Keywords:

Mamie,

Ce manuscrit est pour toi.

Tu as toujours rêvé d'un docteur dans la famille. Ton Titi.

ACKNOWLEDGEMENTS

First of all, I would like to warmly thank my CEA -UPSaclay supervisor, Dr. Jean-Christophe Gabriel, for giving me the opportunity to enroll in this international joint PhD program. I am highly grateful he put his trust in me at first sight, for taking over the development of the microfluidic platform, which was already the result of a long-time effort. The remote supervision during one year and a half, while I was in Singapore and he was in France, was continuously fed by regular laboratory meetings, which always helped me to correct or improve likely deviations and to stay on the right track. I thank him for the independence I had to lead and build my own PhD, from which I gained a lot of experience. Finally, I thank him for all the discussions we exchanged from which I learned a lot (scientifically and professionally).

I also have sincere and profound acknowledgements for my NTU supervisor, Prof. Charles Yang Chun, who especially took time to listen to my oral progress reports and always gave me insights into my research.

I would also like to thank the members of my jury committee for their interest in my research work, Prof. François Puel, Dr. Alain Marty, Assoc. Prof. Su Pei-Chen, Assoc. Prof. Marcos and the referees Dr. Sandrine Dourdain and Prof. Anne Varenne. I thank Dr. Ange Maurice for his patience in teaching me how to use the microfluidic platform when I arrived in Singapore in February 2021. I sincerely thank him for his time and for accepting several discussions when I had some problems with the platform. I also thank Dr. Varun Rai for helping me to find my feet in the SCARCE lab. I thank Prof. Jean Duhamet and Prof. Thomas Zemb profusely, who agreed to share some of their vast knowledge through several discussions.

I would also like to thank Mr. Aaron An from SCENG Robotics in Singapore for his hard-work, his availability and his sincere will to manufacture the exact piece I had in mind each time, thanks to his meticulous attention to detail through precise and concise questions. I thank Dr. Florent Malloggi for the time he spent advising me on my microfluidic simulations.

Many thanks to my two scientific research families. One is in NTU Singapore, the SCARCE team! Thank you Dr. Sim Ying, Dr. Xia Dong, Dr. Yamuna Jayaraman, Dr. Jeremy Ang Koon, and all the others I am not writing about here, but you will all always be in my thoughts. Thank you Nicolas, member of the French team (Nicolas, Sarah and I) during my stay in Singapore, for all the discussions we had when you were working on your physical e-waste sorting and I was doing some chemistry related stuff.

My other research family is located in CEA Saclay -France, the LICSEN! First of all, thank you Dr. Vincent Derycke for your help, advice and support when I arrived as a fresh new PhD student on September 30 th , 2020. Many thanks to all the permanent researchers for their warm welcome. I also thank the group of PhDs and interns that brought life, joy and laughs within the walls of the 466 for the end of my PhD: Yuemin, Nicolas, Sarah, Alexandre, Emma, Emilie, Rebecca, Hindia, Mylana.

Finally, I would like to pay tribute to all the teachers and professors who have taught me since primary school and who have provided me with all the knowledge that has helped me grow. Although they did not directly participate in this PhD, the fact that I am reporting my PhD work in this manuscript is the result of their involvement throughout my academic education. I have learned so much things from them, and for that, I will forever express my deepest gratitude.

Merci à ma famille pour leur amour et leur soutien, Maman, Papa, Napou, Mamie, Pépère, Tati, Hervé, les Can Can Emi, Fred, Arthur et le Lé, Julie, Paul, Zoé et Lebron. Merci Goule, Maz et Mel pour votre amitié immortelle.

Thank you to the one who accepted to continue our adventure in Singapore. Thank you to the one who made this extraordinary experience so unbelievable, lah! Thank you to the one whom I called my colleague for three years and who was the closest colleague I will never know again. The one who listens to me, supports me, sometimes goes against me, but still advises me, understands me and brings out the best in me every single second of my life. The one who accepted to be my wife on that unique February 3 rd on a Cambodian shore. The one. Sarah, je t'aime.

GENERAL INTRODUCTION

Metal recycling is the process of collecting, processing (separation) and reusing metal materials from various sources, such as discarded products, industrial wastes and metal scraps, by returning them to the production cycle. The concept of metal recycling has gained momentum over the last decade. On the one hand, industrial companies have continued to increase their production of electronic and electrical equipment, which has led to an everincreasing demand for metals. On the other hand, metal production from primary mineral resources has been extensively exploited and several international scientific institutions have shown that the existing and known metal resources from ores will not be sufficient to continue to meet the overall metal demand [START_REF] France | Géosciences : la revue du BRGM pour une terre durable = the review of BRGM for a sustainable Earth[END_REF][START_REF] Forti | The Global E-Waste Monitor 2020: Quantities[END_REF] . Therefore, the need to find reliable secondary resources has arisen, and in this regard, electronic and electrical waste (e-waste) has attracted much attention due to its high metal content (both qualitative and quantitative) and concentration [START_REF] Vermeşan | Advanced recovery techniques for waste materials from IT and telecommunication equipment printed circuit boards[END_REF][START_REF] Gorewoda | Determination of metals' content in components mounted on printed circuit boards from end-of-life mobile phones[END_REF] . When it comes to developing processes for recovering the associated metals, both physical and chemical processes can be implemented [START_REF] Wu | A critical review on the recycling of copper and precious metals from waste printed circuit boards using hydrometallurgy[END_REF] . Physical treatments typically enrich the e-waste stream with components or elements that have, for example, similar shapes or physico-chemical properties. Chemical processes then allow for further elemental separation and purification, resulting in single-element or alloys that meet commercial purity grade and specifications, hence that can be returned into the raw material stream.

The combination of these two types of processes, taken beyond the state of the art, embodies the true paradigm of the approach conducted in the SCARCE (NTU-Singapore CEA Alliance for Research in the Circular Economy) project in Singapore. Since the composition of one e-waste stream is not as steady and constant as for natural mineral resources, there is a need to quickly adapt the processes that can face highly variable chemical composition of the inputs. Therefore, the method that is advocated in SCARCE consists of first a disassembly followed by a smart automated sorting of the electronic components to enrich the e-waste sorting bins before implementing any hydrometallurgical chemical process. The latter represent a major category of chemical processes that are used to recycle metals from e-waste. These processes consist of dissolving, separating and recovering the metals contained in e-waste using a usually aqueous-based medium, but they need to be as selective as possible. They also tend to replace pyrometallurgical processes, which consume large amounts of energy (e-waste incineration), require large capital equipment expenses, and contribute to the release of toxic fumes and ashes. The latter, also wastes many metals as once they are mixed in the bottom or flying ashes, they are almost impossible to retrieve in an economically viable way. Among the hydrometallurgical processes, liquid-liquid extractions and solid-phase separations can be carried out for this application of metal recovery. However, extraction or separation efficiency depends on several parameters such as the concentration of the metal ions, the nature and concentration of the aqueous medium in which they are dissolved, the characteristics and properties of the extraction phase, the temperature. Therefore, the development of such processes on a laboratory batch scale for likely future scale-up and industrialization can be both time consuming and result in large amounts of liquid waste due to the number of experiments that need to be carried out for process optimization.

A fully automatized and instrumented platform was developed to facilitate the study of new hydrometallurgical chemical processes. A microfluidic liquid-liquid extraction module enabled rapid process development, providing a better understanding of the kinetics, thermodynamics and mechanisms involved during the process. The platform was also upgraded as a millifluidic tool to perform solid-phase extraction processes. Both the microfluidic and millifluidic extraction modules benefited from online downstream analysis of the output extraction streams based on X-ray fluorescence.

This PhD thesis starts with a first chapter outlining the overall context of the work that was carried out. The second chapter presents the microfluidic platform, the upgrades and improvements that were made as well as liquid-liquid extraction results that were obtained. The third chapter focuses on further development of the platform with the addition of a millifluidic setup for the study of solid-phase extractions. Finally, the fourth chapter describes the implementation of a new upstream mixing module that aims at being added to the existing microfluidic liquid-liquid extraction system. Throughout this work, a homemade microfluidic Fourier Transform Infrared Spectroscopy based module was used as a mixing performance assessment tool.

Finally, thanks to the international nature of this joint PhD, it should be noted that this PhD work was geographically conducted in two laboratories located in two different countries. In the first year and a half of the PhD, it was carried out in Singapore, at the Nanyang Technological University where the SCARCE international joint laboratory is located. The second part of this PhD then took place in From the start of this PhD (September 30 th , 2020) to its end (September 30 th , 2023), more than 160 million metric tons (Mt) of waste electrical and electronic equipment (e-waste) will have been generated in the world. According to The Global E-Waste Statistics Partnership led by the United Nations (UN), this corresponds to more than 1,700 kg of e-waste being generated every second around the world [START_REF] Forti | E-waste Statistics: Guidelines on Classifications, Reporting and Indicators[END_REF] . Besides, formally collected data show that ewaste management and recycling are not increasing at the same pace: in 2019, only one kilogram of e-waste was properly collected and recycled for every five kilograms.

Progress is needed in the global e-waste recycling effort, especially since urban mining, which refers to the collection of recyclable materials, such as metals, plastics, and electronics that are present in urban areas, embodies concrete and tangible secondary resources for metal supply. In this regard, pyrometallurgy is today the most implemented process for the recycling of metals coming from e-waste 7 . Nevertheless, its non-ecofriendly by-products and limitations in metal recovery are paving the way for cleaner and more efficient processes, based on hydrometallurgy [START_REF] Maurice | Dismantling of printed circuit boards enabling electronic components sorting and their subsequent treatment open improved elemental sustainability opportunities[END_REF] .

Throughout this first Chapter, a comprehensive description of generated e-waste and their associated management and recycling is drawn. A closer look is also brought on metals' criticality issues as well as currently implemented metal recovery processes and the new ones under study. A focus is then made on specific hydrometallurgy-based metal extraction techniques. The final part deals with their implementation at the microscale, in microfluidics, with a focus on liquid-liquid extraction.

E-WASTE

The term "e-waste", also known as WEEE (for Waste from Electrical and Electronic Equipment), generally refers to any electrical or electronic equipment, or a part thereof that is discarded with no intention of being reused (UN STEP Initiative 2014 9 ). Due to the global development of technology and the Internet, the use of electronics is now widespread, commonplace and present in most people's daily lives. However, their disposal suffers from a two-tier issue. During their time of use, electronics are subject to daily damage, which sometimes lead to malfunctioning problems that require repair maintenance or can even be fatal to the device. However, repair maintenance is challenged by the constant improvement and upgrade of brand-new models, which bring new features, most of the time are more energy efficient and offer a better reliability, or are artificially rendered obsolete by an operating system/application upgrade.

ELECTRONICS AND ELECTRICALS: PRODUCTION AND COMPOSITION

The production of electrical and electronic equipment is a global industry, with major manufacturers mostly located in countries such as China, Japan, South Korea, Taiwan, and the United States. However, it is a massive worldwide industry that encompasses a wide variety of products, from smartphones and laptops to refrigerators, electric vehicles and industrial machinery.

All electronics perform one or more functions, and to teach them what to do or how to interact with their environment, one or more Printed Circuit Boards (PCBs) are required. The PCB can be compared to the "brain" of the device, where all the controls and actions are defined and given, respectively.

Structurally, a PCB is a stratified material made up of alternate layers of Copper and epoxy resin [START_REF] Eric | Conception et fabrication des circuits imprimés rigides[END_REF] . The copper layers act as conductive interconnection tracks, connecting together the electric components (ECs), which are fixed onto them. Over the years, billions of different ECs have been produced, varying in function, type and composition. Example of functions include: Central Processing Unit (CPU), Integrated Circuit (IC) chips, inductors, capacitors, resistors, transistors, operational amplifiers, connectors, etc. [START_REF] Vermeşan | Advanced recovery techniques for waste materials from IT and telecommunication equipment printed circuit boards[END_REF] . Indeed, even within the same function and type, since each component has its own function and specifications, its internal structures and metallic contents vary from one EC reference to another. Also, the metallic element composition of a given EC may vary from one manufacturer to another and from time to time (due to cost issues, R&D improvements, implemented regulations to limit the use of some metals due to toxicity, etc.). Therefore, it is not possible to determine a priori the metallic content of a given EC type or reference, and studies have shown that anything between 10 to 69 different chemical elements can be present in a single electronic equipment [START_REF] Forti | The Global E-Waste Monitor 2020: Quantities[END_REF][START_REF] Vermeşan | Advanced recovery techniques for waste materials from IT and telecommunication equipment printed circuit boards[END_REF][START_REF] Gorewoda | Determination of metals' content in components mounted on printed circuit boards from end-of-life mobile phones[END_REF][START_REF] Debnath | Sustainability of metal recovery from E-waste[END_REF][START_REF] Chen | Recycling Waste Circuit Board Efficiently and Environmentally Friendly through Small-Molecule Assisted Dissolution[END_REF] .

E-WASTE GENERATION AND RECYCLING

As a consequence of our modern society dependence on electronic devices, an increasingly large volume of e-waste is being generated per year, making it the fastest-growing type of any waste stream. Hence, the 2018 global generation of electronic waste reached nearly 50 million metric tons (Mt) [START_REF] Ogunseitan | The Electronics Revolution: From E-Wonderland to E-Wasteland[END_REF] , and went up to 53.6 Mt in 2019, according to The Global E-waste Monitor 2020 (GEM 2020), a comprehensive study on global e-waste generation and management delivered by the United Nations (UN) 2 . Besides, the WEEE Forum, which is a non-for-profit European association founded in 2002 that brings together forty-six e-waste producer organizations, reported that the amount of e-waste generated by the European Union (EU)including Norway, Switzerland and the United Kingdom -has almost doubled between 2000 and 2015 (see Figure 1).

E-WASTE RECYCLING AS PART OF A CIRCULAR ECONOMY

E-waste recycling takes part in what is called today the Circular Economy. It is defined as a manufacturing and consumption model, which encompasses the four following notions, also known as "4 R's": Rethink, Reduce, Reuse and Recycle (see Figure 4). Recycling should be the final step in dealing with a piece of equipment, to be performed only when previous steps, such as reuse or repair after damage, have maximized the lifespan of a given electronic device. In France for instance, the AGEC law (Loi Anti-Gaspillage pour une Economie Circulaire, which stands for Waste Reduction Law for a Circular Economy) was enacted in early 2020 and aims to reduce waste, promote recycling as well as encourage more sustainable consumption patterns (e.g. prohibiting the destruction of unsold non-food products, requiring companies to indicate the reparability of products, provide spare parts and promote the use of recycled materials in products) 18 . In Singapore, the National Environment Agency (NEA) has introduced in 2021 an Extended Producer Responsibility (EPR) scheme for e-waste, through its Resource Sustainability Act (RSA) [START_REF]EPR) System for E-waste Management System[END_REF][START_REF]The Resource Sustainability Act[END_REF] . This means that manufacturers and importers of electronic and electrical equipment are required to be responsible for the disposal and recycling of their products at the end of their lifespan.

Another national e-waste recycling program, called the "Producer Responsibility Scheme" (PRS), involves the collection of e-waste from designated collection points implemented island wide.

METALS' CRITICALITY

As highlighted in section 1.2.1, the content of PCBs can be highly diversified, to include precious metals (such as gold, copper or palladium), Critical Raw Materials (CRM) (see Figure 5) or non-CRMs (e.g. aluminum or iron).

However, access to all metals found in natural ores and required for PCB and EC manufacturing is becoming more limited and scarce as existing mines and deposit reserves or access to them dwindle. For example, in 2009, China imposed an export ban on several Rare-Earth Elements (REEs), namely Tb, Dy, Y, Tm and Lu, and an export quota of 35 kt/yr for Nd, Eu, Ce and La.

In this context, the European Union started in 2011 to list out the CRM that, in the case of a shortage, could jeopardize its manufacturing economy [START_REF]On the review of the list of critical raw materials for the EU and the implementation of the Raw Materia[END_REF][START_REF][END_REF][START_REF]REGIONS Critical Raw Materials Resilience: Charting a Path towards greater Security and Sustainability[END_REF][START_REF][END_REF] . The list is updated every 3 years and except for chromium and helium, which were added to the list in 2014 and 2017, respectively, and then removed in the following versions, no other chemical element has been definitively removed since 2011 (scandium and tantalum only disappeared from the list in 2014). Noteworthy, all lanthanides have always been listed as critical since 2011. Among all the other metals, the list also includes refractory metals such as tungsten, niobium and tantalum, as well as all the metals grouped together under the name "Platinum Group Metals" -PGM (platinum, palladium, ruthenium, rhodium, osmium and iridium). A more comprehensive graph is provided in Annex 1 plotting the supply risk of most raw materials as a function of their economic importance in the EU. As highlighted in section 1.2.1, an important part of the periodic table of elements is represented in the e-waste stream, but we can see here that this is particularly the case for PCBs. Therefore, given the total amount of e-waste worldwide, the extraction of metals from urban mines should be a high priority, especially since the supply of metals is at stake. Nevertheless, although the stock of potentially recyclable metals present in urban mines could be used to alleviate the pressure on the exploitation of natural resources, studies show that this secondary resource would not be sufficient to fully meet today's metal demand [START_REF] France | Géosciences : la revue du BRGM pour une terre durable = the review of BRGM for a sustainable Earth[END_REF][START_REF] Pirard | Doctoral Summer School -Entrepreneurship in Circular Economy[END_REF] . Indeed, Matos calculated for the United States that a 100 % recycling of metals from e-waste could at best contribute to ≈30% of the needs of an ever-increasing demand 34 .

TODAY'S METAL RECYCLING PROCESSES

Waste PCBs are currently recycled through two different channels: a generally careless and unregulated informal sector that focuses primarily on extracting precious metals using environmentally harmful techniques, and a formal sector that uses industrial pyrometallurgybased processes to treat centralized waste in smelters. The latter uses the calorific value of the ~30 wt.% epoxy resins found in waste PCBs to reduce the heating costs 7 and thus has the advantages of being economically viable and environmentally controlled, ca. with limited, if any, release of toxic gases such as dioxins and hydrogen halide (coming from the brominated flame retardants incorporated in the bare boards [START_REF] Xia | On-line spectroscopic study of brominated flame retardant extraction in supercritical CO2[END_REF][START_REF] Gripon | Brominated flame retardants extraction from waste electrical and electronic equipment-derived ABS using supercritical carbon dioxide[END_REF] ). However, the incineration of the epoxy resin releases some carbon dioxide, which should be avoided in order to reduce the carbon footprint of recycling. In addition, it has been reported that currently only ≈10 to 15 of the ≈60 potentially present 37 chemical elements are recovered. Consequently, only about one-third of the total mass of waste PCBs can be recycled using such formal processes 5 . The remaining third of the mass of PCBs is found in complex mixed oxides within the bottom and fly ashes.

CURRENT RESEARCH WORKS ON E-WASTE HYDROMETALLURGY-BASED RECYCLING

The search for hydrometallurgical recycling processes to recover metals from waste PCBs is of dual interest. Firstly, such chemical processes avoid the formation of toxic and harmful fumes, which are not well managed in informal treatments. Secondly, it expands the range of recycled metal elements and finally, it may even increase the metal recovery yields.

