance to the seed along the c-axis,

J c0 (z) = J c0,S 1 -c 0 |z -z S | 2 (3)
The corresponding simulated levitation forces have been added to Fig. 2. Now the agreement between the measurements and the model is good both for the BS and TS configurations. Note that the simulated hysteretic loop is slightly too large for the TS configuration. This suggests that other local properties of the bulk influence the levitation force.

IV. CONCLUSION

The non-uniform local critical current density should be taken into account when designing systems using seeded melt growth (RE)BCO bulks. Indeed, in an optimal design, the bulk would tend to be fully penetrated by supercurrents. In that situation, the performance of the real bulk would probably by lower than the one estimated using an uniform J c .

Figure 1 .

 1 Figure 1. (left) BS configuration, (right) TS configuration. The arrow indicates the PM magnetization direction.

  (a) BS configuration (b) TS configuration

Figure 2 .

 2 Figure 2. Levitation force for the ZFC sequence


Izadora Mayumi

Fujinami Tanimoto

Benjamin Cressiot

Nathalie Jarroux

Jean Roman

Gilles Patriarche

Bruno Le Pioufle
email: bruno.le-pioufle@ens-paris-saclay.fr

Juan Pelta
email: juan.pelta@univ-evry.fr

Laurent Bacri
email: laurent.bacri@univ-evry.fr

Selective Target Protein Detection Using a Decorated Nanopore into a Microfluidic Device

Keywords:

Solid-state nanopores provide a powerful tool to electrically analyze nanoparticles and biomolecules at single-molecule resolution. These biosensors need to have a controlled surface to provide information about the analyte. Specific detection remains limited due to nonspecific interactions between the molecules and the nanopore. Here, a polymer surface modification to passivate the membrane is performed. This functionalization improves nanopore stability and ionic conduction. Moreover, one can control the nanopore diameter and the specific interactions between protein and pore surface. The effect of ionic strength and pH are probed. Which enables control of the electroosmotic driving force and dynamics. Furthermore, a study of polymer chain structure and permeability in the pore are carried out. The nanopore chip is integrated into a microfluidic device to ease its handling. Finally, a discussion of an ionic conductance model through a permeable crown along the nanopore surface is elucidated. The proof of concept is demonstrated by the capture of free streptavidin by the biotins grafted into the nanopore. In the future, this approach could be used for virus diagnostic, nanoparticle or biomarker sensing.

Introduction

Over the past two decades, nanopore technologies have gathered interest for their potential to address challenges in health (Arima et al., 2018a;Wang et al., 2011), energy (Balme et al., 2017;Bétermier et al., 2020;Huang and Hsu, 2020), and biotechnology (Howorka and Siwy, 2020;Ouldali et al., 2020;Stoloff and Wanunu, 2013). This technology is an electrical sensing technique that detects a single molecule or particle as it passes through the diameter of a nanometer-scale pore. This event corresponds with a specific decrease in ionic current compared to that of the empty pore. Protein channels currently provide the best sensitivity and selectivity (Cressiot et al., 2019;Fahie et al., 2016;Jain et al., 2018;Ramarao and Sanchis, 2013;Wang et al., 2018;Ying and Long, 2019). Moreover, these recombinant nanopores produce highly reproducible data due to their well-defined structure and geometry. Ultra-fast DNA sequencing is one of the most advanced applications of this technology (Branton et al., 2008;Derrington et al., 2010;Jain et al., 2018;Kasianowicz et al., 1996) and recent results pave the way to nanopore protein sequencing (Cressiot et al., 2020;Giamblanco et al., 2020;Ouldali et al., 2020;Restrepo-Pérez et al., 2018). However, certain variables must be considered when using this technology. Protein nanopores are limited to a few nanometers in diameter determined by the specific protein channel used. They are sensitive to the environment, e.g. pressure, organic solvents or chaotropic agents. The lipid bilayer that supports the channel is also fragile. A recent study showed that the membrane fragility could be improved using a chip-based platform capable of sustaining high voltages (Kang et al., 2019;Nardin et al., 2000). With the introduction of material science, new classes of artificial nanopores have been designed (Ma et al., 2020). So-called solid-state nanopores allow for the analysis of DNA conformation (Li et al., 2003;Skinner et al., 2009), protein size (Giamblanco et al., 2018;Wei et al., 2012;Yusko et al., 2017) and conformation (Bacri et al., 2011;Freedman et al., 2013;Waduge et al., 2017), nanoparticles (Bacri et al., 2011) and viruses (Arima et al., 2018b(Arima et al., , 2018a)). These biomimetic nanopores are stable in different solvents and environments. They are easy to handle and to be produced on a larger scale. Their nanometric diameter can also be tuned to fit the analyte size. There are two types of solid-state nanopores. The first one is the track-etched nanopore drilled in a micrometer thick membrane, leading to the design of specific sensors (Ali et al., 2008;Giamblanco et al., 2020;Lepoitevin et al., 2017Lepoitevin et al., , 2016;;Lin et al., 2020;Ma et al., 2020Ma et al., , 2019)). The second one is drilled in tens of nanometer-thick membranes (Kowalczyk et al., 2011;Li et al., 2001;Roman et al., 2018Roman et al., , 2017;;Zhang et al., 2020).

While solid-state nanopores present better mechanical stability than lipid bilayers (Roman et al., 2017), low reproducibility, short lifetime (attributed to high surface energy) (Niedzwiecki et al., 2010;Oukhaled et al., 2011) and, lower sensitivity to detect small species are the major limitations of this technology. There is a need to overcome these limitations. The first strategy is the design of hybrid nanopores to take advantages of both the sensitivity and reproducibility of biological nanopores and, the robustness of solid-state nanopores. In this case, a protein channel is inserted into a thin solid-state membrane (Bentin et al., 2020;Cressiot et al., 2018;Hall et al., 2010) or a polymer membrane (Cabello-Aguilar et al., 2013). The second strategy involves coating the nanopores with a fluid lipid bilayer (Venkatesan et al., 2011;Yusko et al., 2017Yusko et al., , 2011)). Another one is to control the surface chemistry of the solid-state nanopore by membrane surface functionalization, which allows to increase their lifetime, to control the pore size and to passivate the membrane (Arima et al., 2018b(Arima et al., , 2018a;;Awasthi et al., 2020;Giamblanco et al., 2018;Lepoitevin et al., 2016;Li et al., 2019;Malekian et al., 2018;Roman et al., 2018Roman et al., , 2017;;Wanunu and Meller, 2007). Nevertheless, specific sensing remains a challenge to design new diagnostic tools (Li et al., 2019). Biomimetic nuclear pores have shown the detection of proteins (Kowalczyk et al., 2011;Malekian et al., 2018). Peptide functionalized nanopores are also used to detect influenza virion (Arima et al., 2018a). A similar strategy allowed pulsed amperometric detection of bovine leukemia virus glycoproteins [START_REF] Ramanaviciene | Biosensors and Bioelectronics[END_REF] the immunosensing of bovine leukemia virus (Ramanavicius et al., 2014). These works show the sensitivity of electrical sensing to the functionalization of the electrode surface. In our work, the surface decoration is performed on the nanopore, which becomes a nanoscale probe. Several questions remain open: what is the influence of salt concentration on the polymer size, conformation and mesh size of chains grafted to the pore surface? Is electroosmotic or electrophoretic flow the driving force for the ions and protein dynamics? What is the relation between the polymer layer thickness and ionic permeability? Is it possible to control the protein transport via a specific capture?

Here, we focus on detecting the specific capture and transport of proteins through a nanopore (Fig. 1). The nanopore chip is inserted into a microfluidic device (Fig. 1a, Fig. S1) to facilitate its handling and ease the buffer exchanges. (Roman et al., 2018) The nanopore (Fig. 1b) is functionalized with polymer chains to increase its stability. The polymers are biotinylated to enhance specific single-particle capture and detection of streptavidin (Fig. 1e). The grafting steps are followed and characterized by electrical measurements (Fig. 1c-d), leading to a structural analysis of the functionalized layer. Consequently, we study the transport dynamics through the decorated nanopores. We show how to control this transport according to ionic strength and pH.