Several studies have reported a step-by-step approach for implementing efficient and reliable separation processes [START_REF] Gorewoda | Determination of metals' content in components mounted on printed circuit boards from end-of-life mobile phones[END_REF][START_REF] Wu | A critical review on the recycling of copper and precious metals from waste printed circuit boards using hydrometallurgy[END_REF][START_REF] Maurice | Dismantling of printed circuit boards enabling electronic components sorting and their subsequent treatment open improved elemental sustainability opportunities[END_REF][START_REF] Chen | Recycling Waste Circuit Board Efficiently and Environmentally Friendly through Small-Molecule Assisted Dissolution[END_REF][START_REF] Khaliq | Metal extraction processes for electronic waste and existing industrial routes: A review and Australian perspective[END_REF] . Prior to the use of chemicals, mechanical treatments have triggered much attention (see Figure 7). Indeed, since metals are dispersed within a single PCB and are unevenly concentrated in its constituent ECs, dismantling of PCBs allows to recover ECs from the bare board and enables their sorting to provide several concentrated streams (either in types of EC or in chemical elements). Automatized mechanical treatments that can be implemented also take into account the magnetic and non-magnetic properties of some metals, for instance [START_REF] Ruan | Constructing environment-friendly return road of metals from e-waste: Combination of physical separation technologies[END_REF][START_REF] Niu | Application of Chloride Metallurgy and Corona Electrostatic Separation for Recycling Waste Multilayer Ceramic Capacitors[END_REF] . After these first separation steps, ECs are leached out using mostly acid solutions, which can be followed by chemical separation and extraction techniques (selective precipitation, filtration, liquid-liquid extraction, solid-phase extraction, membrane extraction, etc.), depending on the complexity of the leaching solution. The final steps usually consist of refining and purification processes to meet the criteria/specifications of industrial purification standards. For instance, Xia et al. showed how neodymium can be recovered from waste MLCCs, describing in detail the entire process (sorting, leaching, separation and purification to industrial standards steps) and highlighting the economic viability of the entire process when implemented on an industrial scale 40 . • The recovery of metals from spent Lithium-ion batteries (mainly Al, Cu, Li, Co, Mg, Ni);

• The recycling of photovoltaic solar panels and the recovery of Si and other metals (Ag, In, Al);

• PCBs recycling and metal recovery implementing smart sorting, physical and hydrometallurgy-based processes;

• The recycling of Plastics or resins commonly found in e-waste and their detoxification. Microfluidics is a field of science and engineering that deals with the study and manipulation of fluids and particles at the micrometer scale. It involves the design, fabrication, and manipulation of microscale devices (or Lab-On-a-Chip -LOC), such as chips, channels, and chambers, that are used to control the behavior and movement of fluids. Microfluidic devices operate according to the principles of microscale physics, which may differ from those of macroscopic systems. For example, in microfluidic systems, surface tension, viscous forces, and electrokinetics are more dominant than gravity. As a result, microfluidic devices can precisely control the movement, mixing, and separation of fluids, making them ideal for a wide range of applications in chemistry, biology, medicine, and engineering. Some common applications of microfluidics include LOC devices for point-of-care diagnostics [START_REF] Myers | Innovations in optical microfluidic technologies for point-of-care diagnostics[END_REF][START_REF] Yetisen | Paper-based microfluidic point-of-care diagnostic devices[END_REF][START_REF] Chin | Commercialization of microfluidic point-of-care diagnostic devices[END_REF][START_REF] Pandey | Microfluidics Based Point-of-Care Diagnostics[END_REF] , microreactors for chemical synthesis [START_REF] Liu | Why microfluidics? Merits and trends in chemical synthesis[END_REF][START_REF] Lo | Application of Microfluidics in Bioprocesses[END_REF][START_REF] Mcmullen | An Automated Microfluidic System for Online Optimization in Chemical Synthesis Abstract : An automated , continuous flow system for the online , multivariable optimization of a chemical reaction is presented . Time and material required for an optimizatio[END_REF] , microfluidic sensors for environmental monitoring [START_REF] Marle | Microfluidic devices for environmental monitoring[END_REF][START_REF] Pol | Microfluidic lab-on-a-chip platforms for environmental monitoring[END_REF][START_REF] Maguire | A review of centrifugal microfluidics in environmental monitoring[END_REF] , and microfluidic systems for cell analysis and manipulation [START_REF] Sibbitts | Cellular Analysis Using Microfluidics[END_REF][START_REF] Yeon | Microfluidic Cell Culture Systems for Cellular Analysis[END_REF][START_REF] Yin | Microfluidics for single cell analysis[END_REF][START_REF] Joensson | Droplet microfluidics-A tool for single-cell analysis[END_REF] . All of these techniques take advantage of relatively high surface-to-volume ratios, which allow (i) precise control of fluid transport due to the dominant surface tension 64 ; (ii) high heat dissipation that can occur during fluid mixing or chemical reaction 65 and; (iii) faster diffusion mechanisms, i.e. species transport. However, as Koo and Kleinstreuer experimentally showed and named "microfluidics effects" (wall slip, surface roughness, viscous dissipation, among others), the same high surface-to-volume ratio can also lead to different results of an experiment carried out either at the micro-or macroscale [START_REF] Koo | Liquid flow in microchannels : experimental observations and computational analyses of microfluidics effects[END_REF] . Indeed, in microfluidics, the surface is becoming increasingly important to the extent that molecules such as extractants of an L-L extraction can develop significant interactions with the walls of the microfluidic chip 67 .

ADVANTAGES OF IMPLEMENTING EXTRACTION PROCESSES AT THE MICROSCALE

Microfluidics makes it possible to carry out experiments at the microscopic scale that are usually performed at the macroscopic scale (for the chemist, a few microliters, or less, instead of the tens of milliliters of the macroscopic approach). Microfluidics enables studying continuous processes in the laboratory and getting rid of the limits and restrictions imposed by the macroscopic mode. This has the following consequences of : (i) facilitating the development of these processes before their implementation on a larger scale; (ii) accelerating the studies thanks to a likely automation of the instruments used; (iii) measuring physicochemical parameters related to the process that were previously difficult or even impossible to measure [START_REF] Kokoric | Determining the Partial Pressure of Volatile Components via Substrate-Integrated Hollow Waveguide Infrared Spectroscopy with Integrated Microfluidics[END_REF][START_REF] Penisson | Water activity measurement of NaCl / H2O mixtures via substrate-integrated hollow waveguide infrared spectroscopy with integrated microfluidics[END_REF] and; (iv) greatly reducing the energy consumption as well as the production of waste (of the order of a milliliter instead of a liter for a macroscale approach), thus reducing the carbon footprint of such research and development efforts.

Although microfluidics has many advantages as mentioned above, there are not many studies that report the implementation of extraction processes in microfluidics for the development of extraction processes dealing with metal recycling. Among them, Gouyon et al. performed gold and palladium electrodeposition from simulated leaching solutions [START_REF] Gouyon | Speciation and quantitation of precious metals in model acidic leach liquors, theoretical and practical aspects of recycling[END_REF]71 . Sathuluri et al. studied the extraction of silver, palladium and platinum (from a real leaching solution) by calix [4]arene extracting molecules using droplet-based microfluidics 72 .

LIQUID-LIQUID EXTRACTIONS IN MICROFLUIDICS †

Among all the industrial chemical processes that aim at separating and recovering elements from a complex mixture, L-L extraction plays a key role due to its chemical efficiency and speed. Because of the many independent variables involved in such complex L-L extraction processes, their development, implementation and understanding require extensive and time consuming phase diagram explorations, especially when they are to be scaled up to an industrial scale [START_REF] Pfennig | Liquid Extraction[END_REF] . Thermodynamic and kinetic data are particularly key to such understanding and process control and development. However, kinetic data can be difficult to extract because the core of extraction processes are carried out in mixer-settlers or pulsed columns [START_REF] Hj | Reactive Extraction[END_REF] , which require turbulent mixing of two immiscible solvents, and therefore preclude (i) knowing the specific microscopic surface area of contact between the two phases, and (ii) measuring the kinetics of phenomena that occur faster than the tens of minutes scale, as one must wait for the mixing, diffusion of species and decantation of the two phases prior to their analysis.

It is important to emphasize that the macroscopic contact area in the emulsified fluids is different from the microscopic contact area of the extracted species [START_REF] Testard | Liquid-liquid extraction: An adsorption isotherm at divided interface?[END_REF] .

There are three main architectures of L-L extraction implemented in microfluidics, the socalled guided structure, discrete and membrane-based extractions [START_REF] Maurice | Microfluidic lab-on-chip advances for liquid-liquid extraction process studies[END_REF] . In all these three techniques, the two immiscible phases are ultimately separated, enabling downstream chemical analysis of both fluids.

Guided structure extraction

This L-L extraction technique uses ultra-thin microchannels with a depth of less than 10 µm and a width accounting for ≈100 µm. When the aqueous and organic phases are brought into contact with each other in the channel, their respective surface tensions are high enough to prevent the formation of a microemulsion between the two liquids, within a certain flowrate range. The L-L interface is therefore stable and a separation of the two phases can be achieved with a final Y junction 76 (see Figure 12).

. Discrete extraction

The discrete extraction technique is based on segmented microflows, in which droplets of each phase alternate within the channels of the microfluidic device. Hence, microdroplets of usually-organic phases are formed into a mobile aqueous phase, thanks to a specific channel geometry (see Figure 13.A). After extraction, the two phases are separated, e.g. by a membrane through which one of the two phases can pass and the other cannot (see Figure 13.B). 1.

CONCLUSION

The generation, management and recycling of e-waste is a global problem that is not fully and properly addressed. The lack of widespread legislation at different community levels is one of the reasons why (i) a large part of comprehensive formal data on generated e-waste is missing, which limits the broad understanding of e-waste and the related actions to be taken and; (ii) only a small part of e-waste is reported to be formally recycled, while informal recycling is not controlled and regulated, allowing non-environmentally friendly processes to be implemented. However, in order to meet the ever-increasing demand for electronics manufacturing -and, by extension, metals -it is necessary to consider e-waste as a real and tangible secondary source to counterbalance the likely supply of metals from primary resources (ores from natural mines). Indeed, the production, access and security of metals has become a geopolitical endeavor, bringing to the international community the notion of the criticality of metals. For this reason, urban mines today represent concrete metal resources for electronics production, where more than sixty chemical elements can be found in a single electronic waste. The precise location where these metals are mostly concentrated is the PCB, which is the brain of any electronic device. Copper, silver, gold, tin or REEs are found there in various concentrations. Today, pyrometallurgy is the most widely used process for their recovery, but PCB incineration not only limits metal recycling yields and the range of chemical elements recovered, but also releases toxic fumes due to the pyrolysis of plastics. For this reason, more environmentally friendly and efficient metal recycling processes have been investigated in recent years, combining both mechanical and chemical treatments. The latter consists of dissolving (leaching) metallic compounds in an aqueous medium and then using chemical engineering techniques to separate and purify the metals: this is called hydrometallurgy. Among these metal extraction processes, L-L extractions and SPE are commonly used on an industrial scale.

In this context, the purpose of this PhD work was to develop hydrometallurgy-based processes by studying them on an automatized and instrumented microfluidic platform. Indeed, microfluidics enables fast-process developments, brings better understandings of the mechanisms involved during the process as well as limits generated liquid waste after experiment. Coupled with a downstream analysis of output extraction streams, such a tool allows carrying out fast and comprehensive metal extractions studies.

The first generation of the further described platform was made during the REECYCLE project funded by the European Research Council (ERC) grant obtained by Prof. Thomas Zemb and Dr. Jean-Christophe Gabriel in 2012. Overall, one of the work package of the project was to develop a microfluidic platform with on-line metal analysis for rapid determination of phase diagrams necessary to define the optimal operating point of L-L extraction studies. Former post-doctoral researcher Dr. Johannes Theisen and PhD student Dr. Christophe Pénisson built the platform from scratch [START_REF] Penisson | Etudes thermodynamiques de fluides complexes par un dispositif de caractérisation microfluidique intégré[END_REF] , but never fully assembled and used it for its purpose because of safety-related-certification time constraints. The whole platform was then shipped out to Singapore for the beginning of the SCARCE project in August 2018 where former post-doctoral researcher Dr. Ange Maurice took over and assembled the platform (see Figure 15) as well as fully-automatized it with the writing of a Python code [START_REF] Maurice | First online X-ray fluorescence characterization of liquidliquid extraction in microfluidics[END_REF] .

Throughout this PhD work, L-L extraction (Chapter 2) and SPE (Chapter 3) comprehensive studies were conducted respectively in microfluidics and millifluidics dedicated modules. Furthermore, a new brick enabling and qualifying liquid mixing was also added to the platform, built with the help of an independent Fourier-Transformed-Infrared (FTIR) microfluidic setup (Chapter 4). Chapter 2: Liquid-liquid extraction processes implemented on a microfluidic platform

PRESENTATION OF THE MICROFLUIDIC PLATFORM

Laboratory researchers have used online characterization tools in microfluidic circuits to maximize automation and avoid manual characterization [START_REF] Maurice | Microfluidic lab-on-chip advances for liquid-liquid extraction process studies[END_REF][START_REF] Li | An Integrated Microfluidic Probe for Mass Spectrometry Imaging of Biological Samples**[END_REF][START_REF] Wang | Ionophore-Based Biphasic Chemical Sensing in Droplet Microfluidics[END_REF][START_REF] Perro | Combining microfluidics and FT-IR spectroscopy: Towards spatially resolved information on chemical processes[END_REF][START_REF] Rizkin | Review Article: Spectroscopic microreactors for heterogeneous catalysis[END_REF][START_REF] Kocot | How to detect metal species preconcentrated by microextraction techniques?[END_REF] . Spectroscopic techniques, such as Raman [START_REF] Chrimes | Microfluidics and raman microscopy: Current applications and future challenges[END_REF][START_REF] Pinho | Microfluidics with: In situ Raman spectroscopy for the characterization of non-polar/aqueous interfaces[END_REF] , infrared [START_REF] Kokoric | Determining the Partial Pressure of Volatile Components via Substrate-Integrated Hollow Waveguide Infrared Spectroscopy with Integrated Microfluidics[END_REF][START_REF] Penisson | Water activity measurement of NaCl / H2O mixtures via substrate-integrated hollow waveguide infrared spectroscopy with integrated microfluidics[END_REF][START_REF] Perro | Combining microfluidics and FT-IR spectroscopy: Towards spatially resolved information on chemical processes[END_REF][START_REF] Silverwood | Towards microfluidic reactors for in situ synchrotron infrared studies[END_REF] , and UV-VIS [START_REF] Silverwood | Towards microfluidic reactors for in situ synchrotron infrared studies[END_REF] , have been widely used. However, to date, only Hellé et al. directly employed online Inductively Coupled Plasma -Mass Spectrometry (ICP-MS) characterization to study L-L extraction processes 92 .

Among these analytical techniques, X-ray fluorescence (XRF) is also used to determine the elemental composition of a sample. This characterization technique works by bombarding the sample with X-rays, causing the atoms in the sample to emit fluorescent X-rays. The energies of these fluorescent X-rays are characteristic of the elements present in the sample, and by measuring the energies of the emitted X-rays, the elemental composition of the sample can be determined. XRF can provide quantitative and qualitative analysis of the sample composition and is non-invasive and non-destructive. When applied to L-L extraction analysis, metal concentrations can be measured in organic phases. This is a significant advantage over ICP-based analysis, which can only be performed on aqueous phases. Nevertheless, only a few off-line studies have used XRF in microfluidics, and they do not address the problem of L-L extraction [START_REF] Mann | Element-specific detection in capillary electrophoresis using X-ray fluorescence spectroscopy[END_REF][START_REF] Miller | Capillary electrophoresis micro x-ray fluorescence: A tool for benchtop elemental analysis[END_REF][START_REF] Nagasaka | Laminar flow in microfluidics investigated by spatially-resolved soft X-ray absorption and infrared spectroscopy[END_REF][START_REF] Mcintosh | Determination of plutonium in spent nuclear fuel using high resolution X-ray[END_REF][START_REF] Tyssebotn | CE-XRF-initial steps toward a non-invasive elemental sensitive detector for liquid separations[END_REF] .

OVERALL DESCRIPTION OF THE PLATFORM

The experimental microfluidic platform used in this PhD work, was designed and implemented in its first version, partly by Dr. Johannes Theisen and partly by Dr. Ange Maurice. It comprises an extraction and a downstream measurement system. As shown in Figure 17, the core of the apparatus consists of a moving part and a static part. The static part is the XRF system for the analysis of the metals contained in the output liquid extraction streams, composed of a miniature X-ray tube and a small XRF detector. The moving part is a 3D-printed holder mounted on a commercial motorized translational moving stage that holds a microfluidic L-L extraction chip, two valves and an XRF chip, in which both aqueous and organic phases are analyzed separately. Indeed, the stage moves between two positions A) and B) allowing to expose the two different areas of the XRF chip to the X-ray system.

measurements. Originally 3D-printed in PLA, this material shows a high mechanical resistance, but it appeared to undergo some strain under the heat released by the X-ray tube over time, which partially melt the tube holder (see Figure 24). As a consequence, this led to a displacement of the X-ray beam during experimental acquisition leading to important changes in the shape of the then-recorded XRF spectra. Thus, a new material was implemented and the prime choice was Polycarbonate (PC), known for both its mechanical and thermal strength.

Besides, this material can also be used for 3D-printing. Over time and to date, such a PC tube holder has never shown any strain.

X-ray analysis optimization parameters

When starting a new L-L extraction study, a comprehensive work must be done to optimize the parameters of the X-ray analysis system. Indeed, this step is of paramount importance as the recorded XRF spectra are the basis for determining the metal concentrations for all L-L extractions. Since these concentrations are then used in the calculation of kinetic and thermodynamic experimental parameters, the accuracy of the obtained data strongly depends on the optimization of the XRF spectra, based on the S/N ratio, which must be as high as possible.

First, both voltage and current of the X-ray tube can be specificied, ranging from 0 to 50 kV and from 0 to 200 µA, respectively (as per Moxtek MAGNUM 50 kV X-ray tube used in the platform). A higher voltage widens the range of chemical elements that can be seen in a recorded XRF spectrum. Besides, increasing either the voltage or the current leads to higher metal peaks but also decreases the S/N ratio. Also, if the intensity of the emitted X-ray signal is too high (high voltage and/or current), the XRF detector can become saturated by the high flux of received fluorescent photons and the resulting spectrum is unexploitable.

LIQUID-LIQUID EXTRACTION IMPLEMENTED ON THE PLATFORM

INTRODUCTION

The following L-L extraction study reports the extraction of ytterbium (Yb), a REE, and iron (Fe) using an organic synergetic mixture of two extracting molecules, namely HDEHP (Bis(2ethylhexyl) phosphate) and DMDOHEMA (N, Nʹ-Dimethyl, N, Nʹdioctylhexylethoxymalonamide) -see molecules' structure in Figure 27. Extraction processes using this synergetic combination of organic molecules were first studied using microfluidics and off-line XRF by Dr. Christophe Pénisson in his PhD thesis work [START_REF] Penisson | Etudes thermodynamiques de fluides complexes par un dispositif de caractérisation microfluidique intégré[END_REF] and further investigated by El Maangar et al. [START_REF] El Maangar | A microfluidic study of synergic liquid-liquid extraction of rare earth elements[END_REF] , where they particularly focused on the extraction selectivity towards REEs when compared to iron.

The purpose of our study was to show how the newly developed integrated microfluidic platform with online XRF could bring new perspectives for L-L extraction understandings and specifically in terms of kinetics measurements. Also, thermodynamic data that could not be obtained in macroscale-implemented (batch) experiments but which could be extracted thanks to the membrane-based technology of the microfluidic extraction chip, are reported.

MATERIALS AND METHODS

Protocol and experimental parameters

The experimental study was divided into two specific extractions. The first one, called direct extraction (D-extraction), consisted of using an initial aqueous solution of nitric acid concentrated in Yb(III) and Fe(III) § and an organic extracting phase only loaded in HDEHP and DMDOHEMA molecules. The second extraction, further referred to as reverse extraction (R-

Aqueous phase

The initial aqueous phase was based on a 0.3 M nitric acid solution, in which weighed amounts of Yb(NO3)3•5H2O and Fe(NO3)3•9H2O salts were dissolved. The concentrations of the two metallic elements were 11 mM.

Preparation of the organic phase for D-extraction

Isane® was used as the solvent for the organic phase to which pipetted volumes of HDEHP and DMDOHEMA extractants were added. Their concentrations were based on weight percentages of Isane®, which were 25 wt% and 75 wt% for DMDOHEMA and HDEHP, respectively.

Prior to using the organic solution in any microfluidic L-L extraction, the phase was placed in a test tube, mixed with a 0.3 M nitric acid solution and stirred on an orbital shaker for 24 h. After centrifugation and phase separation, the organic solvent was ready for use. This process consisted of saturating the organic phase with nitrate anions, which may impinge on the extraction kinetics when implemented in microfluidics if not performed.

Preparation of the organic phase for R-extraction

Two separate test tubes were prepared. One contained equal volumes of the organic phase loaded with extractants and of a solution of 0.3 M nitric acid concentrated at 93.79 ± 0.22 ** mM of Fe(III). The other test tube only differed from the first one with a 0.3 M nitric acid solution concentrated at 17.60 ± 0.07 mM of Yb(III). Batch L-L extractions were then performed by placing the tubes on an orbital shaker for 24 h. Fe(III) and Yb(III) concentrated organic solutions were obtained by centrifugation (6640 g for 20 min) and phase separation, and respective concentrations were calculated by difference of metal concentrations measured by ICP-OES of initial and after L-L extraction aqueous solutions. Final organic phase used for R-extraction was obtained by pipetting calculated volumes of Yb(III) and Fe(III) organic solutions.

X-ray analysis parameters

As showed in Figure 29, the energy values that can be seen by Amptek XRF detector and resulting in a peak are 7.41 (Lα1), 8.40 (Lβ1) and 9.82 (Lγ1) keV for Yb and 6.40 (Kα1) and 7.06 (Kβ1) keV for Fe. For both elements, the peak corresponding to the smallest energy value is the highest and sharpest. Also, the higher the atomic number, the higher the peak intensity is. For these two reasons, Fe Kβ1 is overlapped by Yb Lα1.

** Metallic content of the aqueous solutions are measured by Induced Coupled Plasma-Optical Emission Spectroscopy (ICP-OES).

XRF calibration in the study

Two sets, one aqueous and the other organic, of standard solutions were prepared with randomly defined concentrations of both Yb(III) and Fe(III), in the range of 0 to 12 mM, as showed in Table 3. Each standard was passed and analyzed through the XRF microfluidic chip and all the recorded calibration spectra were computed in a written Python code following the PLS regression.

Figure 31 shows the calibration curves obtained for both Yb(III) and Fe(III). The XRF signal of Fe(III) was integrated between 6.08 and 6.82 keV and the one of Yb(III) from 7.13 to 7.88 keV (as per peaks observed Figure 29). 4, extraction ratios at thermodynamic equilibrium (considered to be reached after 24 h of extraction) for the two elements reach almost 100 %. As explained previously, the metallic concentration calculated for the organic phase after extraction was determined by measuring the metal content in the aqueous phase after extraction by ICP-OES, and using the law of conservation of mass.

Kinetics results

One L-L extraction run (either D-or R-extraction) performed at one of the four chosen temperatures (20, 25, 30 and 35 °C, see section 2.3.2.1) comprised² the study of the four aforementioned flowrates: 20, 10, 5 and 1 µL/min. These 4-in-1 L-L extraction experiment took less than 18 hours and generated less than 30 mL of total liquid waste (aqueous and organic). Between each modification of flowrate, automatized valves were toggled from the extraction phases to the purging liquids (ethanol and pure Isane® for aqueous and organic channels, respectively) -see Figure 28. Purge rate was set at 100 µL/min and lasted 15 min. However, the L-L extraction chip was not purged as the high purge flowrate could lead to some leakage. Thus, only the XRF chip was purged as ethanol could soak the PTFE membrane sandwiched in the microfluidic extraction chip, which needed to be kept loaded with Isane® and the extracting molecules. The absence of purge in the L-L extraction chip could lead to concentration peaks in the organic phase at the beginning of each new flowrate (see graph placed in Annex 3), but this phenomenon was not hindering the overall accuracy of the process.

Figure 33 shows the data obtained for the extractions that were run at the temperature of 25°C (the others are placed in Annex 4). Evolution of elemental concentrations of both Yb(III) and Fe(III) for D-and R-extractions as a function of experimental time are displayed on four graphs. Thanks to the motorized stage enabling alternating measurements of both organic and aqueous phases, evolutions of metallic concentrations in the two immiscible phases are plotted. On each graph, the metal concentration first increases during a transition period until it levels off at a value indicating that a steady-state was reached for the L-L extraction at the given flowrate. Indeed, the first period of increasing concentration corresponds to the time required for the front interface of the solutions obtained under the new conditions to flow through and replace the former liquids within the microfluidic platform (microfluidic chips and tubing). Hence four plateaus are observed, one for each of the four different implemented flowrates. Furthermore, slow flow rates mean that the old and new solutions are in contact for a longer period of time, which leads for some ion diffusions from one solution to the other and hence, the lower the flowrate, the longer the transition time to reach a steady regime. Each steady-state plateau averaged value represents one point of the Yb(III)/Fe(III) L-L extraction kinetic curve. Such a plateau shall not be confused with the reaching of the thermodynamic equilibrium, which was shown in the previous section 2. Although ICP-OES gives accurate concentration results, there is always an intrinsic limit of detection from the apparatus. Hence if one relies only on the aqueous measurement to determine the metal ion concentration in the organic phase using the action mass law, one can have results with a significant margin of error, therefore leading to error in the free energy calculations (further described below). Besides, other disadvantages of this technique of analysis is the amount of time required to prepare and analyze the samples as well as the loss of the liquid sample (destructive method). XRF is not only a non-destructive method but when implemented online, it is time-effective (these points are further discussed Chapter 3), and can be applied to both aqueous and organic channels.

The objective of this section is to present the determining and calculation of the free energy of transfer for the studied L-L extraction.

Definition of the free energy of transfer

In an L-L extraction, an aqueous phase containing ions is brought into contact with an organic phase containing extracting molecules. Upon contact, the chemical potentials of ions 𝑖 in the organic 𝜇 , and aqueous 𝜇 , phases change until reaching equilibrium 102 . At equilibrium, the chemical potentials of ions in both phases are equal (see Equation ( 1)). The change in chemical potential during this extraction is the free energy of transfer, denoted as ∆𝐺 (see Equation ( 2)). The free energy of transfer is defined for each ion 𝑖 and is the sum of the energy mechanisms used to extract the ions, such as the energy of micelle formation or the energy of transfer at the interface 103 .

𝜇 , = 𝜇 , (1)
∆𝐺 = 𝜇 , -𝜇 , = -𝑅𝑇𝑙𝑛 𝐶 𝐶 𝐽 𝑚𝑜𝑙 ( 2
)
Where R is the gas constant, T the temperature in Kelvin and 𝐶 and 𝐶 are the concentrations of ion 𝑖 at thermodynamic equilibrium in the organic and aqueous phases, respectively.

Table 5 presents ∆𝐻 and ∆𝑆 values for Fe(III) and Yb(III) for R-extraction, which refer to the enthalpy and the entropy of the associated metal ion, respectively. The results show an exothermic reaction for both elements. These experimental values are particularly of interest for L-L extraction modelling research on thermodynamic equilibrium [START_REF] Zemb | Recycling metals by controlled transfer of ionic species between complex fluids: en route to "ienaics[END_REF]103 . The quantification of the resistances enabled to deepen the understanding of the kinetics of extraction and to what extent one interfacial resistance may be dominant towards the others.

Principle

In the microfluidic extraction system that is described here, four extraction kinetic contributions needed to be taken into account when modeling the kinetics. These four contributions are underlying four distinct interfacial resistances origins, namely the aqueous phase, the L-L interface, the membrane itself and finally the organic phase (see Figure 36.A)).

The resistance at the L-L interface is the only one that can be responsible for a chemistrydriven kinetics. Indeed, this interface is the specific location of the coordination between the cations from the aqueous phase and the ligands (i.e. extracting molecules) from the organic phase. The three others rely on the diffusion of species through the associated medium in which they are. Hence, knowing which resistance is predominant for a given ion allows qualifying and quantifying its kinetic limiting step. ‡ ‡ This investigation on interfacial resistances was conducted on the L-L extraction system reported by Maurice et al. [START_REF] Maurice | First online X-ray fluorescence characterization of liquidliquid extraction in microfluidics[END_REF] , which was composed of Lanthanum -La(III), Europium -Eu(III) and Ytterbium -Yb(III), instead of Yb(III) and Fe(III) as presented before. However, identical organic phase (75 wt% HDEHP/25 wt% DMDOHEMA in Isane®) is used. Practically, Dr. Ange Maurice conducted the experiments and I performed the resistances' quantification under the supervision of Dr. Jean Duhamet.

Simulation of the extraction in Scilab

The simulation code written for interfacial resistances' quantification was adapted from the one initially written by Dr. Jean Duhamet on Scilab. The parameters used in this L-L extraction modelling tool are summarized Table 6. Initial ion concentrations were depending on whether D-or R-extraction was being simulated in the program. kd (the distribution coefficient, also known as partition coefficient) is the ionic concentration in the organic phase at L-L extraction thermodynamic equilibrium divided by the one in the aqueous phase. Aqueous diffusion coefficient of ionic species was extracted from Touré et al. 105 . However, organic diffusion coefficient of cations chelated by ligand species in an organic medium was less tabulated than diffusion coefficients in aqueous media. Thus, the value inserted in

Results

Figure 37 shows bar plots of the four previously described interfacial resistances for D-and Rextractions and for lanthanum and europium § § , as these cations were two of the three ions that were implemented in the L-L extraction system used for this resistances' quantification study. As expected, the organic resistance is always greater than the aqueous one because of a ten times difference between the two diffusion coefficients (see Daq and Dorg values in Table 6). For each extraction at any temperature, the membrane resistance is accounting for approximately 30 % of the overall resistance. This is similar to what was previously reported by Theisen et al. who showed that the membrane was not the only limiting kinetic factor and that the organic phase was most of the time contributing as much as the membrane in the diffusion of species [START_REF] Theisen | Effects of porous media on extraction kinetics: Is the membrane really a limiting factor?[END_REF] . Also, the L-L interface resistance decreases when temperature increases and does not play a big role in mostly R-extractions, for both species. Indeed, the diffusioncontrolled kinetics in such L-L extraction system is shown with the absence of a significant interface resistance. However, at 20 °C and 25 °C, and specifically for La(III), D-extractions are controlled by both diffusion and chemistry, as the L-L interface resistance accounts for about a third of the total resistance.

Finally, total resistance is in all cases higher in D-extractions than in R-extractions, highlighting an asymmetry between the two types of extraction. This phenomenon relies on a virtually total absence of the interface resistance in R-extractions. The likely explanation of this decrease of interface resistance may be caused by what some studies are recently calling the "interphase". This interphase is defined as a multi-layered structure of extracting molecules between the aqueous and organic phases The quantification of interfacial resistances of an L-L extraction system is a methodology that has been reported in several studies [110][111][112] , and even for membrane-based L-L extractions 113,114 . However, as the L-L interface is not controlled in such reported L-L extraction experiments, the resistance at the L-L interface could not be determined and, in most cases, was assumed to be close to zero (with a transfer coefficient tending to infinity). According to our literature search, the asymmetry of this L-L interface resistance has never been proved with a quantification. This is the consequence of (i) not taking into account the resistance at the L-L interface in the total resistance (due to an uncontrolled interface between the two immiscible phases) and; (ii) not studying the reverse extraction (papers dealing with interfacial resistances' quantification in L-L extraction studies were not carrying out R-extractions). § § Ytterbium was the third element to be implemented in the study but due to its fast extraction kinetics, an accurate quantification of its interfacial resistances was not possible.

understanding of the complex kinetic mechanisms occurring during an ionic L-L extraction process and opens a new path of investigation. ,115,116 , none of them was found to have encountered the issue of highly fast kinetics.

PERSPECTIVES OF THE LIQUID-LIQUID EXTRACTION PLATFORM

In this section, a new microfluidic L-L extraction chip is presented, as the first tool helping the resolution of fast extraction kinetics of less than 1.7 min. The validation of this microfluidic chip was carried out using two REEs, ytterbium and yttrium.

In September 2022, the millifluidic platform was moved out from NTU, Singapore to CEA Saclay, France requiring for a nuclear safety file to be prepared and submitted to the French Xray regulation authority (ASN, Autorité de Sûreté Nucléaire). This is mandatory to get the legal right to use and run the X-ray generator implemented on the platform. At the time of the study, the authorization to run the platform with X-rays was still pending and for this reason, the following study could not be implemented on the platform.

Materials and methods

Presentation of the liquid-liquid extraction microfluidic chip

The microfluidic chip solving fast extraction kinetics still comprised a microfluidic channel etched on a glass square wafer of 5 cm and with a thickness of 1 cm. As Figure 38.A) shows, the extraction channel is a straight etched groove, 2 cm long, 1 mm wide and 0.2 mm deep. Therefore, the length and proportionally the liquid volume of the channel were decreased by 88 % (now 4 µL). The two glass wafers were still sandwiching the same PTFE membrane, enabling a stable and controlled L-L interface between the two immiscible phases, and closed with the same stainless steel structure (see Figure 38.B)).

Therefore, we reached to reduce the interval of Yb(III) thermodynamic equilibrium, which is achieved between 1 min (this study) and 1.7 min (Yb/Fe study) of contact time.

Conclusion

A new microfluidic L-L extraction chip was presented in this section, which aimed at resolving the kinetics of fast extraction kinetic systems. The volume of the microfluidic chip was decreased by 88 %, with same depth and width as previous serpentine chip. Hence, this specific chip enables running L-L extractions with a contact time between the two immiscible phases down to 0.

Microfluidic extraction chip and validation tests

In the below descriptions, the mentioned liquid is considered as referring to an aqueous acidic solution, concentrated in metal ions. The type of membrane that was tested comprised of either a Polyvinylidene Fluoride (PVDF) or a PTFE substrate layer, on which a thin layer of 2Dnanosheet material (semi-hydrophobic) is deposited. The membranes used in this preliminary study were prepared by Ms Sarah Chevrier, whose PhD work focuses on the development of such membranes for, among other applications, metal extraction purposes.

For this work, two types of liquid flows, thus two designs of microfluidic chip, were considered and studied.

soaked with the aqueous flowing phase. After the saturation of the PVDF layer with the water phase, a uniform output liquid flow was observed. The same conclusion was also drawn at flowrates of 20 and 1 µL/min.

Next step consisted of testing a membrane, based on PVDF with the nanomaterial layer. The same liquid and flowrates were tested. Using this perpendicular-flow-based chip, the input flowing liquid was facing first the side of the nanomaterial layer, which featured semihydrophobic properties. However with the three flowrates, no output liquid was observed and this led every time to internal liquid leakages.

At this step and considering the semi-hydrophobicity of the membrane, this perpendicular approach was put aside and another technique described below was considered. However, such cross-flow technology might still be of interest if working with a 100 % hydrophilic membrane, i.e. with a highly permeable material.

Parallel flow

The parallel flow presented in this section was identical to the one that is implemented in microfluidic L-L extractions, but instead of establishing a co-flow involving two immiscible phases, two aqueous phases are flowing along each side of the membrane. Hence, the very same microfluidic serpentine-based extraction chip could be used.

In a first set of experiments, composite membranes made of PVDF + nanomaterial-based layer were tested at liquid flowrates of 20, 10 and 1 µL/min. However independently of the flowrate, output flow could only be observed in one of the channel, which could randomly be either the retentate (liquid flowing on the side of the nanomaterial layer) or the permeate (liquid flowing on the side of the PVDF layer) side. To avoid this, we used hydrophobic PTFEbased composite membranes. However since only aqueous liquids were flowing through the microfluidic chip, a way to allow for the hydrophobic membrane to be wetted by such solutions was needed. Hence, the following protocol was developed. First, an aqueous liquid was dispensed at 20 µL/min on the nanomaterial side and at the same time, pure ethanol was injected on the PTFE side channel, at same flowrate. Indeed, the soaking of the PTFE by ethanol can be observed by naked eye as it turns from white (due to light scattering) to translucent, when wet (see Figure 41.A)). Then, when all the serpentine turns translucent, the ethanol syringe was replaced by another one containing the aqueous phase. A picture of the full setup is showed Figure 41.B).

Thanks to this new protocol, output liquids were observed at the exit of both liquid outlets. However for each implemented flowrate, liquid volumes obtained at the retentate and permeate sides were unequal. The cause of this phenomenon may come from an imperfect permeability of the membrane or from an unbalanced osmotic pressure.

liquid outlets for the experiment carried out at 10 µL/min. First, different molar quantity values of the same element are observed in the retentate and permeate, which means that the 2D-nanosheet material did not break when closing the microfluidic chip (the PTFE substrate is a soft polymer layer). This is also confirmed by the fact that two different liquid volumes of retentate and permeate were obtained. Besides, the permeate side contains 2.04.10 -3 mmol of Ag(I) and 1.29.10 -4 mmol of Cu(II), which corresponds to an extraction ratio of 46.11 % and 2.91 %, respectively. This shows that silver ions could pass through the membrane while copper ions were mostly blocked. These results show (i) that the membrane layer was not damaged while closing the microfluidic chip and; (ii) a high selectivity of the membrane towards silver ions.

Conclusion

In this section, a new application that could lead to comprehensive studies conducted on the microfluidic platform was presented: membrane testing. Membranes are promising materials for metal separation processes but their study in batch mode can lead (i) to time-consuming experiments if the kinetics of extraction relies on diffusion only and; (ii) to the generation of large amounts of liquid waste. The microfluidic platform can solve these two issues as such extraction process takes advantage of both the microscale and the online XRF analysis. In this regard, membrane testing implemented in a microfluidic tool is a topic that has only been reported in only a handful of studies [121][122][123] .