Materials and methods

Buffer preparation

Sodium chloride (NaCl, Merck, Germany) solutions ranging from 0.1 M to 1 M were prepared at pH 7.4 with 25 mM tris(hydroxymethyl) aminomethane (Tris, Merck, Germany) and pH 6 with 10 mM sodium dihydrogen phosphate monohydrate (NaH2PO4, Avocado, U.K.). The final pH was adjusted with HCl and NaOH solutions using a pH meter (WTWTM pH meter inoLabTM 720, Xylem Analytics, Germany).

Nanopore fabrication

All silicon nitride membranes were purchased from Nanopore Solutions ® , Portugal. The SiNx chips are 3 mm large. They are made of a 200 µm thick silicon substrate, covered by a 20 nm thin Si3N4 layer. The substrate is etched in its center to obtain a 15 x 15 µm 2 free-standing Si3N4 membrane. The nanopores were drilled with a transmission electron microscope (TEM, Titan Themis) operated at an accelerating voltage of 200 kV according to the standard protocol demonstrated by Storm et al., 2003. In which, by changing the focused electron beam diameter it is possible to control the nanopore size. The beam size can be determined by the full width at half-maximum (FWHM) of the Gaussian intensity profile in the transverse focal plane. In this work, the nanopores are ranging from 20 to 35 nm with a precision lower than 1 nm. Immediately after drilling, the nanopore image obtained by TEM is used to infer its radius (R0) (Fig. S2).

Nanopore functionalization

Polyethylene glycol (PEG) chains were grafted over the surface of the nanopore chip in a three-step protocol. First, the chip was exposed to a UV/Ozone cleaner (Jelight Company Inc., U.S.A.) for 15 min per face to generate hydroxyl groups (-OH), activating the surface of the Si3N4 membrane. Then, to cover all the active sites of the membrane surface with the (3-chloropropyl) trichlorosilane (CPTS) chains, each face of the chip was incubated for 1 hour at 100 rpm (Rotamax 120, Heidolph Instruments GmbH & CO, Germany) in a precursor bath of 2.5 mM CPTS (Merck, Germany) in acetone. The final bath, composed of PEG in anhydrous tetrahydrofuran (THF), is applied for 1 hour per face over an orbital shaker. In this step, we use a) only 2.5 mM solution of long 7 kDa PEG (Agilent technologies, U.S.A.) chains, that has been previously described (Roman et al., 2017), or b) a mixture with 1.25 mM of short 500 Da mPEG (Merck, Germany) and 1.25 mM of long 7 kDa PEG chains. The abbreviation mPEG designates a monomethyl-ether (-OCH3) terminated PEG. The treated chips were then rinsed with milli-Q filtered water (Millipore) to remove the non-grafted PEG chains off the membrane. They were stocked in ultra-pure water for further utilization.

The biotin was grafted on the end of the 7 kDa PEG chains (-OH group) through esterification (Gravert and Janda, 1997). The chip was immersed 1 hour per side over an orbital shaker in a dimethyl sulfoxide (DMSO, VWR Chemicals, U.S.A.) solution, containing 3.12 mM biotin (Merck, Germany), 3.12 mM N,N'-Dicyclohexylcarbodiimide (DCC, Merck, Germany) and 0.312 mM 4-pyrrolidinopyridine (Fluka TM analytical, Germany). Then, another rinsing cycle with milli-Q water was performed to remove the non-grafted biotin off the membrane. Functionalized nanopores were stored in ultra-pure water in the refrigerator.

The protein, atto 594-Streptavidin, was purchased from Merck, Germany.

Microfluidic device

The microfluidic device fabrication process has been previously described (Roman et al., 2018). The final device is made of two identical sub-units that form the cis and trans chambers between which the nanopore chip is inserted. Briefly, PolyDiMethylSiloxane (PDMS, Sylgard 184, Dow Corning, U.S.A.) with a 10:1 base to curing agent ratio was used thanks to the conventional soft lithography technology (McDonald and Whitesides, 2002). The mold was 3D-printed (use of an extrusion 3D printer) in PET (polyethylene terephthalate) using a silicon wafer as a substrate. First, the PDMS was poured over the 3D-printed mold to make the channel, and again over a bare wafer to close the channel. Both of them were cured at 75°C for 1h30. The two parts were sealed together to form one sub-unit by activating the surfaces by 30 sec of low energy air plasma exposure (500-700 mTorr, 15 W) from a plasma cleaner (Harrick Plasma, U.S.A). The activated surfaces were then covalently bound after heating at 75°C for 20 min. Fluidic access to the channels was made at this step using a biopsy puncher. The two sub-units prepared, as explained above, were then partially bound together. The upper half remains unbound for nanopore chip insertion. Fluidic access tubes were inserted in the pierced holes to act as inlet/outlet of the cis and trans channels. Finally, the apertures are made through the thin layer in between the two sub-units where the chip can be placed to allow the solutions to reach the nanopore.

Electric current measurement

The microfluidic device was filed up with the electrolyte solution. Then, Ag/AgCl electrodes were directly inserted in the cis (ground) and trans (working electrode) channels of the microfluidic device to measure the ionic current passing through the nanopore. The current was amplified with an Axopatch 200B (Molecular devices, U.S.A.). A PCIe-6251 board (National Instruments, U.S.A.) interfaced with a custom .Net based program was used for data acquisition. Recorded currents were analysed by IGOR Pro (WaveMetrics, U.S.A.). The experiments were carried out in different nanopores (N = 8). The I-V curves were obtained by applying 60 s long voltage steps ∆V = 20 mV varying from -200 mV to +200 mV at 1 ksamples/s and filtered with a Bessel two poles low pass filter at 10 kHz. Resistive pulse sensing was performed by applying 120 s voltage steps at 150 samples/s and filtered at 10 kHz. The final set-up, comprising of the microfluidic device, the nanopore membrane and the amplifier headstage, was placed inside a Faraday cage to avoid electromagnetic interferences. All the measurements were carried out at room temperature and they were repeated 3 times to obtain an averaged value. Thus, for a given concentration of NaCl, we measured the ionic current at least 63 times. Therefore, if we consider the 3 functionalization steps and the 4 different concentrations of NaCl, an average of 756 measurements per nanopore chip was performed.

Results and discussions

In this study, we focus on developing a device able to detect a specific target molecule, streptavidin, at a singlemolecule scale. To that end, biotin was grafted at the free end of each long PEG chain (7 kDa PEG), to lead to a biotinstreptavidin complex (Fig. 2), one of the strongest non-covalent interactions known in nature (Weber et al., 1989). The PEG functionalization aims to passivate the membrane, to avoid nonspecific interactions (e.g. experiments with BSA Fig. S3), and to increase the experiment lifetime from a few hours to several months (Fig. S4) (Giamblanco et al., 2018;Roman et al., 2017). Moreover, the nanopore's apparent diameter can be controlled according to the polymer chain size (Roman et al., 2018). These functionalization steps are followed at the nanometric scale by electrical measurements. After the first PEG-based grafting, ion current is measured according to voltage to obtain the modified nanopore's current-voltage (I-V) characteristics (Fig. 3a). The linear shape of these curves shows that the driving force does not modify the polymer chain's conformation in response to the applied voltage (Giamblanco et al., 2018;Roman et al., 2018Roman et al., , 2017)). The conductance (G) is calculated from the I-V curve slope. G increases with increasing sodium chloride (NaCl) concentration, between 0.1 and 1 M.

After nanopore biotinylation as described in Fig. 2, the I-V responses of the nanopore are measured according to ionic strength (0.1 M ≤ [NaCl] ≤ 1 M) (Fig. 3b). Here, we observe a slight asymmetry in the shape of these I-V curves.

Fig. 3c shows an increase in both the ionic current amplitude and the curve asymmetry between the negative and positive voltage in the presence of streptavidin. We notice that the nanopore conductance also rises with increasing NaCl concentration. At a constant concentration of 1 M NaCl (Fig. 3d), we compare the I-V curves measured after PEG functionalization, biotin grafting and streptavidin addition with the values deduced from the calculated conductance of the bare nanopore (see Equation 1). First, we observe a strong decrease in the ionic current from the original bare pore to the PEG grafted pore (Fig. 3d, black vs green). These findings are in good agreement with the presence of a polymer layer covering the inner nanopore (Giamblanco et al., 2018;Kowalczyk et al., 2011;Roman et al., 2017). Then, we observe an increase in conductance after biotin functionalization and streptavidin addition. These results are unexpected and will be discussed below.