Here, we developed a modus operandi for the study of ion-specific membranes that can definitely be implemented at the microfluidic scale. Two types of technologies were tested: perpendicular and parallel flow. The first one may be considered for highly permeable membranes. Concerning the parallel flow, a protocol for the implementation of a semihydrophobic material was developed and preliminary tests showed promising results for a full integration on the automatized and instrumented platform.

CONCLUSION

The development of a microfluidic system for L-L extraction is a first step towards faster studies of physicochemical systems, also accounting for a relatively reduced carbon footprint.

In this regard, a fully automatized and instrumented microfluidic platform was further developed by upgrading its constituent parts. The first upgrade comprises a microfluidic L-L extraction chip, which puts into contact two immiscible phases, one aqueous and the other organic. The chip sandwiches a hydrophobic PTFE membrane, enabling a controlled interface throughout the extraction process. The two output liquids are then sent to a downstream and online analysis module using XRF for the measurement of metals' concentrations in the liquid streams. Overall, such platform drastically reduces the total liquid waste generated by this type of chemical process as well as the time required to carry out the experiments, when compared to batch mode.

L-L extraction involving Fe(III) and Yb(III) was conducted on the platform and particularly unraveled the kinetics of iron. A synergetic organic phase made of two extracting molecules (HDEHP and DMDOHEMA) was implemented as the extracting phase, and a rarely performed dual direct and reverse extractions study was carried out. Such dual approach helped defining the true extraction thermodynamic equilibrium values, especially in the case of extremely slow extraction kinetics, which may be mistaken for a no extraction reaction without it. Hence, the iron's thermodynamic equilibrium, which could only be reached for high contact times, appeared to be significantly higher than 30 min whereas the one of ytterbium occurred in less than 1.7 min. Following this extraction study, a new L-L extraction chip was developed to tackle and solve such fast kinetics extractions. We can now explore kinetics with extraction reaction characteristic times down to a few tens of seconds of contact time (lower limit is 14.4 s). With such capability, we showed that the fast ytterbium extraction kinetics could be studied.

Finally, a new application case, enabling the testing of membrane's permeability for metal extraction, was developed, tested and validated. This shows the versatility of such an instrumented and automatized platform toward many great hydrometallurgy case studies.

INTRODUCTION

Solid-phase-extraction (SPE) belongs to the world of chromatography and consists of eluting a mobile -liquid -phase containing dissolved species through a stationary -solid -phase.

The aim of such a process is to remove one or more compounds from the continuous liquid phase by taking advantage of their affinity towards the solid they are in contact with. SPE processes are mainly implemented for chemical, pharmaceutical, food or cosmetic purposes [START_REF] Zhang | Solid-phase microextraction coupled to LC-ESI-MS/MS: Evaluation and correction for matrix-induced ionization suppression/enhancement for pharmaceutical analysis in biological and environmental samples[END_REF][START_REF] Zhang | Depth-profiling of environmental pharmaceuticals in biological tissue by solid-phase microextraction[END_REF][START_REF] Vuckovic | Automated solid-phase microextraction and thin-film microextraction for high-throughput analysis of biological fluids and ligand-receptor binding studies[END_REF] . Well-known processes based on SPE, such as HPLC, aim at highly purifying solutions by removing unwanted impurities. However, one of the drawbacks of SPE lies in the solid-phase, which is the most expensive part of the process, as it needs to be renewed regularly because its efficiency decreases from one extraction cycle to another. This, added to the considerable time needed to develop a process, deters efforts to optimize SPE.

The metal SPE processes considered in this report are based on a three step cycle. Each step is followed by an intermediate wash using a neutral liquid solution (usually DI water), which removes the previously eluted solution from the solid phase enclosure. The first step of a cycle is the elution of the liquid phase to be purified, where species are interacting with the solid phase. This step is then followed by the desorption of the captured compounds and finally, the last step of the cycle, which enables a regeneration of the solid phase for the next cycle. Consequently, one may foresee that many parameters must be taken into account when implementing and optimizing such a process 124 : flowrates of the solutions to be eluted, desorption solution, regeneration solution, etc. Finding the optimal parameters for a given SPE is a long-standing effort, which requires non-negligible upstream studies. For instance, this is of particular interest when dealing with metal recycling SPE processes involving REEs and Iron. Indeed, lots of work has been done in the past few years focusing on the recovery of the elements commonly found in magnets, e.g. Neodymium-Iron-Boron magnets (NdFeB) [125][126][127][128][129][130][131][132] or electronic components that can be enriched in REEs, such as Nd-based capacitors [START_REF] Xia | Sustainable route for Nd recycling from waste electronic components featured with unique element-specific sorting enabling simplified hydrometallurgy[END_REF] .

Most of these previous studies deal with SPEs followed by time consuming and/or expensive offline analysis of their output, using either ICP-OES 133 , XRF 134 or liquid chromatography coupled with Mass Spectrometry (MS) 135,136 . The drawbacks of these offline measurements are that they: (i) require a larger sample size; (ii) may need further sample preparation and;

(iii) may destroy the sample.

Throughout this chapter, the upgraded XRF integrated millifluidic platform is presented in detail. To illustrate its performance, the application case of the SPE of lanthanum, neodymium, ytterbium and their separation from iron is reported. The kinetics of extraction will also be compared to theoretical models that are commonly used in such extraction process. Finally, as an acidic cation exchange fiber was implemented in this study, a pH measurement of the output liquid during the adsorption step was also conducted. Indeed, H + protons are released when exchanged with metal ions and can serve as a tracer of the stationary phase exchange capacity.

This chapter is mostly based on the article "On-line

Solid phase

The solid phase refers to the METALICAPT®-MFC-11 cation-exchange fiber with sulfonic acid (SO3H) functional groups purchased from AJELIS SAS. The fiber was delivered already wrapped in the chromatographic column. Due to the hygroscopic nature of REEs' nitrate salts, each of their solutions needed an initial calibration using ICP measurements to accurately determine their concentrations. All masses and final standard concentrations obtained via ICP ‡ ‡ ‡ An HNO3 acidic medium was chosen so it could best replicate real leaching solutions coming from e-waste acid treatment.

X-ray analysis parameters and associated calibration

Similarly as previously described in Chapter 2, the XRF spectra acquisition conditions were optimized to obtain the highest S/N ratio when measuring lanthanum, neodymium, ytterbium and iron: 20 kV at 150 µA for the X-ray generator, an integration time of 120 s and a combination of two 50 µm-thick filters, one made of titanium and the second of silver.

The quantities required for the calibration procedure in the new geometry adopted for this work are recalled in Annex 7.

EXTRACTION RESULTS

Solid phase extraction results

Thanks to the integrated online XRF measurement, a full SPE process with periodic measurements (every 120 s) of all metals concentrations within the output solution took only 4.5 h and accounted for 100 mL. A usual measurement of the concentrations by ICP-OES would have more than doubled the total time for one extraction (taking into account the preparation of the solutions to be sent for ICP-OES, followed by their analysis) and would have resulted in nearly ten times more liquid waste.

Firstly, an SPE involving an adsorption solution of 30 mM of La(III), Nd(III), Fe(III) and Yb(III) in 0.1 M HNO3 was implemented in order to get an idea of how the adsorption process was behaving between these four metal elements and H + . The graph presented Figure 46 shows the evolution of the metal ion concentrations, followed by online XRF during the three process steps, and the pH during the adsorption step. This first SPE experiment was cycled four times to check the result repeatability. Graphs placed in Annex 8 display similar adsorption and desorption curves for each of the four cycles, which confirms that there is no modification undergone by the fiber after several cycles.

or a strongly acidic one (with all the other experimental parameters fixed) would lead to different extraction efficiencies 144 . Indeed, the interactions between a binding site on the fiber and a proton will differ because of their different acidic constants, resulting in final different selectivity competitions between all the involved species.

At the end of the adsorption step, the output metal concentration was measured as equal to the input concentration (30 mM) indicating that all adsorption sites of the cation-exchange fiber were occupied: the fiber was saturated and no more interaction between the eluting liquid and the fiber occurred. After the washing step with DI water to rinse out the adsorption solution, the desorption step showed four peaks, one for each element. The highest peak refers to La(III) with a maximum at 120 mM and, as the area under the desorption curve is proportional to the molar quantity of the adsorbed species on the fiber, La(III) was consequently the most adsorbed element, compared to the others. Indeed, lower peak maxima were measured for Nd(III) at 95 mM, Yb(III) at 50 mM and finally Fe(III) at a little more than 40 mM.

The fact that Fe(III) had the smallest desorption peak can be related to the 60 mM peak maximum for Fe(III) concentration at 4.5 BV during the adsorption step. Indeed, such a peak can be explained as coming from the competition between each element versus the binding site on the fiber 145 . Calculations, placed in Annex 9, show that the amount of adsorbed iron is effectively equivalent to the total desorbed Fe(III) during both the adsorption peak at 4.5 BV and the desorption peak. Therefore, we can conclude from the observation of this 4.5 BV iron peak that some Fe(III) desorption was induced by its lower affinity with the fiber when compared to La(III), Nd(III) and Yb(III), during adsorption. To a lesser extent, this is also observed for Yb(III), with a broad peak centered at ≈5 BV.

Retention capacity of the fiber

Definition

The obtained desorption curve also enables a determination of the total exchange capacity, or retention capacity, of the fiber allowing a comparison with the commercial value given by the manufacturer (3-6 eq/kgfiber). The calculation of this exchange capacity is determined for each species and then summed in order to get the total capacity. The retention capacity is given by Equation ( 6) as follows:

𝑐 , = 𝑒𝑞 𝑚 ( 6
)
Where eqi (mol) is the molar equivalent, which refers to the molar quantity of adsorbed species 𝑖 on the fiber, determined by the product of the area under the desorption curve for element 𝑖 by the cationic charge (+3 for every element included in this study) and 𝑚 (kgfiber) is the mass of the fiber wrapped in the column.

Calculation of desorbed quantity of metals

In order to determine the retention capacity for each element, a calculation of the area under the desorption curve is necessary. Figure 47 shows graphically the area that was considered for La(III), Nd(III), Yb(III) and Fe(III).

Results

Table 10 gives the retention capacities for La(III), Nd(III), Yb(III) and Fe(III). Overall, the total retention capacity of the fiber is of the same order of magnitude, although slightly smaller than the value of 3-6 eq/kgfiber given by the manufacturer. This is due to the low pH at which we were working, which favors the protonation of the binding sites instead of the exchange of a proton by a metallic cation.

EXPERIMENTAL DATA COMPARED TO THEORETICAL MODELS

The performance of a fixed-bed column assessed experimentally can be compared with adsorption mechanism theoretical performance. Usually, only the curve obtained during the adsorption step, called the breakthrough curve, is modelled and evaluated with the implemented experimental parameters (flowrate, bed height, initial concentration).

Throughout this SPE study, several theoretical models were tested (Adams-Bohart 146 , Liu 147 , Thomas 148 , Yoon-Nelson 149 ). Among these models, only the Thomas and Yoon-Nelson models are presented below because of their closest fit with our experimental data. The Adams-Bohart and Liu models are placed in Annex 10.

Thomas model

The Thomas model is the most widely used mathematical model in the prediction of breakthrough curves. This model is based on a Langmuir equilibrium, second-order reversible kinetics and a plug-flow-based flow system 148 . External and internal diffusion resistances are ignored in this model. The Thomas model is expressed by the following Equation ( 7):

𝐶 𝐶 = 1 1 + exp 𝑘 , 𝑞 , 𝑚 𝑄 -𝑘 , 𝐶 𝑡 (7)
Where 𝐶 and 𝐶 (g/L) are the concentrations of the species 𝑖 at time 𝑡 and 𝑡 , respectively, 𝑘 , (mL/(min.g)) is the Thomas rate constant of the species 𝑖, 𝑞 , (mg/g) is the adsorption capacity of the fiber of the species 𝑖, 𝑚 (g) is the mass of the fiber wrapped in the column, 𝑄 (mL/min) is the liquid flowrate and 𝑡 (min) the time.

Yoon-Nelson model

The Yoon-Nelson model is based on the adsorption theory and breakthrough of adsorbate probability 149 . This model is described by Equation ( 8):

𝐶 𝐶 = 1 - 1 1 + 𝐶 𝑄𝑡 𝑞 , 𝑚 (8)
Where 𝐶 and 𝐶 (g/L) are respectively the concentration of the species 𝑖 at time 𝑡 and 𝑡 . 𝑄 (mL/min) is the liquid flowrate, 𝑡 (min) the time, 𝑞 , (mg/g) is the adsorption capacity of the fiber of the species 𝑖, 𝑚 (g) is the mass of the fiber wrapped in the column and 𝑎 is an empirical parameter determined experimentally of the species 𝑖.

Results

Linear regressions of these two models were performed in order to determine the parameters kT and qT for the Thomas model and a and qYN for the Yoon-Nelson model. Besides, only the experimental data of lanthanum and neodymium were used, because both iron and ytterbium display a desorption peak during their respective breakthrough curve. Table 11 gives the values of the Thomas and Yoon-Nelson parameters as well as their corresponding R-squared value.

However, neither model led to a good fit of the experimental data. This could be interpreted as due to the use of a multi-component extraction system. Indeed, if some reports showed a good accuracy of the Thomas model when dealing with extractions of multiple ions 150,151 , a majority of studies also pointed out unfitting data in such multi-species cases for the Thomas model 152-155 .

Adapted Thomas model

Although experimental and modelled data were not fitting, one might notice a similarity in the shapes between the experimental and the modelled curves. In this regard, a closer look at the existing literature was taken to see if any study had ever dealt with the modification of either the Thomas or the Yoon-Nelson model. This search led to the work of Yan et al. where they proposed a new Thomas model 154 , that was even confirmed by Lima et al. 155 . However, this Yan model based on the Thomas model still could not fit our data (see Annex 10). Hence, a phenomenological approach was considered by adding a corrective coefficient to the two models. While this attempt failed for the Yoon-nelson model, a theoretical adjusted Thomas model was found to fit our experimentally obtained breakthrough curves. Equation ( 9) gives the updated equation from the initial Thomas model.

𝐶 𝐶 = 1 1 + exp 𝑘 , 𝑞 , 𝑚 𝑄 -𝛂 𝐢 𝑘 , 𝐶 𝑡 (9)
Where α = 1.6 and α = 2.1.

In Equation ( 9), an apparent rate constant under the expression 𝑘 , = 𝛼 𝑘 , (𝑖 = La(III) or Nd(III)) reflects a new rate constant for this adapted Thomas model. The fit for this model for both Lanthanum and Neodymium is also plotted Figure 49.A) and Figure 49.B), respectively.

To further test the applicability of this modified model, an SPE involving a single REE (Nd) was performed, and the data fitted well with the proposed modified Thomas model (see Annex 11). For this single ion extraction, the alpha value was adjusted at 𝛼 = 2.6, which differs from the one obtained for the multi-ion extraction, where 𝛼 = 2.1. Therefore, the modified Thomas model presented herein does not only stem from the implementation of a multi-ion extraction system, it also derives from the fact that a synthetic fiber is implemented, with a far higher exchange surface area than conventional ion-exchange resin 156-161 . Moreover, Liu et al. also reported that an unsmooth interface such as our fiber may have an effect on the existing predicted models 162 . With such fibers, cation diffusion to the binding site plays little role, hence allowing for faster sorption kinetics. This could explain why such kinetic observations have seldom been reported, as well as the need to adjust the theoretical modelling as shown by Vuorio et al. 157 .

(0.1 M and 2 M). Indeed, the impact of HNO3 concentration on the separation was also studied: HNO3 solutions at 0.1, 1 § § § and 2 M were tested.

To study the impact of different Fe:Cit molar ratios on Fe(III) adsorption, a calculated and pipetted volume of 3 M Cit was added to 55 mL of a 30 mM La(III), Nd(III), Yb(III) and Fe(III) HNO3 mixture (55 mL corresponded to the volume to be injected in the column during the adsorption step). Table 12 presents the elemental concentrations calculated after the addition of the citric acid solution and the associated dilution. Fe:Cit molar ratios were first experimentally optimized prior to studying the role of HNO3 concentrations. The amount of desorbed Fe(III) was determined by varying the Fe:Cit = 1:X molar ratios (where X = 1, 2, 4, 6, 10 and 20). The results obtained, and displayed in Figure 51, indicate that the molar quantity of desorbed Fe(III) decreases when the Fe:Cit molar ratio increases. Hence, in order to get the least adsorption of Fe(III) on the fiber, a Fe:Cit molar ratio of 1:20 and a 0.1 M HNO3 were determined to be best. § § § 2 M HNO3 standard solutions were used to prepare 1 M Rare-Earth Elements and Fe mixtures.

PRELIMINARY RESULTS OF SOLID-PHASE EXTRACTION WITH A REAL LEACHING SOLUTION FROM WASTE CPU

INTRODUCTION

A CPU, or Central Processing Unit, is the primary component of a computer that performs most of the processing and calculations that enable a computer to function. It is often referred to as the "brain" of the computer embedded in its PCB. Regarding the metal composition of a CPU, its metallic content with associated concentrations is similar as a PCB: a high variety of chemical elements present in a wide range of concentrations. Several studies determined that Cu, Ni, Sn, Al can be present at dozens of wt.%, while Au, Ag, Pd or Pt may represent up to only hundreds of ppms [168][169][170] .

Hydrometallurgical recovery of these metals coming from leached PCBs are being developed and some studies particularly focus on the recycling of Au by implementing different techniques such as electrolysis 168,171,172 , preferential leaching 169 or L-L extraction 173,174 . However, these methods either lack of Au selectivity versus other metals such as Cu, generate high amount of liquid waste or require expensive raw materials (CAPEX) 175 . Another chemical process that is gaining interest is SPE, which can benefit from a high selectivity depending on the selected solid phase and which CAPEX can be greatly amortized thanks to the regeneration and then reuse of the solid phase. Some studies reported SPE of Au from waste CPUs, where either commercialized or synthesized bead resins were chosen as the solid phase 176,177 . Cyganowski et al. reported for instance an 87 % Au removal from the initial leaching liquor and reached to fully desorb all Au ions that was adsorbed on their implemented core shell resin 177 .

The purpose of the study reported herein consisted of developing a SPE process for the easy separation of gold from mainly copper among the other elements that are present in a CPUbased leaching solution. Instead of implementing commonly-used ion-exchange beads, one of AJELIS fiber was used as the solid phase.

MATERIALS AND METHODS

This work was jointly conducted with Dr. Yamuna Jayaraman, research fellow working in the SCARCE Lab in Singapore. Her work aimed at recovering metals found in Ceramic Dual In-line Package (CERDIPS) of Integrated circuits (IC) or Central Processing Units (CPUs). The former is an especially difficult case to deal with directly by hydrometallurgy due to the particularly high value of the Cu/Au ratio. Hence, Dr. Y. Jayaraman developed an experimental protocol at the laboratory scale, consisting of a proprietary leaching of CPUs to improve the selectivity of the metal lixiviation in an aqueous medium. After the final step of the process, an aqueous solution is obtained, which is proved to be concentrated in copper, tin and nickel with a relatively small but maximized amount of gold (see section 3.4.2.1 below).

Solid phase

Given the aqueous matrix of the leaching solution, gold and copper complexes were most likely displaying an overall negative apparent charge. Indeed, when implementing these elements and the acids that were used in the leaching step in the Hydro Medusa software (described previously section 3.3.4.1), [Au(S2O3)2] 3-and [Cu(S2O3)3] 5-are complexes that are likely to exist in the acid mixture. Hence, an anionic-exchange fiber (METALICAPT®-MFH11; Ammonium and tertiary amine functional groups) from AJELIS was chosen as the solid phase for this solid-liquid separation (see Figure 54).

FIRST EXTRACTION RESULTS

ICP-OES analysis was performed on liquid samples taken at different steps of the extraction: initial leaching solution, liquid after adsorption, liquid after DI water cleaning, liquid after desorption. Analyzing the washing solution was implemented to check if pure water could act as a desorption agent towards some metals.

In the following results, only gold concentration is analyzed **** . As reported in Table 14, the concentration of Au in the liquid after the adsorption step is null, which means that all the Au that was leached out from waste CPU was adsorbed by the anionic-exchange fiber. The total amount of adsorbed Au corresponded to 8.3.10 -1 ± 0.1 mmol. Besides, after both DI washing and desorption steps, Au concentration still remained null, which showed that 4 M NaOH was either not concentrated enough or was not the appropriate solution to desorb Au complex anions.

After washing step 0

After desorption step 0

Moreover, the XRF analysis of a part of the fiber after it underwent a full SPE cycle displayed distinct 𝐿 Au peaks, which confirmed the presence of Au in the fiber (see Figure 56). Besides, sharp 𝐾 Cu peaks are also present on the XRF spectrum, which showed that a large amount of copper (area under the curve is proportional to the metal concentration) was still not desorbed. However, one should note that a small Fe peak remains on the spectrum (which was not measured in the initial CPU leaching solution). Hence, if other metal ions had been adsorbed on the fiber during the adsorption step, the 4 M NaOH solution reached to desorb them all. This indicates that the separation problem mainly concerns two elements: gold and copper.

**** Indeed, concentration results of metals such as copper and tin led to inconsistent results. Because of time constraints independent to Dr. Yamuna, the SPE could not be performed a second time.

† † † † Negative concentration given by ICP-OES.

have a stronger affinity than other ions (such as copper), fiber gold enrichment would be possible. Indeed, in batch mode, there is a time when all Au ions are adsorbed on the fiber and hence, there is no more driving force for new Au ions to replace other adsorbed metal ions. Regarding the desorption of a supposedly 100 % Au concentrated fiber, other aqueous solutions might be used instead of NaOH. The main feature of such a solution would be to have stronger affinity with gold complexes than the solid phase with gold ions.

CONCLUSION

This chapter presented a newly implemented millifluidic module enabling the study of SPE processes. The development of this extraction brick took advantage of the existing microfluidic L-L extraction platform, but was upgraded to enable larger values of liquid flowrates. The SPE part of the platform was designed so it could welcome a chromatographic column where the solid phase is placed in. The downstream XRF analysis module was also adapted to a single phase analysis process with higher flowrates than those used in microfluidics.

After setting up the millifluidic platform, a first SPE study was carried out with a mixture of REEs and iron. An SPE cycle consisted of three steps: adsorption of the metals, washing of the column and desorption of the metals. The flowrates and acid concentrations used allowed the kinetics of adsorption and complete desorption of the adsorbed metal ions to be followed. Following these experimental results, several theoretical models were studied and compared with the experimental curves. As none of the selected models fitted, the equations of the Thomas and Yoon-Nelson models were adapted as they showed the closest trend to the experimental data. This method of modifying a theoretical model is something that had already been used in other reported SPE studies such as the one of Yan et al., who developed a breakthrough curve model originally based on the Thomas model 154 . It was finally found that adding an additional parameter specific to a chemical element in the Thomas equation could lead to a close fit. However, the explanation for such a parameter remains phenomenological. Following the implementation of the SPE of REEs and iron, a separation process was developed to remove iron from the cationic mixture. Citric acid, used as a chelating agent, was selected as a suitable candidate to selectively modify the apparent charge of iron complexes. Several SPE cycles were performed, each time with a different Fe:Cit molar ratio, allowing easy determining of the optimal citric acid content in the initial metal mixture to avoid Fe sorption on the fiber during the adsorption step. Finally, a Fe:Cit molar ratio of 1:20 was found to be the optimized ratio (among those tested) to obtain the minimum amount of desorbed iron in the desorption step, i.e. sorbed Fe in the adsorption step. This kind of SPE study is generally conducted in batch mode [178][179][180] , where only the steady-state adsorption and desorption solutions are analyzed. Although the liquid volumes used for the extraction (without considering the volumes required for concentration analysis) are similar to the ones used on the platform (≈100 mL), batch-conducted experiments do not resolve the kinetics. Some studies that analyzed the impact of a chelating agent on the separation efficiency implemented in a chromatographic column reported a liquid waste generation up to more than 1 L 181,182 . This shows how liquid waste efficient the developed platform is.

Finally, a SPE study was conducted on a real CPU-based leaching solution. The preliminary tests reported in this PhD thesis were carried out in batch mode with liquid analysis using ICP-OES. Initial results showed a high affinity of Au ions to the anionic exchange fiber, which showed to be better (100 %) than the one reported in the literature using the same type of extraction (87 %) 177 . Further investigation and experimentation would be required to develop a complete and comprehensive separation process for the recovery of gold among all the metals present in CPUs.

Chapter 4: Mixing in microfluidics, a new module added to the platform

INTRODUCTION

Mixing in microfluidics is a research topic that has triggered much attention since the emergence of microfluidic technologies. Because of the micrometric dimensions of the channel a given liquid is flowing through, its Reynolds number (Re) given by Equation (10) will most of the time be characteristic of a laminar flow (i.e. less than 100). Indeed, its calculation is not only based on the channel dimensions but also on the flowrate of the liquid, which does not usually exceed a few dozens of microliter per minute.

𝑅𝑒 = 𝜌𝑢𝑙 𝜂 ( 10
)
With 𝑙 = , the hydraulic parameter of a rectangular channel

Where 𝜌 is the volumetric mass density, 𝑢 is the fluid velocity inside the channel, 𝑙 is the hydraulic diameter of the channel, 𝐴 is the cross-sectional area of the flow, 𝑃 is the wetted perimeter of the cross-section and 𝜂 is the fluid dynamic viscosity.

Therefore, mixing is in most cases conducted by inducing a disturbance of the fluids to be mixed together, as it leads to a higher Re and consequently a more efficient mixing. At the laboratory scale, many mixing methods exist such as orbital shakers or magnetic/mechanic stirrers. At the industrial level, an entire science has been developed on mixing processes, which now represents an operation unit by itself. Overall, all these processes are characterized by high advection mechanisms, which are resulting from the turbulence created in the fluid volume. In microfluidics, however, transport of species is mainly driven by diffusion, which is much slower than advection. The Peclet number (Pe) compares the advection over diffusion for a given species in a specific medium. Its calculation through Equation ( 11) enables a fast and easy determining of the dominant transport mechanism (either advection or diffusioncontrolled). In microfluidics, an aqueous phase concentrated of metal ions can be characterized by a Pe ≈ 10 .

𝑃𝑒 = 𝑢𝑙 𝐷 (11)

Where 𝑢 is the fluid velocity inside the channel, 𝑙 is the hydraulic diameter of the channel and 𝐷 is the diffusion coefficient of species 𝑖 in fluid medium 𝑚.

Molecular transport controlled by diffusion is led by the characteristic diffusion length 𝛿𝑙 of the species, also known as penetration length, which is calculated according to Equation (12). As the diffusion coefficient 𝐷 is a constant at a given temperature and species 𝑖 in medium 𝑚, only the contact time -or time residency of fluid -𝑡 can vary the overall diffusion length.

𝛿𝑙 = 𝐷 𝑡 (12)

Microfluidic mixing is a technology that is used for a wide range of applications. In chemical synthesis, microfluidic devices allow for precise control over reaction conditions, leading to more efficient and selective reactions [183][184][185] . Microfluidic mixing is also used in drug discovery to screen large libraries of compounds and identify promising drug candidates 186,187 . For diagnostics, microfluidic devices can detect specific biomarkers in biological samples, such as blood or saliva, or to diagnose diseases like COVID-19 [188][189][190][191][192][193] . Additionally, microfluidic mixing is used in bioanalysis to detect and quantify biomolecules in biological samples, which can help monitor treatment and understand biological processes 194,195 .

Throughout this chapter, the state-of-the-art on mixing implemented in microfluidics is drawn. Thanks to the insight given by the literature, a new online and upstream mixing module was designed and tested. Its main objectives were to make L-L extractions faster, more efficient, and with less human interventions during the processes' study. The validation of the mixing design was performed with a homemade Fourier Transformed Infrared Spectroscopy (FTIR) module. This patented setup can measure precisely the chemical activity of a solvent and was used in this PhD work for the assessment of the mixing efficiency of two solvents flowing through a mixing module.

TWO CATEGORIES OF MICROMIXERS

Passive mixers

Passive mixing relies on microfluidic channels' geometries, with no external addition of any source of energy to induce mixing. The design of the microchannels aims at breaking the dominant laminar flow by increasing the apparent surface of contact as well as the time of contact between the fluids to be mixed. However, measuring chemical concentrations inside a microfluidic channel is a challenge because: (i) concentration measurement shall not impinge on fluid flow, and (ii) fluid at the exit of the microfluidic circuit cannot be used for mixing assessment as it may have undergone mixing mechanisms while flowing towards the exit of the fluidic circuit. Besides, as mentioned in the introduction of this section 4.2.2, non-invasive optical screenings in microfluidic channels are today mainly conducted when following the evolution of a mixing. Thus, the researcher ends up with multiple images usually taken by a high resolution Charge Coupled Device (CCD) camera at different experimental time. Consequently, the mixing index takes into account the standard deviation of the pixels' intensities of the recorded pictures (see Equation ( 14) 213,214 .

𝜂 = 1 - 1 𝑁 𝐼 -𝐼 𝐼 ( 14
)
Where 𝑁 is the number of pixels of the recorded pictures, 𝐼 is the intensity of pixel 𝑖 in the reference picture of a perfect mixture flowing in the microfluidic channel and 𝐼 is the intensity of pixel 𝑖 in the picture of the microfluidic channel taken at a given experimental time.

Color-and fluorescence-based measurement

Most studies dealing with microfluidic mixer performance implement an optical measurement. This analysis is either based on the color of fluids where droplets of ink were diluted in 213,215,216 or on a fluorescent measurement with the use of a fluorescent tracer diluted in one of the two liquids that is flowing in the channel 217,218 . As Figure 62 shows, the common setup enabling such a study is composed of a flow inducer such as a syringe pump, a microfluidic mixing chip, a high resolution camera placed above the transparent microchannel and a computer. This mixing measurement has the advantage of being non-invasive and fast.

To increase the accuracy of such a method, some researchers have also developed calibration procedures as for a real analytical tool 213,215 . Although the obtained results are showing precise mixing efficiencies on the macroscopic level, uncertainty remains on the quality of the mixing down to the molecular scale.

FTIR-based measurement -the molecular scale

Although the definition of a mixing down to the molecular level is a complex task, it may be answered by measuring the chemical activity of the mixture that is studied [START_REF] Oleschuk | Trapping of bead-based reagents within microfluidic systems: on-chip solid-phase extraction and electrochromatography[END_REF] . Chemical activity is a thermodynamic parameter, which gives quantitative information on the interactions, at the molecular level, a given species is sharing with the medium it is diluted in. FTIR spectroscopy is an analysis technique based on binding energies between atoms and we report herein that it can also be used as a tool for the measurement of a solvent's chemical activity by measuring its vapor pressure. The relation between the activity of a solvent and its vapor pressure is given by one of Raoult's law called the tonometry law 219,220 (see Equation ( 15)).

𝑃 = 𝑃 . 𝑎 (15)

With 𝑎 = 𝑥 considering an ideal solution, i.e. with no additional interactions between species compared to those present in the pure compounds

Where 𝑃 is the vapor pressure of the solvent, 𝑃 is the vapor pressure of the pure solvent, 𝑎 is the chemical activity of the solvent and 𝑥 is the molar fraction of the solvent.

At thermodynamic liquid-vapor equilibrium, chemical potentials in the liquid and vapor phases are equal, which leads to Equation ( 16) using Raoult's tonometry law (Equation ( 15)).

𝛾 𝑥 𝑃 = 𝜑 𝑦 𝑃 (𝑇) (16) Where 𝛾 and 𝑥 are the activity coefficient and the molar fraction of species 𝑖 in the liquid phase, respectively, 𝜑 and 𝑦 are the fugacity and the molar fraction of species 𝑖 in the vapor phase and 𝑃 (𝑇) is the saturated vapor pressure of species 𝑖 at temperature 𝑇.

Besides, the Beer-Lambert law gives the relation between the absorbance of gaseous species 𝑖 at wavelength 𝜆 with its concentration 𝑦 (Equation ( 17)).

𝐴 (𝑦 ) = 𝜖 . 𝑙. 𝑦 (17)
Where 𝐴 (𝑦 ) is the absorbance of gaseous species 𝑖 at wavelength 𝜆, 𝜖 is the molar extinction coefficient at wavelength 𝜆 and 𝑙 is the length of the optical pathway.

Consequently, by combining Equations ( 16) and ( 17), chemical activity can be defined as:

𝑎 (𝑥 ) = 𝛾 𝑥 = 𝐴 (𝑦 )𝜑 . 𝑃 𝜖 𝑙𝑃 (𝑇) (18)
As 𝑎 = 1 for the measurement of a pure species, it is possible to determine the chemical activity of the same species in a mixture by dividing Equation ( 18) applied to the species in the mixture by Equation ( 18) applied to the pure species.

𝑎 (𝑥 ) = 𝐴 𝐴 (19)
Where 𝐴 and 𝐴 refer to the absorbance of species 𝑖 in the mixture and of the pure species, respectively.

Literature dealing with mixing performed either in microfluidics or with a macroscale approach and quantified by a measurement of chemical activity is virtually nonexistent. Only Prim et al. reported an online FTIR measurement coupled with a micromixer 221 . In this study, mixing efficiency of the microfluidic chip was determined through the analysis of the conformation of a synthesized protein.

In the following sections, a microfluidic FTIR-based setup initially built and reported by Pénisson et al. is presented [START_REF] Penisson | Water activity measurement of NaCl / H2O mixtures via substrate-integrated hollow waveguide infrared spectroscopy with integrated microfluidics[END_REF] . This setup proved to be reliable for chemical activity measurements and has thus been extensively used during this PhD for mixing related studies described below.

CONCLUSION

We showed throughout this section how the scientific community finally tackled the inherent mixing issue when implemented in microfluidics. From passive to active microfluidic mixing, many technologies are now well studied and some are today commercialized. Most research studies are using one method to assess whether a mixing is "good" or not: optical assessment. However, this technique is insufficient for giving information of the mixing at the molecular scale. That is why the measurement of a mixture's chemical activity enabled by FTIR can help bringing what the current method coming from the state-of-the-art is lacking. In this context, a homemade and patented microfluidic FTIR module is introduced below and further described in the following section.

INTRODUCTION AND PRESENTATION OF THE MICROFLUIDIC FTIR MODULE

Genesis of the module

The aforementioned FTIR microfluidic module was built at the same time as the microfluidic L-L extraction platform was being developed. As previously introduced in section 4.2.2.3, the setup is able to measure precisely the vapor pressure through the measured absorbance of a gaseous species at liquid-vapor equilibrium with its liquid phase, thus allowing the determining of the chemical activity of the liquid species flowing through the module. Indeed, when dealing with L-L extraction system, monitoring the chemical activity provides information about the aggregation number, which is the number of extracting molecules required to extract an ion 222 . In L-L extraction studies presented in this PhD thesis, the organic phase was made of extracting molecules diluted in an organic solvent. Therefore, interactions exist between the extracting molecules and the solvent. When ions are extracted from the aqueous phase to the organic phase, the extracting molecules create aggregates consisting mainly of the ion and several extracting molecules. Consequently, the quantity of nonaggregated extracting molecules in the organic phase decreases. Similarly, the interactions between the organic solvent and the extractant decrease, causing an increase in the activity of the organic solvent. Monitoring the chemical activity then allows determining the aggregation number and provides information on the involved molecular mechanisms.

Description of the module

As Figure 64 outlines, the structure of the FTIR module is composed of three main parts. The lower part consists of a microfluidic channel where the liquid sample flows through. The volume of this channel is 22 µL. Above the channel, an intermediate interconnection part allows the molecules that evaporate from the sample to diffuse towards the analysis hollow waveguide, which is the third part. The interconnection part is made up of (i) a permeable membrane, which is closing the liquid channel from above without altering the liquid-vapor thermodynamic equilibrium, and (ii) above the membrane, a flat piece is drilled with small organized holes allowing the vapor to diffuse towards the optical waveguide. This piece constitutes the lower part of the waveguide and reflects infrared (IR) rays. The drilled holes represent 6% of the open surface at the base of the waveguide. pure DI water system is more mixing efficient because of the concentration gradient driving force linked to the use of non-identical fluids.

Even though ethanol displays intense IR peaks and thus enables easy screening using FTIR analysis, we decided to implement a NaCl solution because (i) of the similarity of the system that will be used in L-L extractions (mixing of aqueous solutions, based on the dissolution of salts) and; (ii) the mixing of two aqueous solutions seems to be more difficult to reach than in the case of two different fluids, according to the study of Wang et al 226 .

Optical mixing assessment

Optical mixing tests were firstly conducted on PMMA microfluidic mixing chips where two dyecolored liquids (yellow pure DI water and blue 75 % NaCl saturated DI water) were injected by a syringe pump at equal flowrates. The very first version of the pearl-chain microfluidic design was based on a serpentine of 6 parallel channels, each one comprising a series of four 3 mm diameter pearls. Optical assessment was conducted using eye (human) vision.

For each microfluidic chip mixing test, three flowrates were implemented: 20, 10 and 1 µL/min. A reference test was performed with a commercial 1-mm-pearl-chain mixing chip purchased from Darwin Microfluidics. As Figure 68.A) shows, distinct yellow and blue unmixed liquids flowing at 10 µL/min are observed in the last pearls of the microfluidic channel before liquid output. However, a homogeneous green solution is obtained in the commercial mixing chip at the same flowrate (see Figure 68.B)). As seen in Figure 68.C), flowrate of 1 µL/min in the mixing chip also leads to an output inhomogeneous green solution. Consequently, the mixing efficiency of the first microfluidic mixing chip version was visually unsuccessful, compared to the mixing that was obtained with the commercial chip.