Fig. 3e shows the variation of the nanopore conductance according to the NaCl concentration. This conductance is positively related to NaCl concentration (i.e. the conductance increases with rising salt concentration between 0.1 M and 1 M) as expected in a regime dominated by the ionic transport. This behavior is described by the relation (Balme et al., 2015;Roman et al., 2018):

𝐺 𝑖𝑜𝑛𝑖𝑐 = 𝐾 𝑏𝑢𝑙𝑘 𝐿 0 𝜋𝑅 0 2 + 1 4𝑅 0 (1)
where 𝐾 𝑏𝑢𝑙𝑘 = (µ + + µ -) ⋅ 𝑒 2 ⋅ 𝑐 is the bulk conductivity, L0 is the length of the nanopore, R0 is its radius, 𝑒 is the elementary charge, 𝑐 is the ion concentration (in ions per 𝑚 3 ), µ ± = 𝐷 ± /(𝑘 𝐵 𝑇) is the bulk ion mobility that is related by the Einstein relation to the ionic diffusion coefficients (D), the Boltzmann constant 𝑘 𝐵 and temperature T.

Here, the measurements were done using a NaCl electrolyte with different salt concentrations. Hence, 𝐷 𝑁𝑎 + = 1.334 × 10 -9 𝑚 2 𝑠 -1 and 𝐷 𝐶𝑙 -= 2.032 × 10 -9 𝑚 2 𝑠 -1 at 25°C. The 1/(4𝑅 0 ) term takes into account the access resistance near the pore entrance on either side of the membrane (Hall, 1975).

In Fig. 3e, we observe a strong decrease of the conductance due to a decrease in the apparent radius of the nanopore following 7 kDa PEG based functionalization. From which, we compute the functionalized apparent nanopore radius R'PEG = 8.03 ± 0.02 nm, smaller than that of the bare nanopore R0 = 13.25 nm as observed by TEM. The decrease of 5.22 ± 0.02 nm is attributed to the PEG layer (Giamblanco et al., 2018;Kowalczyk et al., 2011;Roman et al., 2017). Assuming that the PEG layer thickness (h) is the relevant parameter to characterize the grafted chain conformation whatever the nanopore size and; that the grafting is the same on the membrane and inside the pore, we can calculate the PEG layer thickness. The apparent radius R' and length L' of the nanopore, after membrane functionalization will become:

R'= R0 -h

(2)

L' = L0 + 2 h (3)
R0 and L0 are the initial radius and length of the bare pore, and h is the layer thickness. Thus, from Equation 2 and 3 the Equation 1 can be transformed

𝐺′ 𝑖𝑜𝑛𝑖𝑐 = (µ + +µ -) ⋅ 𝑒 2 ⋅ 𝑐 𝐿 0 +2 ℎ 𝜋(𝑅 0 -ℎ) 2 + 1 4 (𝑅 0 -ℎ) (4)
By applying several nanopores (R0 = 10.2 nm, 14 nm, 13.25 nm, 13.65 nm) grafted by 7 kDa PEG in multiple electrolyte concentrations to the equation above, we obtain an apparent PEG thickness of h7 kDa = 5.1 ± 0.7 nm (Fig. 3f).

Nevertheless, the active sites between PEG chains, hydrolyzed in the presence of water, could lead to nonspecific chemical bonds. To rule out this possibility, we used a mixture composed of 7 kDa PEG/500 Da mPEG 1:1 to facilitate the grafting of small 500 Da mPEG chains between each large 7 kDa PEG chain. This grafting, like with 7 kDa PEG chains alone, leads to a decrease of the nanopore conductance (Fig. S5a). Following the same reasoning, we calculate the apparent thickness of the 7 kDa PEG/500 Da mPEG grafting layer hmix = 5.5 ± 1.5 nm. This result shows that the 500 Da mPEG addition does not influence the 7 kDa PEG chain conformation (Fig. S5b).

After biotinylation of the 7 kDa PEG chains, the conductance linearly increases along with the NaCl concentration (Fig. 3e). Using Equation 4, the fit leads to an apparent thickness of hBiotin = 4.03 ± 0.16 nm (Fig. 3f) for the pore with biotinylated 7 kDa PEG chains. This value is thinner than the 5.1 ± 0.7 nm thickness of the PEG layer before biotinylation.

Considering Equation 4, we fit the curves of Fig. 3e to compute the apparent thickness of the layer composed of the 7 kDa PEG -Biotin -Streptavidin complex, which brings the grafting thickness (hStreptavidin) down to about 3.26 ± 0.03 nm (Fig. 3f). Similar to what was observed after biotinylation, Fig. 3e-f show, once again, a decrease in thickness of the functionalization layer after streptavidin addition.

These results suggest that the conformation of the PEG chains is modified after biotinylation and streptavidin addition. In ideal solvent conditions, grafted PEG chains have a mushroom-like. The magnitude of the layer thickness is similar to N 0.5 a = 4.4 nm, where N is the number of monomeric units, and a is the unit length (N = 159, a = 0.35 nm for 7 kDa PEG) (de Gennes, 1980). This calculation Step by step nanopore functionalization characterization from their ionic conductance according to the ionic strength. a) I-V curves after 7 kDa PEG functionalization according to the ionic strength. b) I-V curves of different NaCl concentrations after biotin grafting. c) I-V curves after adding the streptavidin into the cis channel. d) I-V curves in 1 M NaCl in a bare pore (black circles, theoretical calculation), after PEG functionalization (green squares), after biotin grafting (blue triangles) and after streptavidin addition into the cis channel (red pentagons). e) Conductance versus NaCl concentration after PEG functionalization (green squares), biotin functionalization (blue triangles) and after adding the streptavidin (red pentagons) in the cis channel, at pH 7.4. An increase of the ionic conductance after each functionalization step is observed. Solid lines are calculated from Equation 4. The error bars are computed from three different measurements using the same parameters. f) Grafting thickness after PEG functionalization (green), after biotin grafting (blue) and after streptavidin addition into the cis channel (red). The nanopore size is R0 = 13.25 nm, drilled in a SiNx membrane (thickness 20 nm), 0.1 M ≤ [NaCl] ≤ 1 M, 25 mM Tris, pH 7.4.

agrees with the conductance-based nanopore characterization we performed, about 5.1 ± 0.7 nm (see above), and with previous works (Giamblanco et al., 2018;Roman et al., 2017). Our observation agrees with the assumption that the polymers could reduce the nanopore diameter. Then, the number of PEG chains grafted on the inner side of the nanopore could be estimated. If we consider a 20 nm large nanopore, its perimeter is equal to 63 nm. As the PEG size is equal to 5.1 nm, the number of PEG chains along this circumference is estimated to 12. Given the membrane is also 20 nm thick, there is only space for 4 layers of PEG leading to 48 PEG chains grafted along the nanopore.

However, the apparent layer thickness surprisingly decreases about 2.11 ± 1.07 nm, an averaged value for several pores, after biotinylation (hPEG > hBiotin). This decrease was an unexpected result because we presumed that the biotin would be grafted within the inner surface of the pore, thus increasing its apparent thickness. A possible interpretation of this result is that the biotin grafted at the end of the 7 kDa PEG chains, could be more attracted to the SiN surface than the solvent, consequently disrupting the conformation of polymer chains. We assumed that the dipole moment of the biotin molecule is attracted to the negative surface of the SiN membrane and could slightly modify the PEG structure. Indeed, Bajgiran et al. (Bajgiran et al., 2020) demonstrated that a biotinylated nanoparticle generates a positive dipole moment and could be used to detect avidin particles. Similarly, a decrease of the apparent thickness was observed when the streptavidin molecules were added (hPEG > hBiotin > h'Streptavidin). This was possible due to the formation of biotin-streptavidin complex, in which there could be a strong interaction between the negatively charged SiN surface and the apparent positive charge of the biotin at pH 7.4. These increases in the apparent radius of the nanopore, with the successive functionalization steps, is confirmed in Fig. 3d-e. We found that the conductance increases after each functionalization step whatever the ionic strength between 0.1 M and 1 M NaCl. We verified that even with the increase in ionic current, the apparent diameter did not exceed the diameter of the bare pore (Fig. 3d-e, black circles), showing that no pore erosion occurred during the experiments.

We suggest the following model to explain these observations. Based on Dekker and colleagues' theory (Ananth et al., 2018), we assume that the structure of the functionalized nanopore is not homogeneous in term of ionic conductivity and is described with three areas: i) the SiN area considered as an insulator, which conductivity is taken to zero (r = R0); ii) the functionalized layer with a crown shape (R' < r < R0), characterized by a nonzero conductivity (Kpore); iii) the central region where there is no polymer chain and which conductivity is equal to the bulk one (Kbulk)(see above).

The pore conductivity Kpore is obtained by integration of the local ionic conductivity K(r) in the nanopore (Ananth et al., 2018):

𝐾 𝑝𝑜𝑟𝑒 = 1 𝜋𝑅 0 2 ∫ 2𝜋 𝑟 𝐾(𝑟) 𝑑𝑟 𝑅 0 𝑟=0 (5)
where

K(r) = Kbulk if 0 ≤ r ≤ R', K(r) = Kcrown if R'≤ r < R0.
Then, assuming that the access conductivity Kaccess is equal to Kbulk, Equation 1 reads:

F 𝐺 = 1 𝐿 0 +2 (𝑅 0 -𝑅′) 𝜋[𝐾 𝑐𝑟𝑜𝑤𝑛 𝑅 0 2 +(𝐾 𝑏𝑢𝑙𝑘 -𝐾 𝑐𝑟𝑜𝑤𝑛 ) R′ 2 ] + 1 4 𝐾 𝑏𝑢𝑙𝑘 𝑅 0 (6)
We could now define the layer permeability as P = Kcrown/Kbulk and, considering the layer thickness h, the previous relation reads:

𝐺(ℎ, 𝑃) = 𝐾 𝑏𝑢𝑙𝑘 𝐿 0 +2 ℎ 𝜋[𝑃 𝑅 0 2 +(1-𝑃) (𝑅 0 -ℎ) 2 ] + 1 4 𝑅 0 (7)
The pore conductance (G) and the pore size (L0, R0) are considered as constant to graphically solve this relation. In Fig. S6, Equation 7 is plotted by varying both parameters h and P together according to the conductance and the size of three nanopores (R0 = 10.1 nm, R0 = 10.3 nm and R0 = 13.25 nm). The intersection of these curves leads to the determination of the thickness (h) and the permeability (P) of the functionalized layer after biotin grafting and after streptavidin addition according to the ionic strength (Fig. 4a-b). We could do three observations. Firstly, the permeability and the thickness of the layer do not vary with the ionic strength, between 0.1 M and 1 M. Secondly, the low magnitude of the biotinylated layer permeability (9 ± 2 %) indicate that the biotin grafting slightly modifies the PEG conformation. Nevertheless, the high magnitude of the permeability after the streptavidin addition (70 ± 3 %) suggests that the streptavidin addition strongly modifies the PEG structure. Thirdly, the biotin grafting leads to a thickness decrease of about 1 nm, confirming biotinylation does not significantly alter the PEG conformation. However, the streptavidin addition leads to a consistent increase of the thickness until 8.5 ± 0.2 nm. These results show that the streptavidin molecules interact with the PEG layer leading to a strong change of the chain structure (Fig. 4c). The streptavidin can be bound to four biotins at most. Considering that there are more than 48 biotinylated 7 kDa PEG chains in the 20 nm large nanopore, we assume that each streptavidin is bound to at least two biotins. Which leads to a polymer network reticulated by the streptavidin. This observation is similar to the one previously performed in track-etched nanopores (Lepoitevin et al., 2016). It shows that the biotinylated nanopore can specifically capture streptavidin (around 48/4 = 12 streptavidin molecules in the 20 nm large nanopore). The concentration of streptavidin being 18.8 nM, we can assume that the majority of the streptavidin is free in solution.

An important issue is whether these functionalized nanopores can perform streptavidin sensing at the singlemolecule level. First, we do not really measure current blockades with the 20.1 nm, 20.4 nm and 20.5 nm large nanopore (Fig. S7). Considering the model we use, whereas the functionalized layer is 8.5 nm thick, the central part would be only 3 nm large. Therefore, streptavidin could not enter this central part because this dimension is smaller than the size of streptavidin and the functionalized layer is too dense to allow streptavidin transport (Fig. 4c). Hence, we doubt that a streptavidin molecule has enough space to enter the nanopore. However, it could be stuck in the nanopore entrance. Subsequently, we use the 26 nm large nanopore, which is characterized by a 9 nm large central part, larger than the size of streptavidin. However, the blockade frequency is still relatively low (Fig. S8) when a negative voltage is applied by the electrode in the transside compartment, and the streptavidin is added in the cisside. We did not detect any blockades when we applied a positive bias according to previous works (Yusko et al., 2011;Zhang et al., 2020). Given the isoelectric point (pI) of the streptavidin is 5.5, this protein is negatively charged at pH 7.5 (Firnkes et al., 2010;Sivasankar et al., 1998). Then, the electrophoretic force (EPF) should not move these proteins from the cis to the trans side. Nevertheless, the SiN based surface is negatively charged at pH 7.5 (Firnkes et al., 2010), leading to an electroosmotic flow (EOF), which can move streptavidin from the cis to the trans side. The long blockade duration we observe could be attributed to the strong electrostatic repulsion between the captured streptavidin in the PEG-biotin layers and the free ones in solution. This repulsion could lead to an increase in the energy barrier of streptavidin confinement through the functionalized nanopore.

To prevent these electrostatic repulsions, i.e. neutralize the protein charge and decrease the streptavidin confinement energy, we decreased the pH from 7.4 to 6 (Firnkes et al., 2010). Moreover, we decreased the apparent surface charge of the SiN surface (Zeta potential ζ decreases from -20 mV at pH 7.5 to -10 mV at pH 6) (Firnkes et al., 2010) effectively reducing the EOF, which should govern the dynamics through the nanopore (Zhang et al., 2020) (Fig. S9). Now, we study the structure modification of the layer at pH 6. We measure the conductance measurements at pH 6 of 30 nm and 35 nm large nanopore: 28.3 ± 1.7 nS at 0.3 M NaCl, 12.6 ± 0.96 nS at 0.1 M NaCl, respectively. Following the method described above, we determine the new values of layer thickness h and permeability P. The increase of the layer thickness (h = 13.5 ± 1.7 nm) and the decrease of its permeability (P = 0.55 ± 0.06) show a modification of its structure due to the pH decrease (Fig. S10).

We performed this experiment using a 35 nm diameter nanopore functionalized with biotinylated 7 kDa PEG in the presence of 500 Da mPEG at pH 6 and 0.1 M NaCl. After streptavidin addition, we observe several blockades at -180 mV applied voltage with a 2.34 ± 0.06 blockades/s rate and short duration events (< 100 µs) (Fig. 5a). If we increase the electrolyte concentration up to 1 M, we observe a decrease of the blockade rate (< 1 blockades/s) and the apparition of very long blockades (>> 10 ms) (Fig. 5b-c). We measured the ionic conductance (Fig. S11) at different salt concentrations. We show the ionic current measurements before streptavidin addition in the supporting file (Fig. S12).

We continue with statistical analysis of each blockade, characterized by its duration Td, its blockade current Ib and the inter-event time between two following blockades Ti (Fig. S13). The Fig. 5d-f shows the results of this analysis according to the Debye length (κ -1 = √ 𝜀 0 𝜀 𝑟 𝑘 𝐵 𝑇 2 𝑐 𝑒 2 , where ε0 is the vacuum permeability, εr the relative permittivity). Which describes the screening length of electrostatic interactions. We observe that the blockade rate increases with the Debye length. Two kinds of blockade durations are measured: the short ones (below 2 ms) and the long ones (until 90 ms). Nevertheless, these blockades are too short to be discriminated at 0.1 M NaCl. The long and short duration times increase as the ionic strength increases.