Therefore, a new version of the pearl-chain mixing chip with a similar design to the commercial mixing chip was manufactured. The new chip displayed smaller pearls than the first chip version, with a diameter of 1 mm. Hence, the new design was closer to the one of the commercial mixing chip and comprised 10 serpentine parallel channels each one having a series of 12 consecutive pearls. Similar liquid flowrates were implemented. As Figure 68.D) and Figure 68.E) show, a homogeneous green solution is obtained in the second pearl-chain channel and in the second to last channel at 1 and 20 µL/min, respectively. Decreasing the flowrate let more time to the fluids to diffuse and mix. Hence, the 1-mm-pearl-chain mixing chip was validated by visual observation for equal liquid flowrate from 20 down to 1 µL/min.

All the microfluidic setup was placed in the temperature-controlled incubator set at 25 °C. The FTIR spectrometer was continuously purged with nitrogen avoiding any airborne species contamination during the analysis. The studied microfluidic mixing chip was connected to the FTIR setup by connecting the liquid outlet of the mixing chip to the liquid inlet of the FTIR module. Initially, the two gas valves that are placed at the nitrogen inlet and gaseous outlet of the waveguide were toggled, closing tightly this gaseous channel. The two liquids were then perfused during 6 h through the microfluidic circuit at a flowrate of 1 µL/min, which was the first flow rate to be studied. This allocated time allows the fluidic system to reach a steadystate flow as well as the full setup thermalisation. After 6 h and while keeping the liquid flow at 1 µL/min, the valves were toggled again, enabling a purge of nitrogen at constant gaseous flowrate. At this time, ten reference spectra were recorded (one recorded spectrum is the average of 100 IR spectra, at a rate of 1 spectrum every 0.6 s). After the recorded tenth spectrum, FTIR measurements of the sample vapors are initiated while keeping the perfusion of the samples at a flowrate of 1 μL/min. Once the number of 20 recorded spectra is achieved, liquid flowrates were then set to 10 µL/min for 60 minutes. The next steps are identical as the ones followed with the 1 µL/min flowrate. Table 15 summarizes the waiting time and recorded spectra for each flowrate. Flowrate waiting times are calculated according to five times of the total microfluidic volume of the overall circuit (approximately 70 µL), corresponding to a steady-state flow. Moreover, preliminary tests were performed to determine the time (i.e. the number of spectra) required for the establishment of the liquid-vapor equilibrium. Results placed in Annex 13 show that the liquid-vapor equilibrium is independent of the liquid flowrate and is reached within 7.5 min. Thus, choice was made to record twenty spectra for each flowrate. Figure 70 shows a pure DI water IR spectrum obtained with the FTIR setup. Two molecules' characteristic footprint can be seen: one from water (H2O) composed of two separated peaks ranging from 1300 cm -1 to 1950 cm -1 (red area) and the other from carbon dioxide (CO2) also composed of two close peaks ranging from 2300 cm -1 to 2400 cm -1 (green area).

1 mol/L (choice was made not to implement the ion concentration used in the previously described experiments, which could have led to higher calculation and simulation time).

Conclusion

In this section, we reported a mixing validation protocol for the development and implementation of a micromixer in the existing microfluidic L-L extraction system. Firstly, optical assessment was conducted. Then, we reported one of the first FTIR-based measurement of mixing performance of a micromixer, based on the IR analysis of the vapor at liquid-vapor equilibrium with its flowing liquid phase. Results show good mixing efficiencies for the designed microfluidic mixing chip, which were also validated with a simulation software.

ULTRASOUND-BASED MIXING CHIP

Previous reported microfluidic mixing designs are based on passive mixing. However during the development of the microfluidic passive-mixing chip, an active micromixer was also designed and set up. Specifically, an ultrasound-based mixing chip is reported in the section. This new mixing chip was directly manufactured in glass (fused silica, jgs2 grade) and with a wafer thickness of 2 mm. These two characteristic parameters were chosen to optimize the diffusion of ultrasound towards the liquid mixing channel. The design of the mixing channel is similar as the one of the L-L extraction chip, described in Chapter 2 (see Figure 74.A)). This specific serpentine design was implemented so it could fit with the piezoelectric transducer featuring a diameter of 23 mm and covering the whole microfluidic channel (see Figure 74.B)). This piezoelectric transducer was electrically connected to an amplifier, which was in turn connected to an oscilloscope that enabled the variation of the voltage and frequency of the electrical signal being sent to the piezoelectric (see Figure 74.C)). Finally, the same hydrophobic/oleophobic PTFE membrane used in previously described passive micromixers was sandwiched between the two glass wafers. Nevertheless, all experimental mixing tests were conducted by setting identical flowrates for the two input liquids. Indeed, if the flowrates were to be unequal, the simulation showed that some backflow of the slowest liquid (and caused by the fastest liquid) may occur in the tested microfluidic designs. However, one key point of our microfluidic L-L extraction is the nitrogen backpressure applied on the aqueous side to avoid any organic droplets in the aqueous channel. Considering the implementation of an online microfluidic mixing directly followed by an L-L extraction may likely lead to fluidic flow issues. Besides, mixing results obtained with a DI water and ethanol system showed a relatively weak mixing at high flowrates. For these reasons, the project of implementing an online microfluidic mixing was abandoned because of its complex applicability to the existing L-L extraction system. calculated according to the expected liquid volume of the mixture (see calculations in Annex 15). The two syringe pumps 1 and 2 (see Figure 83) are perfusing until the total liquid volume is reached. The liquid valve is then toggled so the syringe pump 3 can withdraw the mixed liquid from the mixing tank. When all the mixed liquid is removed, the liquid valve is toggled again and the L-L extraction process can begin: syringe pumps 3 and 4 perfuse the aqueous and organic phases, respectively. While L-L extraction is taking place, the program can start the first steps of the purging mode, i.e. the mixing tank can be filled with DI water for cleaning. When L-L extraction ends, the liquid valve is toggled and the syringe pump 3 can withdraw the purging liquid from the mixing tank. Once the mixing tank is emptied, the liquid valve is eventually toggled and the purge of both aqueous and organic (XRF chip only) channels is done.

CONCLUSION

Mixing in microfluidics is a most studied technique in the broad field of microfluidics, which enables the mixing of species, theoretically only ruled by diffusion at this microscale level. As diffusive mechanisms are characterized by slow transport phenomena, two main types of micromixers have emerged to accelerate microfluidic mixing processes, namely active and passive micromixers. Active microfluidic techniques use an external source of energy to induce an internal mixing of the species in the microchannel. Passive micromixers take advantage of a specific channel geometry resulting in either a chaotic advection phenomenon or the lamination of fluids.

The objective of this work was to build a new upstream mixing module allowing even faster screening of L-L extraction processes. As little modifications of concentration of aqueous or organic phase can have an important impact on the overall L-L extraction, there was a need to design and build a mixing setup that could be precise down to dozens of µmol/L. Besides, the focus was made on the mixing of aqueous liquids (varying either the acid or metal concentration). Firstly, micromixers that are comprehensively described in the literature were thought to be good candidate in the implementation of the mixing module. The first idea was to integrate the mixing part directly with the extraction module within one single microfluidic chip, which was compatible with either an active or passive mixer. Hence, a passive pearlchain mixer was first designed, manufactured and tested. Test solutions made of pure water and NaCl concentrated water were used so they could replicate as much as possible the aqueous solutions that are dispensed in L-L extraction processes. For mixing efficiency assessment, two experimental techniques along with a mixing simulation using COMSOL Multiphysics® were conducted. Among the two experimental analysis, optical assessment was performed by coloring the two aqueous liquids giving information of the mixing at the macro scale. This method of analysis enabled getting a first rough idea of the mixing performance of the designed microfluidic mixing chips. The second method was using a homemade FTIR module, which is a tool that measures the chemical activity of the solvent that is flowing through it, by analyzing the IR spectra of its vapors. This method helps bringing mixing information at the molecular scale due to the nature of the analysis and is one of the first to be reported in this context 221 . This specific setup was used to compare the mixing performance of the designed micromixers with already batch mixed solution as reference. For the mixing of pure water and NaCl concentrated water, it was shown a rather good mixing efficiency when compared to the reference mixing. Eventually, all these results were crossvalidated with a simulation software modelling the macromixing process experimentally carried out. An active micromixer using induced ultrasound was also tested but proved to be unsuccessful.

A new mixing module was consequently imagined and designed, based on a milli-batch mixing. Its working operation was programmed and totally incorporated to the existing code enabling the control of the microfluidic platform. A full L-L extraction study has not been performed with this new module yet.

GENERAL CONCLUSION

The recovery of metals from waste PCBs is a complex task due to the wide variety of chemical elements found in PCBs and their high concentration range. Hydrometallurgical processes consist of dissolving the metals to be recovered and can specifically address this issue of metal stream complexity due to their highly selective properties. However, the development of such processes on a batch scale for likely future scale-up and industrialization can be both very time consuming and result in large amounts of liquid waste due to the number of experiments that need to be carried out to optimize the process. In fact, the efficiency of metal extraction processes depends on several parameters such as the concentration of the metal ions, the nature and concentration of the aqueous medium in which they are dissolved, the characteristics and properties of the extraction phase, and the temperature. For this reason, a fully automated and instrumented platform has been developed to easily carry out and study chemical processes, taking advantage of downstream online analysis based on XRF.

L-L extraction processes involve an aqueous phase loaded with metal ions as in leaching solutions of ECs from waste PCBs, and an organic phase aimed at extracting metal ions concentrated in the immiscible aqueous liquid. Such a process was the first to be implemented on the platform using microfluidics. Several parts have undergone some material and design updates, extending the range of use of high concentration aqueous acids and organic solvent phases. Among these updates, the L-L extraction microfluidic chip material moved from a PMMA polymer to glass and the XRF microfluidic chip from 3D printed PLA to polyimide tubes.

The L-L extraction study reported in this PhD thesis not only showed that thermodynamic data can be easily obtained, but demonstrated that new and comprehensive kinetic data can also be extracted, providing unique insights into the kinetic mechanisms involved. Such data, which cannot be obtained from batch-conducted experiments, not only brings some new fundamental understandings of the processes but it also represents key information to convince any industrial regarding the extrapolation for the process upscaling. Throughout the PhD work, we also demonstrated the versatility of such a microfluidic extraction chip, which can also be used as a membrane testing tool for metal recycling and separation applications. When future extraction processes will be implemented on the platform, membrane-based metal separation studies will greatly benefit from the online measurement, helping to faster optimize the process parameters. Furthermore, when modelled, our direct/reversed extraction follow up of both channels have brought never reported before results, such as the interface resistance anisotropy.

SPE processes have also been implemented on the platform thanks to a newly designed extraction setup. This type of extraction involves an aqueous phase, similar to the one used in L-L extractions, and a solid phase on which metal ions are sorbed. The carried out study enabled the quick and easy screening of the kinetics of both adsorption and desorption profiles of REEs and iron as well as a comparison of the experimental breakthrough curves with commonly used theoretical adsorption models. Since the experimental data did not fit with classical models, a new and adapted phenomenological model was successfully developed and fitted well. Such a fast approach also able to unlock crucial kinetics data, will likely be key enablers for the development of separation processes based on Simulated Moving Bed Chromatography, which is the ultimate goal of this project's roadmap. Finally, a separation process of iron from REEs was developed and optimized, taking full advantage of the instrumented platform. A preliminary SPE study was also started off-platform using a real leaching solution from waste CPUs. First results associated to this study showed great potential for a full gold recovery.

The processes implemented on the platform can also benefit from an upstream sample preparation module, leading to full automation of the platform without human intervention throughout the process study. This mixing module was initially designed to be implemented online and embedded in a modified extraction chip. Optical, FTIR-based and computersimulated mixing tests were performed to assess and control the mixing efficiency of the selected microfluidic mixing designs. Although these methods showed relatively good mixing efficiencies, the microfluidic solution was not further selected due to likely flow problems when working online with the rest of the L-L extraction setup (non-identical flow rates, nitrogen back pressure). Therefore, a new milli-batch mixing module was designed, developed and integrated to the platform instruments' ecosystem. The next step for this module will be to include it in an L-L extraction study, allowing easy variation of either acid or metal concentration in the aqueous liquid stream injected into the microfluidic extraction chip. Now that the platform can handle and withstand almost any type of acid and organic solvent, this paves the way for virtually unlimited chemical extraction processes. Of particular interest will be the development of new extraction and separation processes on the platform using real leaching solutions from e-waste. This will greatly benefit the issue of highly variable inputs of e-waste streams that require fast hydrometallurgical process development. The solid phase separation performed with a CPU-based leaching solution is the very first step of all the likely extraction studies to be performed on the platform. As the platform is currently being replicated in the SCARCE laboratory, the setup represents a truly useful laboratory tool for any researcher developing hydrometallurgical metal recovery processes. Apart from chemical separation processes, the FTIR module has also undergone several material updates of its constituent parts for a specific reason. Thanks to the new materials in which the various parts were manufactured, the instrument is now able to measure highly concentrated nitric acid vapors, thus allowing the monitoring of their associated chemical activity, which has an application in the nuclear sector.

ANNEX 10: ADSORPTION THEORETICAL MODELS OF ADAMS-BOHART, LIU AND YAN

Along with the Thomas and Yoon-Nelson models, the models of Adams-Bohart and Liu were tested and compared.

Adams-Bohart model 146 :

The Adams-Bohart model refers to the following Equation ( 24):

𝐶 𝐶 = exp k , C t - k N , 𝑄 (24)
Where 𝐶 and 𝐶 (g/L) are respectively the concentration of the species 𝑖 at time 𝑡 and 𝑡 . 𝑄 (L/min) is the liquid flowrate, 𝑡 (min) the time, k , (L/(g.min)) is the Adams-Bohart constant of the species 𝑖, N , (g/L) is the saturation concentration of the species 𝑖. 21 summarizes the parameters obtained for the Adams-Bohart model. However, the concentrations calculated with these parameters are diverging for both La(III) and Nd(III).

Liu model 147 :

The Liu model refers to the following Equation ( 25):

𝐶 𝐶 = 1 -exp - 𝑘 𝐶 𝑡 𝜏 ( 25
)
Where 𝐶 and 𝐶 (g/L) are respectively the concentration of the species 𝑖 at time 𝑡 and 𝑡 . 𝑡 (min) is the time, k (m 3 solution/(m 2 fiber.min)) is the adsorption coefficient of the species 𝑖, τ (min) is the desorption time.

ANNEX 15: EQUATIONS FOR FLOWRATES' CALCULATION MADE IN THE MIXING PROGRAM

The two input aqueous liquids' flowrates are calculated according to a system of two equations (see System (1)).

( Where 𝐶 and 𝐶 denote the concentration of the most concentrated, respectively diluted, aqueous solution to be mixed, 𝑄 and 𝑄 denote the concentration of the most concentrated, respectively diluted, aqueous solution flowrate, 𝐶 is the concentration targeted after obtaining a liquid mixed volume of 𝑉 and 𝑡 is the time allocated for the mixing process.

By reorganizing System (1), it is possible to isolate the two liquid flowrates as detailed in System ( 2):

(2) ⎩ ⎨ ⎧𝑄 = 𝑉 𝑡 . (1 - 𝐶 -𝐶 𝐶 -𝐶 ) 𝑄 = 𝑉 𝑡 . 𝐶 -𝐶 𝐶 -𝐶
In this system, all the parameters are fixed but 𝑉 can be calculated according to the volume of liquid which will be used for the following L-L extraction. Equation ( 1) details its calculation.

𝑉

= 𝑄 . 𝑡 ( 28
)
Where 𝑄 is the ith flowrate implemented in the L-L extraction process and 𝑡 is the time required to reach the steady-state of extraction (i.e. five times the total fluidic volume) and then to record 10 XRF spectra.

ANNEX 16: VALIDATION PROTOCOL OF THE MILLI BATCH MIXING MODULE

The following detailed protocol was developed for the validation of the newly built mixing module. DI water and 75 % NaCl saturated DI water are dispensed in the mixing tank using two distinct syringe pumps. The magnetic stirrer is set at 1200 rpm. Randomized DI water and 75 % NaCl saturated DI water flowrates are chosen, leading to randomized calculated mixed volumes. Pairs of flowrates and total volume mixtures are tabulated Table 24. A fixed 30 min mixing preparation time is set. After 30 min, the entire mixed liquid is withdrawn with another syringe pump and then sent to the FTIR module for mixing assessment. Batch mixtures are then compared to the ones obtained in the mixing tank. For this, pipetted volumes of DI water and 75 % NaCl saturated DI water (calculated according to the associated flowrate) are added in a centrifuge tube. The centrifuge tube is then placed on a vortex for 60 s. The mixture is finally sent for mixing analysis in the FTIR module. L'extraction en phase solide (SPS) appartient au monde de la chromatographie et consiste à éluer une phase mobile -liquide -contenant des espèces dissoutes à travers une phase stationnaire -solide. Un tel procédé a pour but d'éliminer un ou plusieurs composés de la phase liquide continue en profitant de leur affinité vis-à-vis du solide avec lequel ils sont en contact. Les procédés SPS sont principalement mis en oeuvre à des fins chimiques, pharmaceutiques, alimentaires ou cosmétiques. Les procédés bien connus basés sur la SPS, tels que la HPLC, visent à purifier fortement les solutions en éliminant les impuretés indésirables. Cependant, l'un des inconvénients de la SPS réside dans la phase solide, qui est la partie la plus coûteuse du procédé, car elle doit être renouvelée régulièrement car son efficacité diminue d'un cycle d'extraction à l'autre. Ceci, ajouté au temps considérable nécessaire pour développer un procédé, décourage les efforts d'optimisation de SPS.

RESUME EN FRANÇAIS

Les procédés SPS métalliques considérés dans ce rapport sont basés sur un cycle en trois étapes. Chaque étape est suivie d'un lavage intermédiaire à l'aide d'une solution liquide neutre (généralement de l'eau déionisée), qui élimine la solution préalablement éluée de l'enceinte en phase solide. La première étape d'un cycle est l'élution de la phase liquide à purifier, où les espèces interagissent avec la phase solide. Cette étape est ensuite suivie de la désorption des composés captés et enfin de la dernière étape du cycle qui permet une régénération de la phase solide pour le cycle suivant. Par conséquent, on peut prévoir que de nombreux paramètres doivent être pris en compte lors de la mise en oeuvre et de l'optimisation d'un tel procédé : débits des solutions à éluer, solution de désorption, solution de régénération, etc. Trouver les paramètres optimaux pour un SPS donné peut se révéler être un travail de longue haleine, nécessitant des études en amont non négligeables. Par exemple, cela est particulièrement intéressant lorsqu'il s'agit de procédés SPS de recyclage de métaux impliquant des éléments Terre Rare (TR) et du fer. En effet, de nombreux travaux ont été réalisés ces dernières années en se concentrant sur la récupération des éléments que l'on trouve couramment dans les aimants.

Le développement de cette nouvelle brique d'extraction a tiré parti de la plateforme d'extraction microfluidique L-L existante, mais a été mise à niveau pour permettre l'utilisation de débits liquides plus importants. La partie SPS de la plateforme a été conçue pour pouvoir accueillir une colonne chromatographique dans laquelle est placée la phase solide. Le module d'analyse par fluorescence X aval a également été adapté à un procédé d'analyse monophasique avec des débits supérieurs à ceux utilisés en microfluidique.