Consequently, we attempted to understand the physical mechanism involved in the functionalized nanopore after streptavidin addition. From the description of protein transport by Firnkes et al., the effective velocity Veff of a protein reads:

𝑉 𝑒𝑓𝑓 = 𝜀 0 𝜀 𝑟 𝜂 𝐸 (𝜁 𝑝𝑟𝑜𝑡𝑒𝑖𝑛 -𝜁 𝑛𝑎𝑛𝑜𝑝𝑜𝑟𝑒 ) (8)
where E the electric field, η the solution viscosity. ζprotein and ζnanopore are the zeta potential of protein and the nanopore, respectively. At pH 6, the streptavidin could be considered as neutral (|ζprotein|<<| ζnanopore|), this equation becomes:

𝑉 𝑒𝑓𝑓 = 𝜀 0 𝜀 𝑟 𝜂 𝐸 𝜁 𝑛𝑎𝑛𝑜𝑝𝑜𝑟𝑒 (9)
Because the zeta potential of the nanopore is negative and increases with the ionic strength, we assume the absolute value of this zeta potential increases with the Debye length. Then, Equation 9 leads to an increase of the effective velocity magnitude of the streptavidin with κ -1 , which defines the thickness of the counterion flow in the nanopore. In other words, the electroosmotic flow increases with the Debye length. This hypothesis is congruent with the increase of the blockade rate with κ -1 (Fig. 5d). As the blockade duration could read Td ≃ ℓ / Veff where ℓ = 20 nm is the membrane thickness, the experimental measurements confirm the expected variations of Td (Fig. 5e -f).

Finally, the rebounds of streptavidin failing to enter the nanopore could explain the short blockade durations. The long ones could be explained by the streptavidin going through the functionalized layer, which provides for a partially permeable physical barrier.

Conclusion

We designed a robust device in which we specifically control the chemistry of solid-state nanopore surfaces, the driving force, the dynamics and the capture of a target protein. It is composed of a nanopore chip inserted in a microfluidic circuit to handle fluids. This device is stable for several days and reusable. It allows ionic current measurements at different salt concentrations and pH to perform sensing through this nano-channel. This technology is suitable to follow chemical grafting steps at the nanometer scale. The strategy to obtain a selective target protein detection is validated by a hybrid decoration of a nanopore with small polymer chains to avoid nonspecific interactions (Fig. S3 and Fig. S14), and with large ones terminated with biotin groups to increase the specific ones. We consider a model based on the permeability of the functionalized layer in which we monitor the ionic current evolution for all experimental conditions to precisely characterize its structure. This new conformation improves the lifetime of the nanopore (Fig. S15).

The pH variations allowed us to modify the charge of the protein and of the membrane to control the protein transport through the pore at the single-molecule level. Blockade rates and durations depend on the Debye length, which shows that the electroosmotic flow dominates transport dynamics. The proof of concept was done using the biotin-streptavidin complex. This device is a biomimetic model for specific interactions involved in biology such as antigen-antibody system. It paves the way for the design of a new generation of diagnostics or for applications based on an active decorated nanopore with singleparticle resolution.

Figure 1 .

 1 Figure 1. Protein specific capture. a) Schematic of the microfluidic device with a nanopore chip placed on it (left); Sketch of a PEG functionalized nanopore drilled into a SiNx based membrane (right). b) High-resolution transmission electron microscopy (HRTEM) picture of a bare 20 nm thick SiNx nanopore, R0 = 14 nm. c) A representative sequence of current measurements used to obtain a current vs voltage (I-V) plot; each trace corresponds to a voltage ranging from -200 mV to +200 mV. d) I-V curve. SiNx nanopore, R0 = 14 nm, L0 = 20 nm, grafted by 7 kDa PEG, 0.1 M NaCl, 25 mM Tris, pH 7.4. e) Schematic of the possible forces acting within the nanopore environment under a negative applied voltage. EOF is the electroosmotic flow, and EPF is the electrophoretic force.

Figure 2 .

 2 Figure 2. Chemical pathway of the nanopore functionalization. a) Surface passivation and diameter control. The membrane was functionalized with a polymer of 1:1 stoichiometry between 7 kDa PEG and 500 Da mPEG. b) Specific detection mechanism. To specifically capture a target molecule, a bioreceptor, biotin, was grafted at the end of the 7 kDa PEG. On the bottom right a top view of the nanopore system is depicted.

Figure

  Figure 3.Step by step nanopore functionalization characterization from their ionic conductance according to the ionic strength. a) I-V curves after 7 kDa PEG functionalization according to the ionic strength. b) I-V curves of different NaCl concentrations after biotin grafting. c) I-V curves after adding the streptavidin into the cis channel. d) I-V curves in 1 M NaCl in a bare pore (black circles, theoretical calculation), after PEG functionalization (green squares), after biotin grafting (blue triangles) and after streptavidin addition into the cis channel (red pentagons). e) Conductance versus NaCl concentration after PEG functionalization (green squares), biotin functionalization (blue triangles) and after adding the streptavidin (red pentagons) in the cis channel, at pH 7.4. An increase of the ionic conductance after each functionalization step is observed. Solid lines are calculated from Equation4. The error bars are computed from three different measurements using the same parameters. f) Grafting thickness after PEG functionalization (green), after biotin grafting (blue) and after streptavidin addition into the cis channel (red). The nanopore size is R0 = 13.25 nm, drilled in a SiNx membrane (thickness 20 nm), 0.1 M ≤ [NaCl] ≤ 1 M, 25 mM Tris, pH 7.4.

Figure 4 .

 4 Figure 4. Permeability and thickness of the layers according to ionic strength. a) Permeability of the layer after the biotin grafting (P = 0.09 ± 2, red squares), after streptavidin addition (P = 0.7 ± 0.03, blue triangles) b) Thickness of the layer after the biotin grafting (h = 5.1 ± 0.2 nm, red squares), after streptavidin addition (h = 8.5 ± 0.2 nm, blue triangles). The dotted lines correspond to the average values of each data. 0.1 M ≤ [NaCl ≤ 1 M], 25 mM Tris, pH 7.4. c) Structure of a functionalized 20 nm large nanopore after streptavidin addition.

Figure 5 .

 5 Figure 5. Single-molecule sensing of streptavidin. a-b) Ionic current blockades in the presence of 18.8 nM streptavidin in the cis chamber, ΔV = -180 mV, 0.1 M NaCl and 1 M NaCl, respectively. c) Focus on one current blockade at 1 M NaCl, ΔV = -180 mV, Td is the dwell time, and ΔI = Ib -I0 is the current blockade. d) Blockade frequency vs. Debye length (0.1 M ≤ [NaCl] ≤ 1 M). e) Short dwell time vs Debye length. f) Long dwell time vs Debye length. All experiments were performed with a R0 = 17.5 nm nanopore initial radius functionalized with 7 kDa PEG and 500 Da mPEG, and grafted with biotin, 10 mM NaH2PO4 at pH 6.

Acknowledgements

We acknowledge financial support from DIM Respore (PhD grants), DEFI Instrumentation aux limites 2016/2017, ANR Epsilomics (17-CE09-0044-02). We thank John T. Gonzalez for careful reading and improving the use of English in the manuscript.

Declaration of competing interest

The authors declare that they have no known competing financial interests of personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Supplementary data

Supplementary data to this article can be found online at https://doi.org/ Microfluidic integration; TEM images; Conductance of a nanopore decorated with 7 kDa PEG and 500 Da mPEG at pH 6 in the presence of BSA; Evolution of the ionic conductance of a nanopore decorated with 7 kDa PEG and 500 Da mPEG at pH 6; Ionic conductance after each functionalization step and polymer thickness; Relation between polymer thickness and permeability; Small nanopore decorated with 7 kDa PEG and 500 Da mPEG at pH 7.4; Large nanopore decorated with 7 kDa PEG at pH 7.4; Driving forces acting on the nanopore environment; Structure modification of the PEG layer at pH 6; Ionic conductance of large nanopore decorated with 7 kDa PEG and 500 Da mPEG at pH 6; Ionic conductance of large nanopore decorated with 7 kDa PEG and 500 Da mPEG at pH 6 after biotin functionalization; Nanopore decorated with 7 kDa PEG and 500 Da mPEG at pH 6; Conductance of a nanopore decorated with 7 kDa PEG and 500 Da mPEG and biotin-streptavidin complex at pH 6 in the presence of BSA; Evolution of the ionic conductance of a nanopore decorated with 7 kDa PEG and 500 Da mPEG and biotin-streptavidin complex at pH 6.


Sarra Houidi
email: sarra.houidi@etu.univ-nantes.fr

Franc ¸ois Auger
email: francois.auger@univ-nantes.fr

Philippe Frétaud
email: philippe.fretaud@univ-nantes.fr

Dominique Fourer
email: dominique.fourer@univ-evry.fr

Laurence Miègeville
email: laurence.miegeville@univ-nantes.fr

Houda Ben
email: houda.benattia@enicarthage.rnu.tn

Attia Sethom

Design of an electricity consumption measurement system for Non Intrusive Load Monitoring

Keywords: Intrusive Load Monitoring (NILM), Home Electrical Appliances (HEAs), data acquisition, Arduino microcontroller, sampling frequency

Non Intrusive Load Monitoring (NILM) of Home Electrical Appliances (HEAs) has received a major interest in the last twenty years due to the increase of electricity consumption in the residential sector. One NILM key component is the acquistion of voltage and current signals at a high sampling rate, to ease HEAs recognition. In this paper, we discuss the design of a cheap and easy to install current and voltage measurement system based on an Arduino MKR Zero microcontroller. This system allows to monitor the usage of HEAs and thus give to consumers information on their consumption and help them to manage it. The hardware architecture of the proposed system is first described. It includes the current and voltage conditioning circuits. Then, a detail of the software implemented in the microcontroller is given. It consists mainly of an interruption routine that ensures the acquisition regularity and a background task for storing data into a micro SD card. Finally, we show in the last section several individual HEAs power consumption profiles and a whole house power consumption profile obtained using the designed acquisition device.

I. INTRODUCTION

Direct feedback on electricity consumption in the residential sector helps the consumer to get real-time information down to the HEA level [START_REF] Tiefenbeck | Realtime feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives[END_REF]. It has proved to induce considerable behaviour changes and substantial energy savings: almost 12% of reduction in the total residential energy consumption [START_REF] Tiefenbeck | Realtime feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives[END_REF], [START_REF] Kelly | Does disaggregated electricity feedback reduce domestic electricity consumption? a systematic review of the literature[END_REF]. As an indicator, if broadly implemented in United States, real-time feedback could provide the equivalent of 100 billion kilowatt-hours of electricity savings annually by 2030 [START_REF] Tiefenbeck | Realtime feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives[END_REF]. Supervised Non Intrusive Load Monitoring (NILM) has recently emerged as a promising solution to provide a breakdown of the residential energy consumption without instrumenting all Home Electrical Appliances (HEAs) [START_REF] Zeifman | Non intrusive appliance load monitoring: Review and outlook[END_REF], [START_REF] Abubakar | Recent approaches and applications of non-intrusive load monitoring[END_REF]. It involves several steps detailed in Fig. 1 [START_REF] Zeifman | Non intrusive appliance load monitoring: Review and outlook[END_REF], [START_REF] Hart | Nonintrusive appliance load monitoring[END_REF], [START_REF] Tabatabaei | Toward non-intrusive load monitoring via multi-label classification[END_REF]. The first step consists of acquiring voltage and current measurements at the Point of Common Coupling (PCC). In the second step, electrical features such as active and reactive powers and/or their harmonics are extracted from voltage and current measurements. The next step involves the detection of events in the aggregated data due to one HEA switching On or Off (or changing state). The fourth step is the identification of HEAs in the load curve through pattern recognition based machine learning techniques which require prior knowledge about HEAs and imply the establishment beforehand of a HEAs database [START_REF] Tabatabaei | Toward non-intrusive load monitoring via multi-label classification[END_REF]. This database consists of HEAs that are described with electrical features derived from the voltage and current measurements acquired with the acquisition system. Finally, an estimation of each HEA contribution in the load curve is achieved. These informations should be directly fed back to the consumers, so that they can make an informed choice about saving energy and hence reducing their electricity bills. This paper focuses on the upstream of NILM methods: the design and development of a low cost and and easy to install metering unit for real-time current and voltage acquisition. The paper is organized as follows: Section II presents a state of the art of the acquisition systems commercially available, as well as the ones designed for academical and research purposes. This state of the art allows to set up a number of requirements for the design of the developed monitoring device and to stress the characteristics of the system that are innovative in comparison with what is available. Section III introduces the proposed acquisition system by first giving a general overview of its architecture before describing in detail the hardware architecture including the current and voltage conditionning stages, and the software which mainly includes and interrupt service routine implemented in an Arduino MKR Zero microcontroller for real-time acquisition and data storage. The sampling rate selection will be detailed in addition to a data compression technique that allows to store more measurements in the microcontroller's RAM and then in the micro SD card. Section IV shows real acquistions obtained using the proposed prototype: individual HEAs and a whole house power consumption profiles. Finally, Section V is devoted to the final conclusions and pointers to research directions and improvements.

II. STATE OF THE ART

The role of a data acquisition system is to obtain current and voltage measurements at an appropriate rate so that distinctive HEAs' signatures can be identified in the load curve [START_REF] Najmeddine | State of art on load monitoring methods[END_REF]. Line voltages and currents are sensed and supplied to a microcontroller to allow data logging and storage. One of the main concern for the developpement of such a device is the sampling rate that somehow "governs" its cost. Indeed, it determines the type of information that can be extracted from the electrical signals as well as the ability of the device to support long-time measurements [START_REF] Depuru | Smart meters for power grid challenges, issues, advantages and status[END_REF]. For instance, to capture the higher order harmonics of the electrical signals, which are integer multiples of the fundamental frequency, the sampling rate of the meter must satisfy the Shannon sampling theorem. To capture transient events or electrical noises generated by the electrical signals, frequencies from the kilohertz range are needed [START_REF] Zeifman | Non intrusive appliance load monitoring: Review and outlook[END_REF], [START_REF] Najmeddine | State of art on load monitoring methods[END_REF]. Conversely, a low sampling frequency allows to store an important duration of measurements. Recently, the world of the industry as well as researchers have taken a close interest in manufacturing these devices, taking account these considerations. In what follows, we present some of the industrial marketed products before presenting some of the prototypes set up by researchers for experimental evaluation. We aim at highlighting the main requirements for our own prototype design.

A. Industrial marketed products

While not flooding the market, energy monitoring products are now more and more widespread. They aim at providing the needed feedback to consumers by monitoring the whole-house energy consumption. We are interested into products that are as close as possible to what we aim at designing. Thus, products such as " The Energy Detective 5000 (TED 5000)" [START_REF]The energy detective[END_REF], "Wattseeker by Qualisteo" [START_REF] Depuru | System and method to monitor and manage performance of appliances[END_REF], [START_REF]Qualisteo wattseeker solution[END_REF], "Eyedro" [START_REF]Eyedro[END_REF], "Neurio" [START_REF]Neurio[END_REF] and "Sense Home Energy Monitor" [START_REF]Sense[END_REF] are concerned. They combine a hardware device (hereinafter referred as a "Measurement Transmission Unit (MTU)") and a web-based analytical tool. The hardware device is installed in the electric panel with Current Transducers (CTs) clamped around the phases of the power supply cable in addition to a voltage transducer. For some products, professionals are needed for the installation. The voltage and current measurements are transmitted to a local PC or a remote server (generally a cloudbased server) through an Ethernet gateway, Wi-Fi or Power Line Communication (PLC) technology. Table I presents the main characteristics of these products. It is to be noted that the approximated prices don't take the costs of the installation into account when required.