Après la mise en place de la plateforme millifluidique, une première étude de SPS a été réalisée avec un mélange d'éléments TR et de fer. L'objectif de ce travail était de construire un nouveau module de mélange en amont permettant l'étude encore plus rapide des procédés d'extraction L-L. Comme de petites modifications de la concentration de la phase aqueuse ou organique peuvent avoir un impact important sur l'extraction L-L globale, il était nécessaire de concevoir et de construire une configuration de mélange qui pourrait être précise jusqu'à des dizaines de µmol/L. Par ailleurs, l'accent a été mis sur le mélange de liquides aqueux (jouant soit sur la concentration en acide soit en métal). Premièrement, les micromélangeurs qui sont décrits de manière exhaustive dans la littérature ont été considérés comme de bons candidats dans la mise en oeuvre du module de mélange.

3. 1 .Chapter 4 :

 14 Introduction ................................................................................................................ 3.2. From microfluidics to millifluidics ............................................................................... 3.2.1. Extraction module and millifluidic XRF chip ...................................................... 3.2.2. Liquid flow generators ...................................................................................... 3.2.3. Overall millifluidic platform ............................................................................... 3.3. First solid-phase extraction implemented on the platform ........................................ 3.3.1. Materials and methods ..................................................................................... 3.3.2. Extraction results ............................................................................................... 3.3.3. Experimental data compared to theoretical models ........................................ 3.3.4. Selective separation of Fe from Rare-Earth Elements ....................................... 3.3.5. Conclusion ....................................................................................................... 3.4. Preliminary results of Solid-phase Extraction with a real leaching solution from waste CPU ................................................................................................................................... 3.4.1. Introduction .................................................................................................... 3.4.2. Materials and methods ................................................................................... 3.4.3. First Extraction results ..................................................................................... 3.4.4. Conclusion ....................................................................................................... 3.5. Conclusion ................................................................................................................. Mixing in microfluidics, a new module added to the platform ....................... 4.1. Introduction .............................................................................................................. 4.2. State-of-the-art on mixing implemented in microfluidics ......................................... 4.2.1. Two Categories of micromixers ....................................................................... 4.2.2. Determining of mixing quality ......................................................................... 4.2.3. Conclusion ....................................................................................................... 4.3. Implementing a new micromixing-based module ..................................................... 4.3.1. Introduction and presentation of the microfluidic FTIR module ..................... 4.3.2. Mixing studies of a 2-in-1 microfluidic chip .................................................... 4.3.3. ultrasound-based mixing chip ......................................................................... 4.3.4. Why microfluidic was not selected ................................................................. 4.3.5. Conclusion ....................................................................................................... 4.4. Development of an independent mixing setup ......................................................... 4.4.1. Introduction .................................................................................................... 4.4.2. Preliminary test for setup validation ............................................................... 4.4.3. Design and description of the setup ............................................................... 4.4.4. Conclusion ....................................................................................................... 4.5. Conclusion ................................................................................................................. General Conclusion ........................................................................................................ List of Publications ......................................................................................................... Oral Presentations ......................................................................................................... Annexes ......................................................................................................................... Annex 1: Metals' criticality -Supply risk versus economic importance ................... Annex 2: Web of Science Research keywords ........................................................... Annex 3: Liquid-liquid extraction raw data involving La, Eu and Yb .......................... Annex 4: Liquid-liquid extraction data involving Yb and Fe conducted at 20, 30 and 35 °C .......................................................................................................................... Annex 5: Experimental determining of the diffusion coefficient of metal ions in the organic phase ............................................................................................................

Figure 4

 4 Figure 4 The Circular Economy of metals -extracted from https://eitrawmaterials.eu/[START_REF]EIT RawMaterials -Developing raw materials into a major strength for Europe[END_REF]

Figure 5

 5 Figure 5 Elements and materials listed as CRM by the EU from 2011 to 2020 -Extracted with permission fromthe study ofPopov et al. 25

Figure 7

 7 Figure 7 Metal recycling process made of different steps and techniques from the PCB to the purified metal elements -Extracted with permission from Wu et al. 5

Figure 8

 8 Figure 8 Photo of the Singapore based part of the SCARCE team working on PCB recycling processes development in January 2022 (co-PIs: Prof Alex Yan Qingyu and Vg Prof. JC Gabriel are missing from the picture)

Figure 12 Y

 12 Figure 12 Y-shape microfluidic channel used for L-L extraction of copper -Extracted with permission fromPriest et al. 76

Figure 14

 14 Figure 14 Microfluidic L-L extraction chip sandwiching a square of a hydrophobic PTFE membrane -Extracted with permission from Theisen et al. 80

80

 80 Figure 14 Microfluidic L-L extraction chip sandwiching a square of a hydrophobic PTFE membrane -Extracted with permission from Theisen et al. 80

Figure 15

 15 Figure 15 Photo of the microfluidic platform performing L-L extraction on October 15 th 2021

Figure 24

 24 Figure 24 Photo of strained X-ray tube holder 3D-printed in Polylactic Acid (Left); Tube holder rough from 3Dprinted in Polycarbonate (Right).

Figure 27

 27 Figure 27 Molecular structures of extractants DMDOHEMA (left) and HDEHP (right)

Figure 32 L

 32 Figure 32 L-L extraction ratios obtained for batch and microfluidic direct extractions ("extraction") and reverse extractions ("R-extraction"). In this study conducted by Maurice et al., three REEs are implemented with a concentration of 11.11 mM in 0.3 M nitric acid. The organic solvent is 75 wt% HDEHP/25 wt% DMDOHEMA in Isane® 81 .

  Figure33shows the data obtained for the extractions that were run at the temperature of 25°C (the others are placed inAnnex 4). Evolution of elemental concentrations of both Yb(III) and Fe(III) for D-and R-extractions as a function of experimental time are displayed on four graphs. Thanks to the motorized stage enabling alternating measurements of both organic and aqueous phases, evolutions of metallic concentrations in the two immiscible phases are plotted. On each graph, the metal concentration first increases during a transition period until it levels off at a value indicating that a steady-state was reached for the L-L extraction at the given flowrate. Indeed, the first period of increasing concentration corresponds to the time required for the front interface of the solutions obtained under the new conditions to flow through and replace the former liquids within the microfluidic platform (microfluidic chips and tubing). Hence four plateaus are observed, one for each of the four different implemented flowrates. Furthermore, slow flow rates mean that the old and new solutions are in contact for a longer period of time, which leads for some ion diffusions from one solution to the other and hence, the lower the flowrate, the longer the transition time to reach a steady regime. Each steady-state plateau averaged value represents one point of the Yb(III)/Fe(III) L-L extraction kinetic curve. Such a plateau shall not be confused with the reaching of the thermodynamic equilibrium, which was shown in the previous section 2.3.3.1 to be close to 100% of extraction for both species. Regarding Fe(III) D-extraction (Figure 33.b)), a continued decrease of the Fe(III)-concentration value of each plateau in the aqueous phase indicates that thermodynamic equilibrium is not reached, even at highest contact time of 34 min. This is double-confirmed by both R-extraction Fe(III) graph (Figure 33.d)) and previous batch results. Nevertheless, in the case of Yb(III), all D-extraction steady-states have the same value, which indicates that the thermodynamic equilibrium has already been reached(Figure 33.a)). This is further confirmed by the R-extraction experiment (Figure 33.c)) and the similarities of Yb(III) plateau values in both D-extraction and R-extraction. Hence, for both experiments one can

1 .

 1 Preparation of the adsorption phaseInitial standard solutions of sole La(III), Nd(III), Yb(III) and Fe(III) with an intended concentration of 250 mM each were respectively prepared by dissolving calculated and weighted amounts of La(NO3)3•6H2O, Nd(NO3)3•6H2O, Yb(NO3)3•5H2O and Fe(NO3)3•9H2O salts in a solution of nitric acid ‡ ‡ ‡ at 0.1 M.

  Some microfluidic geometries have become widely known and used for their reported mixing efficiencies. One of them is called the Herringbone micromixer and was first introduced by Stroock et al. 196 in 2002. As Figure 58.A) shows, the floor of the main fluidic channel is etched with crenel-like microchannels. Thanks to these channel discontinuities, a chaotic mixing is induced between the flowing liquids (see Figure 58.B)): this is called chaotic advection. Nowadays, microfluidic chip commercial manufacturers are selling Herringbone-based micromixer for fluid mixing purposes.

Figure 58 A

 58 Figure 58 A) Herringbone micromixer geometry. B) Pictures of the fluids' mixing through the channel, where one liquid is concentrated with a fluorescent tracer -Extracted with permission from Stroock et al. 196

Figure 62

 62 Figure 62 Schematic representation of the usual setup implemented for mixing performance measurement -Extracted with permission from Mahmud et al. 215

Figure 72 .

 72 Figure 72.A) and Figure 72.B) present respectively side and top views of the simulated microchannel, in which the species' concentration is plotted. These views were obtained with fluids flowing at 20 µL/min and same views are placed in Annex 14 for 1 µL/min and 10 µL/min flowrates. Red color means that the fluid is highly concentrated (close to the input concentration 1 mol/L of the most concentrated fluid) whereas blue corresponds to a pure fluid. When comparing all the simulated views with the experimental mixing obtained with colored liquids, both results are optically close to each other.

  First tests were based on optical mixing assessment of the liquid flowing out of the mixing channel (see outlined channel part in Figure 74.B)). Similar NaCl concentrated colored liquids as the ones described section 4.3.2.2.1 were used. The setup is shown Figure 74.D).

ANNEX 1 :Figure 85

 185 Figure 85 Graphical representation of the raw materials' criticality in the EU taking into account their supply risk versus economic importance -Extracted from 228

  La première idée était d'intégrer la partie mélange directement avec le module d'extraction au sein d'une seule puce microfluidique, compatible avec un mélangeur actif ou passif. Par conséquent, un mélangeur passif à chaîne de perles a d'abord été conçu, fabriqué et testé. Des solutions de test composées d'eau pure et d'eau concentrée en NaCl ont été utilisées afin qu'elles puissent reproduire autant que possible les solutions aqueuses qui sont distribuées dans les processus d'extraction L-L. Pour l'évaluation de l'efficacité du mélange, deux techniques expérimentales ainsi qu'une simulation de mélange à l'aide de COMSOL Multiphysics® ont été menées. Parmi les deux analyses expérimentales, une évaluation optique a été réalisée en colorant les deux liquides aqueux donnant des informations sur le mélange à l'échelle macro. Cette méthode d'analyse a permis d'avoir une première idée approximative des performances de mélange des puces de mélange microfluidique conçues. La deuxième méthode consistait à utiliser un module FTIR fait maison, qui est un outil qui mesure l'activité chimique du solvant qui le traverse, en analysant les spectres IR de ses vapeurs. Cette méthode permet d'apporter des informations de mélange à l'échelle moléculaire en raison de la nature de l'analyse et est l'une des premières à être rapportée dans ce contexte. Cette configuration spécifique a été utilisée pour comparer les performances de mélange des micromélangeurs conçus avec une solution déjà mélangée comme référence. Pour le mélange d'eau pure et d'eau concentrée en NaCl, il a été démontré une assez bonne efficacité de mélange par rapport au mélange de référence. Finalement, tous ces résultats ont été croisés avec un logiciel de simulation modélisant le processus de macromélange réalisé expérimentalement. Un micromélangeur actif utilisant des ultrasons induits a également été testé mais s'est avéré infructueux.Un nouveau module de mélange a alors été imaginé et conçu, basé sur un mélange en millibatch. Son fonctionnement a été programmé et totalement intégré au code existant permettant le contrôle de la plateforme microfluidique. Une étude d'extraction L-L complète n'a pas encore été réalisée avec ce nouveau module.

  France at CEA (French Atomic Energy and Alternative Energies Commission) Saclay in the LICSEN (Laboratory of Innovation in Surface Chemistry and Nanoscience) laboratory. PhD lab work in SCARCE was financially supported by the National Research Foundation, Prime Minister's Office, Singapore, the Ministry of National Development, Singapore, and National Environment Agency, Ministry of the Environment and Water Resource, Singapore under the Closing the Waste Loop R&D Initiative as part of the Urban Solutions & Sustainability -Integration Fund (Award No. USS-IF-2018-4).

Table 1

 1 Averaged metallic contents found in EC of PCBs

	Metal Elements Metal concentration (wt %)	EC where reported
	Cu	7 -20 wt.%	CPU, MLCC, Inductance, Pin, Integrated Circuit, Resistor
	Al	1.0 -7 wt.%	Pin, CPU
	Pb	Traces -3 wt.%	CPU, Resistor
	Zn	Traces -1 wt.%	CPU, Pin
	Ni	0.001 -3 wt.%	Pin, CPU
	Fe	0.1 -5 wt.%	Inductance, Pin, CPU, Resistor
	Sn	0.01 -5 wt.%	CPU, Solder, Integrated Circuit
	Ag	50 -4000 ppm	CPU
	Au	400 ppm	CPU, Pin
	Pt	30 ppm	Pin
	Pd	50 -300 ppm	Capacitor, MLCC
	REE	Trace -15 wt.%	Magnet, REE-based MLCC

Table 3

 3 Standard concentration solutions for XRF calibration

	Organic standards	Fe(III) (mM) Yb(III) (mM)	Aqueous standards	Fe(III) (mM) Yb(III) (mM)
		4	9	16	10	5
		5	5	17	7	6
		3	4	18	4	9
		2	8	19	2	8
		1	9	20	5	5
		10	5	21	1	9
		6	8	22	9	2
		7	6	23	3	4
		9	2	24	6	8
	10	12	0	25	12	0
	11	6	0	26	6	0
	12	1	0	27	1	0
	13	0	1	28	0	1
	14	0	6	29	0	6
	15	0	12	30	0	12

Table 4

 4 Extraction ratios of single elemental batch L-L extractions, run at 23°C

	Element	Aqueous phase before extraction	Organic phase after extraction	Extraction ratio (%) 𝑶𝒖𝒕 [𝑿] 𝒐𝒓𝒈 [𝑿] 𝒂𝒒 𝑰𝒏
	Yb(III)	17.60 ± 0.07 mM	17.50 ± 0.07 mM	99.43 ± 0.81
	Fe(III)	93.79 ± 0.22 mM	93.57 ± 0.24 mM	99.77 ± 0.49

Table 5

 5

	Entropy and Enthalpy values for Yb(III) and Fe (III) for R-extraction as deduced from a van't Hoff
	decomposition assuming ideal behavior and no nano structuration
	Element	∆𝑆 (J/(mol.K))	∆𝐻 (J/mol)
	Yb(III)	15.2 ± 29.4	-15.5 ± 2.9
	Fe(III)	-113 ± 9	-4.0 ± 2.0
	2.3.5. QUANTIFYING THE INTERFACIAL RESISTANCES OF A LIQUID-LIQUID
	EXTRACTION ‡ ‡

Table 6

 6 Parameters implemented in the simulation

	Parameter	Value	Source
	Channel width	1 mm
	Channel depth	200 µm
	Channel length	170 mm
	Membrane thickness em	30 µm
	Membrane porosity 𝑝 Membrane tortuosity 𝜏	70 % 2	Experimental
	Distribution coefficient kd	Variable (depends on the element)
	Initial ion concentration in aqueous or organic phase	0 or 11.11 mM
	Flow rate	0.7, 1, 5, 10 and 20 µL/min
	Diffusion coefficient of ionic
	species in the aqueous	5×10 -10 m 2 /s	Touré et al. 105
	channel Daq
	Diffusion coefficient of ionic		Estimated from Touré et
	species in the organic	7.25×10 -11 m 2 /s	al. 105 and viscosity
	channel Dorg		measurements
	Transfer coefficient at the interface kv	Fitted

  Table 6 was estimated (i) from the one obtained by Touré et al. for Neodymium extraction by HDEHP diluted in Isane® and; (ii) by conducting viscosity measurements detailed in Annex 5. Finally, the transfer coefficient at the interface, kv, was varied by fitting the experimental data that were obtained by Maurice et al. 81 with the simulated data.

  In the microfluidic L-L extraction chip presented in the previous section 2.3, the total liquid volume that can be accommodated in one microfluidic channel is of 34 µL. Because 20 µL/min is the highest flowrate that can be used to dispense liquids without any leakage, the slowest contact time between the organic and aqueous phases is thus 1.7 min. However, in the kinetic study involving ytterbium and iron, ytterbium steady-state plateau values are identical for the four liquid flowrates in D-extraction and embody the thermodynamic L-L extraction equilibrium of Yb(III). Therefore, given the highest implemented flowrate of 20 µL/min, the kinetic of extraction of Yb(III) is achieved within a contact time of less than 1.7 min. Although some studies report the determining of the kinetics of L-L extraction systems 112

	2.4.1. SOLVING FAST EXTRACTION KINETICS SYSTEMS
	2.4.1.1. Introduction

  24 min (at maximum flowrate of 20 µL/min). Yb(III) and Y(III) were chosen as metallic elements to be studied in the validation of this new extraction chip. In the previous extraction study involving Fe(III) and Yb(III), we proved that Yb(III) was thermodynamically extracted within less than 1.7 min. Thanks to the new L-L extraction chip, the kinetics of Yb(III) was finally uncovered.

2.4.2. FROM LIQUID-LIQUID EXTRACTION TO MEMBRANE TESTING

2.4.2.1. Introduction

Instead of using an extracting and water-immiscible solvent to selectively extract metals from an aqueous leaching solution, membranes can also be used as a physical and/or chemical technique to separate metals (see Chapter 1, section 1.4.3.3) 117-120 . In L-L extraction studies presented previously, the two aqueous and organic phases are separated by a PTFE membrane, which is chemically neutral and only acts as a stabilizer of the L-L interface. Such a capability can be used in a reverse fashion: to study new research and development of new ion specific membranes. Hence, we present in this section preliminary investigations of the potential of our microfluidic platform for the study of new ions specific membranes.

Table 8

 8 Metal extraction results from membrane testing conducted at 10 µL/min

	Retentate	Permeate

  Quantification of Solid-Phase Metal Extraction Efficiencies Using Instrumented Millifluidics Platform" published in the Chemical Engineering Journal in November 2022. This paper is the result of experiments conducted by myself, data analysis and interpretation together with Ms. Sarah Chevrier, Dr. Jean-Christophe Gabriel and myself, which first draft was also written by myself. Hence, parts of this article were extracted, adapted and added to this PhD thesis, with permission from all co-authors. The published article can be found in the Annex 19.