B. Research prototypes

Few non-commercial metering devices were developped as prototypes by the NILM research community. For most of the researchers, the design of these prototypes is motivated by limitations, especially in terms of sampling rate and cost, found in available commercial solutions. For instance, K. Gomez et al. introduce in [START_REF] Gomez | Energino: a hardware and software solution for energy consumption monitoring[END_REF] "Energino" a novel hardware and software solution for realtime energy consumption monitoring in wireless networks. It consists of an hardware and an Arduino platform-based software. A management backend written in Python is used to gather the energy consumption statistics. The hardware stage integrates a voltage sensor (based on a voltage divider) and a current sensor (based on the Hall effect). The Arduino continuously polls the voltage and the current sensors and accumulates the values into two separate registers. At the end of the polling period the average power consumption is computed and the result is sent through an USB to an interface. Another solution for the real-time energy management in a house is presented in [START_REF] Aurilio | A low cost smart meter network for a smart utility[END_REF]. It consists of several slave STM32 microcontrollers with their voltage and current transducers, connected to HEAs and which communicate via PLC with a master data aggregator. The slave microcontrollers are programmed to acquire the voltage and current with two simultaneous 12 bits resolution analog to digital converters at a 10 kHz sampling frequency. Through an implemented web server, the users can remotely control their consumption using a web browser. Based on this literature, we have been able to set the criteria that make our prototype an efficient alternative. The criteria consist of:

• a sampling rate that allows to extract harmonics up to the 15 th range (higher than 1.5 kHz according to Shannon theorem) and at the same time, that supports long-time measurements, • a good resolution, • a low cost and a low power device, cheap to produce and to deploy, The Energy Detective (TED) Eyedro Green Solutions Neurio Sense Qualisteo

• an autonomous device that can operate as a standalone monitor, with a dedicated power supply, • an easy installation. In what follows, the main components of the proposed current and voltage acquisition prototype are presented.

III. ARCHITECTURE OF THE PROPOSED ACQUISITION

SYSTEM

This section is devoted to the description of the hardware and software part of the proposed current and voltage measurement device. We first give a general overview of our prototype architecture.

A. Acquisition system overview

The acquisition system that we set up, includes the key components of an acquisition and data processing chain. The general architecture of the system can be seen in Figure 2(a) and a photo of the real acquisition device is shown in Figure 2(b). The whole system is spread over three boards, so the sensors or the microcontroller can easily be changed. In what follows, the hardware subsystem and the software implemented in the Arduino MKR Zero microcontoller are described in detail. The current and voltage measurement board includes a Chauvin Arnoux E3N probe for current sensing and a LEM LV25-P for voltage sensing, whose output quantities are adapted to the voltage range (0-3.3V) expected by the Analog to Digital Conversion (ADC) included in the Arduino MKR Zero microcontroller through conditioning circuits. This board allows the measurement of a consumption which is almost equal to 6 kW. This corresponds to the maximum power allowed by a usual dwelling contract from the French electricity provider. The microcontroller is devoted to the ADC and to data sampling and storage in a micro SD card. The entire system is plugged into a nearby power outlet that delivers 230 V AC. This is connected to a power supply circuit that incorporates a 2×12V-5VA transformer whose output is given to a bridge rectifier that converts the AC to DC, and delivers the voltage to a filter circuit that smoothes out the ripples and passes it to two voltage regulators IC 7812 and IC 7912, which regulate the output voltage at +12V and -12V DC, respectively. This supply board allows to hold the output voltage at ±12 V necessary for the LEM LV5-P voltage sensor and the Arduino microcontroller that is supplied with 5 V using an IC 7805 voltage regulator. Protection against overcurrent and short circuits is provided by fuses. This solution remains low cost as shown in Table II which gives a simplified bill of the main components of the proposed prototype when considering wholesale prices.

B. Hardware: current and voltage sensing and conditioning stage

In order to shape the signals measured by the current and voltage sensors for the ADC, both signals are conditioned. The outputs of both sensors have to be scaled for 0-3.3V which represents the voltage range expected by the Arduino MKR Zero ADC. This is done through electrical conditioning circuits which are detailed in what follows.

1) Current conditioning: Fig. 3 shows the current conditioning circuit. A Chauvin Arnoux E3N current probe is used to measure the current on one of the domestic network phases. This allows to measure the load current without opening the network to insert an ammeter. A voltage V iI = s i is delivered by the probe, where i denotes the load current and s is the probe sensitivity. This voltage constitutes the input of a non-inverting opamp circuit (using an OP27 op. amp.) including the resistors R I1 and R I4 . The output voltage

V B = 1 + R I1
R I4 V iI is applied to a voltage divider made of resistors R I2 and R I3 . When the current i + is negligible, then:

V B -V oI R I3 + V ref -V oI R I2 = 0 A (1)
R I2 and R I3 values are equal so that V oI = V ref /2 when i = 0 A. A voltage-follower-type opamp circuit is then used to constitute an impedance adaptation stage. The output voltage V o I is hence equal to:

V oI = R I3 R I2 + R I3 V ref + R I2 R I2 + R I3 1 + R I1 R I4 s i (2)
In order to get an output voltage scaled for 0-3.3V and a maximal active power close to 6 kW, the probe sensitivity is set at s = 10 mV/A and the non-inverting opamp circuit as well as the voltage divider have been designed using standard resistor values such as R I2 = R I3 = R I4 = 1 kΩ and R I1 = 8.25 kΩ. Such values allow to measure a maximal peak value of the current equal to i max ≈ 35.67 A which corresponds to an effective value of i rms ≈ 25.23 A (when V oI = 3.3 V), and thus a maximal value of the active power equal to P max = 230 i max / √ 2 ≈ 5802 W. Besides, since the Arduino board supports input channels using a 10bit ADC, it maps input voltages between 0 V and 3.3 V to integer values between 0 and 1023. Thus, the resolution is q V = 3.3 2 10 V per unit. This allows to get a current granularity of q I = q V s 1+R I3 /R I2 1+R I1 /R I4 ≈ 69 mA which leads to an active power granularity of q P = q I √ 2 230≈11.3 W, enough to catch dynamics such as low-energy lamp switch on.

2) Voltage conditioning: The voltage measurement is realized thanks to a LEM LV 25-P voltage sensor, which is a closed loop current transformer using Hall effect sensor with galvanic isolation. It allows a voltage measurement up to 500 V. Fig. 4 shows the voltage conditioning circuit. According to Fig. 3. Current conditioning circuit the datasheet of this voltage sensor [START_REF]LEM LV-25 P voltage transducer datasheet[END_REF], the maximum primary current i pmax is chosen equal to 10 mA. For this maximum to be obtained when the input voltage is equal to

V iNmax = 230 √ 2 V, we chose R U 1 = V iN max ipmax = 230 √ 2
10 10 -3 ≈ 32.5 kΩ. This value can be approximated using 3 resistors of 100 kΩ in parallel. Since the conversion ratio is of 2.5 [START_REF]LEM LV-25 P voltage transducer datasheet[END_REF], the maximum secondary current equals i smax = 2.5 V iN max R U 1 = 24.4 mA can be translated to voltage by the measurement resistor R M between the output and ground. The value of the resistor R M should range between 30 Ω and 190 Ω [START_REF]LEM LV-25 P voltage transducer datasheet[END_REF]. In order to scale the measured voltage so that [-230 √ 2; 230 √ 2] V translates to [0; 3.3] V, two opamp voltage follower circuits and a resistive voltage divider are used to obtain V oN . R U 2 and R U 3 are both fixed to 1 kΩ so that V oN = V ref 2 when V iN = 0 V. The output voltage V oN is then given by

V oN = R U 3 R U 2 + R U 3 V ref + R U 2 R U 2 + R U 3 2.5× R M R U 1 V iN (3) = V ref 2 + 1.25 R M R U 1 V iN (4)
Choosing R M = 130 Ω leads to a maximum value of the output voltage equal to V oNmax = 3.17 V, which is lower than 3.3 V, the maximum voltage allowed by the ADC. One of the most interesting features of the proposed prototype is the implementation of the resistors on integrated circuit supports, which allows to easily change the power level when needed.

C. Software: analog to digital conversion, sampling and data storage

The analog current and voltage signals provided by the measurement board are digitized by an Arduino MKR Zero microcontroller (32 bit processor SAMD21 Cortex-M0 microcontroller, 48 MHz), programmed using the C/C++ programming language, and including ADC channels with 8/10/12 bit resolution. The Arduino MKR Zero microcontroller was chosen for its low cost and performances [START_REF]Arduino MKR zero technical specifications[END_REF]. Moreover, it integrates a micro-SD card shield and a real-time clock module. As mentionned earlier, one of the main issues for data acquisition is the sampling rate. In the proposed prototype, the current and voltage measurements are sampled at F s = 6.25 kHz. For this, two libraries are used: "FastADC.h"(compatible with a 10-bit resolution) for ADC management and "SAMD-TIMER.h" for timer-triggered interrupts. This sampling frequency is related to the writing time of the SD card. Indeed, in earlier versions of the code, we planned to directly write data into text files in the SD card. However, an analysis of the data writing time in a text file of the SD card showed that the use of the file.print() function of the SD.h library is very time-consuming and takes more time when the amount of data increases. In order to address this issue, instead of using a text file writing system, the SD card is used as an EEPROM composed of 512-byte lines (called "blocks"). The voltage and current acquisitions are performed each time an interrupt service is triggered by a 16-bit timer. We use 20 buffers of 510 bytes to store the current and voltage samples in the interrupt routine. Five bytes are used to store two voltage acquisitions and two current acquisitions. A buffer of 510 bytes contains hence 510/5 × 2 = 204 current and voltage acquired samples. When a buffer is full, the next buffer is filled. At the same time, it is signaled to the main loop of the program that the full buffer must be saved in the SD card. This is done via an array of 20 Boolean variables. A boolean variable is set to 1 when a buffer is fully filled, and is set to 0 when this buffer has been stored in a block of the SD card. The use of 20 buffers in the interrupt routine allows to have more time for a buffer of 510 bytes to be written in a line of the SD card using the function "cardwriteBlocks()" of the library "Sd2Fat.h". Indeed, the time required to fill a buffer is 204/6250 ≈ 32.6 ms, and the average time for writing in a line of the SD card is 4 ms for the chosen SD card. However this time is not always constant and exceeds sometimes the 40.8 ms needed to fill a buffer. A comparison of several SD card performances in terms of required time for successively writting 510 bytes into the SD card led to the choice of an SD card with the least amount of time [START_REF]SD memory cards: The features and specifications[END_REF]. We have chosen a 32 Go UHSI speed class Samsung EVO Plus SD card that has a reading speed of 95 MB/s, a writing speed of 20 MB/s and a lifetime of 10 000 writing cycles. With the chosen sampling rate, data acquisition can be made during 204×32.2 30 6250×512 seconds which corresponds to 25.4 days. It should be noted that the first line of the SD card is used as a header to store the total number of acquisitions, the date and time and the number of the last completed block. The main loop also manages a user interface. For this, two push buttons are used. One allows to start/stop an acquisition and the other allows to start/stop the transmission of the recorded data on the SD card to a PC via the USB connection cable. The user has access to the information of the SD card (date and time, first and last "block" of an acquisition) and can select the line or lines he wants to transmit.

IV. EXPERIMENTAL RESULTS

In this section, some acquisitions obtained thanks to the proposed prototype are presented. We show that with the same equipment, it is possible to obtain the consumption profiles of individual HEAs as well as the one of a complete dwelling. These measurements allow to build a database for HEAs recognition and power consumption profile establishment. Here, a power consumption profile corresponds to the time varying active power signal. It is calculated from the current and voltage measurements as follows:

P [n] = 1 M n k=n-M +1 v[k]i[k] (5)
where F 0 = 50 Hz is the utility frequency and M = F s /F 0 = 6250 50 = 125 is the number of samples per period. Fig. 5 shows the consumption profiles of two HEAs. Fig. 5 (a) shows the consumption profile of an LG washing machine operating at 30 • C in "Rapid" mode with a spin of 800 cycles per minute. In this profile we recognize the different steps of a washing machine operating cycles: water heating during the first fifteen minutes, then rinse from 15 to almost 30 minutes and finally spinning during the last minutes. The consumption profile of an electric oven operating at 250 • C is also presented in Fig. 5 (b), where the on/off control can be observed. On the other hand, Fig. 6 hightlights a twelve hours acquisistion of a whole house power consumption. Out of concern for visibility, only four hours of the whole day power consumption profile are shown. During two hours and a half, the washing machine is operating. At almost one hour and half a LCD TV is activated, followed by the electrical oven that is activated twice at almost the second hour and then the fourth one. Finally, an electrical heater is activated between the second hour and half and the third one. The constant power consumption of almost 250 W, referred as the baseload, corresponds to the two continuously operating wifi boxes, and the standby power consumption of five cell phone chargers plugged in and two TV receivers.

V. CONCLUSION

In this paper, we present a low cost and easy to install device for current and voltage acquisitions in the NILM framework, based on an Arduino MKR Zero microcontroller. The goal of such a device is to acquire voltage and current measurements that will be used to detect the switching on/off of HEAs [START_REF] Houidi | Statistical assessment of abrupt change detectors for nonintrusive load monitoring[END_REF] and to identify them once their electrical signatures have been set up [START_REF] Houidi | Relevant feature selection for home appliances recognition[END_REF]. Energy monitoring devices available in the market as well as prototypes set up by the NILM research community were presented with special emphasis on their main technical characteristics. This state of the art made us able to set the criteria needed for an efficient low cost acquisition system with a higher sampling rate than what is available. We then present the general architecture of the proposed protype before detailing the current and voltage sensing and conditioning hardware boards. We also describe the software and argue the sampling rate choice which is mainly dependent on the data storage speed in the micro SD card. This software can be found online at [START_REF]NILM data acquisition software[END_REF]. In the last section, some of the acquisitions made on individual HEAs and in a whole house are presented. This shows the main originality of this prototype which is its ability to adapt to different power levels. In future, two aspects could be developped: networking functionalities (e.g. Ethernet, WLAN, ZigBee) so that the proposed prototype can provide consumers with information on their consumption through an interface via a web server, and the ability to measure the currents and voltages of a domestic three-phase network.

Fig. 1 .

 1 Fig. 1. General framework of supervised NILM methods

  M.C. Lorek et al. present in [17] a low-cost prototype of small size peel-and-stick electricity meter PCB comprising current and voltage sensors with their transducer circuits and a Texas Instruments MSP430G2131 microcontroller. The prototype is installed on the external face of the electrical panel to generate voltage and current signals at a sampling rate of 1920 Hz. It does not require any intervention of a professional.

Fig. 2 .

 2 Fig. 2. Acquisition system overview: General architecture sketch (a) Picture of the real device (b).

Fig. 4 .

 4 Fig. 4. Voltage conditioning circuit

Fig. 5 .

 5 Fig. 5. Individual HEAs power consumption profiles: Washing machine (a) Electric oven (b).

Fig. 6 .

 6 Fig. 6. 4 hours long power consumption profile of a real house.

TABLE I COMMERCIALLY

 I AVAILABLE ENERGY MONITORING DEVICES.

		TED 5000	Eyedro	Neurio	Sense Home Energy	WattSeeker
		MTU + CTs in	MTU + CTs in	MTU + CTs in	MTU + CTs in	Lynx Datalogger
	Installation	the electric panel	the electric panel	the electric panel	the electric panel	(by a professional)
		(by a professional)	(by users)	(by users)	(by a professional)
	Software	Google Power meter /	My Eyedro software	Neurio mobile app	Sense mobile app	Wattseeker software
	support	Bidgely /Plottwatt		and Web service
	Network	Ethernet (IP protocol)	Ethernet (IP protocol)	WLAN access	WLAN access	Ethernet (IP protocol)
	communication	+ wireless	+ wireless
	Sampling	1 Hz	N/A	4 kHz	1 Hz	2kHz
	frequency
	Approximate price	$200	300 $CA	300 $CA	$300	N/A
	Origine	USA	Canada	Canada	USA	France
	Company

TABLE II SIMPLIFIED

 II BILL OF THE MAIN COMPONENTS OF THE PROPOSED ACQUISITION SYSTEM.

	Component	Wholesale price
	Hall effect voltage transducer LEM LV25-P	35 C
	Arduino MKR Zero	21 C
	4 × operational amplifiers OP27	6 C
	3 × IC voltage regulators 7812, 7912 and 7805	5 C
	EVO Plus Samsung microSD card (32 GB)	15 C
	Total	82 C

ACKNOWLEDGMENT

The authors would like to thank the Tunisian Ministry of High Education and Scientific Research, under Grant LSE-ENIT-LR11ES15 of University of Tunis El Manar.