Table 9

 9 Experimental parameters for an SPE

		30 mM La(III)
		30 mM Nd(III)
	Adsorption solution	30 mM Yb(III)
		30 mM Fe(III)
		in 0.1 M HNO3
	Washing solution	DI Water
	Desorption solution	8.5 M HNO3
	Adsorption and desorption flowrate	0.405 mL/min
	Temperature	25.0 °C

Table 10

 10 Exchange capacities of REEs and iron and total capacity of the cation-exchange fiber

	Element	Retention capacity cretention (eq/kgfiber)
	La(III)	0.72 ± 0.04
	Nd(III)	0.51 ± 0.03
	Yb(III)	0.22 ± 0.02
	Fe(III)	0.41 ± 0.02
	Total capacity	1.87 ± 0.11

Table 12

 12 Concentrations of REEs and Fe after dilution by Cit

	Fe:Cit Molar Ratio Added Cit volume (mL)	REEs and Fe new concentrations (mM)
	1:1	0.556	29.7
	1:2	1.111	29.4
	1:4	2.222	28.8
	1:6	3.333	28.3
	1:10	5.556	27.2
	1:20	11.111	25.0
	3.3.4.3. Fe:Citric Acid molar ratios' impact on separation

Table 13

 13 Concentrations measured by ICP-OES of the CPU leaching solution

	Element Concentration measured by ICP-OES (mM)
	Ba	0.25 ± 0.02
	Mn	0.12 ± 0.01
	Zn	0.15 ± 0.02
	Cu	35.0 ± 0.1
	Ni	1.2 ± 0.3
	Sn	1.0 ± 0.1
	Co	Trace
	Al	0.57 ± 0.02
	Pd	Trace
	Pb	0.31± 0.07
	Au	0.12 ± 0.02

Table 14

 14 Au concentrations for the different steps of the implemented SPE measured by ICP-OES

	SPE Step	Au Concentration (mM)
	Initial leaching solution	0.12 ± 0.02
	After adsorption step	0 † † † †

Table 15

 15 Summary of waiting time and recorded spectra for the three flowrates implemented for the FTIR measurement

	Flowrate (implemented consecutively in this order)	Waiting time	Number of recorded reference spectra	Number of recorded spectra
	1 µL/min	6 hours	10	20
	10 µL/min	60 minutes	10	20
	20 µL/min	20 minutes	10	20

Table 16

 16 Parameters used for the mixing simulation in COMSOL®

	Physics	Parameter	Value
		Flowrate at Inlet 1	1, 10 or 20 µL/min
		Flowrate at Inlet 2	Identical as Flowrate at Inlet 1
	Laminar flow	Nature of the flow	Stokes' flow
		No slip wall	-66
		Atmospheric pressure at the outlet	-
		Liquid Material	Water
		NaCl Concentration at Inlet 1	0 mol/L
	Diffusion of diluted species	NaCl Concentration at Inlet 2	1 mol/L
		Ions' diffusion coefficient	5.10 -10 m²/s 227
		Diffusion Law	Fick's Law
	Results

Table 17

 17 Parameters used in the mixing program new mixing module was presented in this section. The setup relies on a milli-batch mixing, which prepares a specific liquid volume to be dispensed in the L-L extraction microfluidic circuit. The mixing technique is based on a mechanical mixing induced by a magnetic stirring bar controlled by a homemade magnetic stirrer inserted in a 3D-printed structure holding the whole setup. Mixing is processed online but is working in batch mode, i.e. the mixture is first prepared and sent to the microfluidic L-L extraction chip afterwards. In line with the fully automatized L-L extraction module, the mixing module was designed to operate independently, without human intervention. For this reason, a whole new Python code was written and added to the existing code. A protocol placed in Annex 16 and based on previous FTIR micromixer assessment was eventually developed to further validate the mixing performance of the module.

		Category
	Name of the parameter	(fixed,	Unit	Description
		calculated)
				Liquid flowrate of the most
	flowrate_concentrated	Calculated	µL/min	concentrated aqueous solution to be dispensed in the mixing
				tank by Syringe Pump 1
				Liquid flowrate of the least
	flowrate_diluted	Calculated	µL/min	concentrated aqueous solution to be dispensed in the mixing
				tank by Syringe Pump 2
				Liquid flowrate of the mixed
				liquid coming out from the
	flowrate_withdraw	Fixed	µL/min	mixing tank and filling the
				syringe installed on Syringe
				Pump 3
				Liquid flowrate of aqueous
	flowrate_extraction	Fixed	µL/min	phase during L-L extraction
				dispensed by Syringe Pump 3
				Liquid flowrate of DI water
	flowrate_purge	Fixed	µL/min	dispensed in the mixing tank during purging mode by the
				peristaltic pump
				Liquid flowrate of the purging
	flowrate_purge_extraction	Fixed	µL/min	aqueous solution dispensed by
				Syringe Pump 3
				Liquid volume to be prepared in
	mixing_volume	Calculated § § § §	mL	the mixing tank for L-L
				extraction
	§ § § § See Annex 15

A

Table 18

 18 Viscosity and Dorg results for the extractant solution used in this work. T=25°CStandard solutions of La(III), Nd(III), Yb(III) and Fe(III) were prepared from their respective nitrate salts. Table19summarizes weighted masses (calculated to get an approximate 250 mM concentration) as well as the final concentrations measured by Inductively Coupled Plasma with Optical Emission Spectroscopy (ICP-OES).

	Liquid	Viscosity (mPa.s)	Diffusion coefficient Dorg (10 -11 m 2 /s)
	Water	0.89	-
	Extractant solution (this work): Isane with HDEHP/DMODHEMA (25%/75%) 0.9M	2.40 ± 0.09	7.25 ± 0.26
	Extractant solution (Touré et al. 105 ) : n-dodecane with HDEHP 1M with Neodymium	3.48	5.00

Table 19

 19 ANNEX 7: XRF CALIBRATION OF LA, ND, YB AND FE SOLID-PHASE EXTRACTIONFor the XRF calibration performed in this study, multiple known concentrations of La(III), Nd(III), Yb(III) and Fe(III) in an aqueous medium were prepared by dilution of standard solutions of known concentration for La(III), Nd(III), Yb(III) and Fe(III). The concentrations of all four elements were generated randomly and uniformly within a given range (see Table20).

		Masses and concentrations of 0.1 M HNO 3 standard solutions
	HNO3
	concentration		La(III)	Nd(III)	Fe(III)	Yb(III)
	(M)
		Mass (g)	10.64	10.14	12.31	11.33
	0.1	Concentration (mM)	203.8 ± 0.5 204.2 ± 0.5 326.8 ± 0.8 282.8 ± 0.7

Table 20

 20 Calibration concentrations of La(III), Nd(III), Yb(III) and Fe(III).

	Samples La(III) (mM) Nd(III) (mM) Fe(III) (mM) Yb(III) (mM)
	1	25	7	15	9
	2	22	12	8	16
	3	7	25	12	12
	4	30	16	14	6
	5	9	21	10	13
	6	10	30	11	4
	7	18	8	5	10
	8	5	9	25	11
	9	15	17	10	27
		20	15	30	7
		6	14	30	12
		4	23	9	30
		27	10	13	10
		14	15	18	25
		22	19	7	14
		15	15	15	15
		10	0	0	0
		50	0	0	0
		100	0	0	0
		0	10	0	0
		0	50	0	0
		0	100	0	0
		0	0	10	0
		0	0	50	0
		0	0	100	0
		0	0	0	10
		0	0	0	50
		0	0	0	100
	Figure 89 shows the calibration curves obtained from measuring the solutions listed in

Table 20

 20

. The following energy ranges were integrated for La: 4.

52-4.99 keV, Nd: 5.02-5.63 keV, Fe: 6.30-6.93 keV and Yb: 7.26-7.93 keV.

Table 21

 21

		Adams-Bohart model parameters
		Adams-Bohart Model
	Elements
		𝑘 (L/(min.g)) N (g/L) 𝑟
	La(III)	4.9.10 -6	8.8.10 3 0.89
	Nd(III)	2.1.10 -5	6.6.10 -3 0.87
	Table

Table 22

 22 Liu model parameters

		Liu Model
	Elements
		k (m 3 solution/(m 2 fiber.min)) τ (min)
	La(III)	37
		43
	Nd(III)	26

Table 22

 22 summarizes the parameters obtained for the Liu model. Figure92.A) and Figure92.B) plot the two theoretical adsorption curves compared to the experimental data for La(III) and Nd(III), respectively.

Table 24

 24 Randomized implemented liquid flowrates and associated mixed volumes

	Mixture	DI Water flowrate	75 % NaCl saturated DI	Total volume of the
	Number	(µL/min)	water flowrate (µL/min)	mixture (µL)
	1	1	12	390
	2	4	5	270
	3	12	8	600
	4	5	9	420
	5	8	13	630
	6	15	2	510
	7	7	5	360
	8	11	11	660
	9	10	3	390
	10	13	7	600

  Le recyclage des métaux est le processus de collecte, de traitement (séparation) et de réutilisation des matériaux métalliques provenant de diverses sources, tels que les produits mis au rebut, les déchets industriels et les déchets métalliques, en les réinjectant dans le cycle de production. Le concept de recyclage des métaux a pris de l'ampleur au cours de la dernière décennie. D'une part, les entreprises industrielles ont continué d'augmenter leur production d'équipements électroniques et électriques, ce qui a entraîné une demande toujours croissante de métaux. D'autre part, la production de métaux à partir de ressources minérales primaires a été largement exploitée et plusieurs institutions scientifiques internationales ont montré que les ressources métalliques existantes et connues à partir de minerais ne seront pas suffisantes pour continuer à répondre à la demande globale de métaux. Par conséquent, la nécessité de trouver des ressources secondaires fiables est apparue et, à cet égard, les déchets électroniques et électriques (DEEE) ont attiré beaucoup d'attention en raison de leur teneur élevée en métaux (à la fois qualitative et quantitative) et de leur concentration. Lorsqu'il s'agit de développer des procédés de récupération des métaux associés, des procédés à la fois physiques et chimiques peuvent être mis en oeuvre. Les traitements physiques enrichissent généralement le flux de déchets électroniques avec des composants ou des éléments qui ont, par exemple, des formes ou des propriétés physico-chimiques similaires. Les processus chimiques permettent ensuite une séparation et une purification élémentaires supplémentaires, ce qui donne un élément unique ou des alliages qui répondent au degré de pureté et aux spécifications commerciales, qui peuvent donc être renvoyés dans le flux de matières premières.La combinaison de ces deux types de procédés, poussés au-delà de l'état de l'art, incarne le véritable paradigme de l'approche menée dans le projet SCARCE (NTU-Singapore CEA Alliance for Research in the Circular Economy) à Singapour. Étant donné que la composition d'un flux de déchets électroniques n'est pas aussi stable et constante que pour les ressources minérales naturelles, il est nécessaire d'adapter rapidement les processus qui peuvent faire face à une composition chimique très variable de ses entrants. Ainsi, la méthode préconisée au sein de SCARCE consiste d'abord en un démontage suivi d'un tri automatisé intelligent des composants électroniques pour enrichir les bacs de tri de DEEE avant de mettre en oeuvre tout procédé chimique hydrométallurgique. Ces derniers représentent une catégorie majeure de procédés chimiques utilisés pour recycler les métaux des déchets électroniques. Ces procédés consistent à dissoudre, séparer et récupérer les métaux contenus dans les DEEE à l'aide d'un milieu généralement aqueux, mais ils doivent être aussi sélectifs que possible. Ils ont également l'objectif de remplacer les procédés pyrométallurgiques, qui consomment de grandes quantités d'énergie (incinération des déchets électroniques), nécessitent des dépenses d'équipement importantes et contribuent au dégagement de fumées et de cendres toxiques. Ce dernier gaspille également de nombreux métaux car une fois mélangés au moût ou aux cendres volantes, ils sont quasiment impossibles à récupérer de manière économiquement viable. Parmi les procédés hydrométallurgiques, des extractions liquide-liquide (L-L) et des séparations en phase solide (SPS) peuvent être réalisées pour cette application de récupération des métaux. Cependant, l'efficacité d'extraction ou de séparation dépend de plusieurs paramètres tels que la concentration des ions métalliques, la nature et la concentration du milieu aqueux dans lequel ils sont dissous, les caractéristiques et propriétés de la phase d'extraction, la température. Par conséquent, le développement de tels procédés à l'échelle d'un lot de laboratoire pour une mise à l'échelle et une industrialisation futures probables peut prendre du temps et entraîner de grandes quantités de déchets liquides en raison du nombre d'expériences qui doivent être réalisées pour l'optimisation des procédés.Une plateforme entièrement automatisée et instrumentée a été développée pour faciliter l'étude de nouveaux procédés chimiques hydrométallurgiques. Un module d'extraction L-L microfluidique a permis un développement rapide du procédé, offrant une meilleure compréhension de la cinétique, de la thermodynamique et des mécanismes impliqués au cours du procédé. La plateforme a également été mise à niveau en tant qu'outil millifluidique pour effectuer des processus d'extraction en phase solide. Les modules d'extraction microfluidique et millifluidique ont tous deux bénéficié d'une analyse en aval en ligne des flux d'extraction de sortie basée sur la fluorescence X.Lorsqu'une extraction L-L est mise en oeuvre à l'échelle macro, seule une fraction des paramètres expérimentaux peut être tirée du procédé. A cette échelle, seules les données thermodynamiques peuvent être extraites, sans informations cinétiques, en raison de la surface de contact non contrôlée et inconnue entre les deux phases non miscibles lorsqu'elles sont mélangées. Aussi, la cinétique est d'autant plus difficile à suivre que, dans la plupart des cas, les deux phases non miscibles doivent décanter (décantation) avant d'être séparées. Cependant, les extractions L-L mises en oeuvre en microfluidique peuvent fournir des données à la fois thermodynamiques et cinétiques, conduisant à une meilleure compréhension des mécanismes impliqués dans une telle extraction. Par exemple, la microfluidique a été particulièrement utile pour lever le voile sur la cinétique du procédé PUREX pour la purification de l'uranium.Le développement d'un système microfluidique pour l'extraction L-L est une première étape vers des études plus rapides des systèmes physico-chimiques, représentant également une empreinte carbone relativement réduite. À cet égard, une plateforme microfluidique entièrement automatisée et instrumentée a été développée par la mise à niveau de ses éléments constitutifs. La première amélioration comprend une puce d'extraction microfluidique L-L, qui met en contact deux phases liquides non miscibles, l'une aqueuse et l'autre organique. La puce prend en sandwich une membrane PTFE hydrophobe, permettant une interface contrôlée tout au long du processus d'extraction. Les deux liquides de sortie sont ensuite envoyés dans un module d'analyse en aval et en ligne utilisant la fluorescence X pour la mesure des concentrations de métaux dans les flux liquides. Dans l'ensemble, une telle plateforme réduit considérablement les déchets liquides générés par ce type de procédé chimique ainsi que le temps nécessaire pour réaliser les expériences. L'extraction L-L impliquant du Fe(III) et de l'Yb(III) a été menée sur la plateforme et a notamment permis de déterminer la cinétique du fer. Une phase organique synergique composée de deux molécules extractantes (HDEHP et DMDOHEMA) a été utilisée comme phase d'extraction, et une étude de double extraction directe et inverse a été réalisée. Une telle approche a permis de définir les véritables valeurs d'équilibre thermodynamique d'extraction, en particulier dans le cas d'une cinétique d'extraction extrêmement lente. Ainsi, l'équilibre thermodynamique du fer, qui ne peut être atteint que pour des temps de contact élevés, apparaît significativement supérieur à 30 min alors que celui de l'ytterbium se produit en moins de 1,7 min. Suite à cette étude d'extraction, une nouvelle puce d'extraction L-L a été développée pour aborder et résoudre ces extractions à cinétiques rapides. Nous pouvons maintenant explorer la cinétique avec des temps caractéristiques de réaction d'extraction jusqu'à quelques dizaines de secondes de temps de contact (la limite inférieure est de 14,4 s). Avec une telle capacité, nous avons montré que la cinétique d'extraction rapide de l'ytterbium pouvait être étudiée.Enfin, un nouveau cas d'application, permettant de tester la perméabilité de membranes pour l'extraction de métaux, a été développé, testé et validé. Cela montre la polyvalence d'une telle plateforme instrumentée et automatisée vers d'autres études de cas de séparation reposant sur de l'hydrométallurgie.

  Un cycle SPS comportait trois étapes : adsorption des métaux, lavage de la colonne puis désorption des métaux. Les débits et concentrations d'acide utilisés permettent de suivre la cinétique d'adsorption et de désorption complète des ions métalliques adsorbés. Suite à ces résultats expérimentaux, plusieurs modèles théoriques ont été étudiés et comparés aux courbes expérimentales. Comme aucun des modèles sélectionnés n'était adapté, les équations des modèles de Thomas et Yoon-Nelson ont été adaptées car elles montraient la tendance la plus proche des données expérimentales. Cette méthode de modification d'un modèle théorique est une pratique qui avait déjà été menée dans d'autres études de SPS telles que celle de Yan et al., qui ont développé un modèle basé sur le modèle de Thomas. Il a finalement été constaté que l'ajout d'un paramètre supplémentaire spécifique à un élément chimique dans l'équation de Thomas pouvait conduire à une bonne correspondance. Cependant, l'explication d'un tel paramètre reste phénoménologique. Suite à la mise en oeuvre de la SPS des éléments TR et du fer, un procédé de séparation a été développé pour éliminer le fer du mélange cationique. L'acide citrique, utilisé comme agent chélatant, a été sélectionné comme candidat pour modifier sélectivement la charge apparente des complexes de fer. Plusieurs cycles SPS ont été effectués, à chaque fois avec un rapport molaire Fe:Cit différent, permettant de déterminer facilement la teneur optimale en acide citrique dans le mélange métallique initial pour éviter la sorption de Fe sur la fibre lors de l'étape d'adsorption. Un rapport molaire Fe:Cit de 1:20 s'est avéré être le rapport optimisé (parmi ceux testés) pour obtenir la quantité minimale de fer désorbé dans l'étape de désorption, c'est-à-dire de Fe sorbé dans l'étape d'adsorption.Enfin, une étude de SPS a été menée sur une véritable solution de lixiviation issue de CPU. Les essais préliminaires rapportés dans cette thèse ont été réalisés en mode batch avec analyse liquide par ICP-OES. Les premiers résultats ont montré une forte affinité des ions Au pour la fibre d'échange anionique, qui s'est avérée meilleure (100 %) que celle rapportée dans la littérature utilisant le même type d'extraction(87 %).Le mélange en microfluidique est une technique très étudiée dans le vaste domaine de la microfluidique, qui permet le mélange d'espèces, théoriquement régi uniquement par la diffusion à ce niveau microscopique. Les mécanismes diffusifs étant caractérisés par des phénomènes de transport lents, deux principaux types de micromélangeurs ont émergé pour accélérer les processus de mélange microfluidique, à savoir les micromélangeurs actifs et passifs. Les techniques de mélange actif utilisent une source d'énergie externe pour induire un mélange interne des espèces dans le microcanal. Les micromélangeurs passifs tirent parti d'une géométrie de canal spécifique entraînant soit un phénomène d'advection chaotique, soit la stratification des fluides.

† This section is extracted and adapted from the introduction ofOlivier et al. 230

§ The notation X(III) refers to the element X in its third oxidative state complexed either in the aqueous or organic phase.

† † The organic phases after these batch L-L extractions were needed for the preparation of the organic phase for R-extraction.

LIST OF PUBLICATIONS

A. A. Maurice, J. Theisen, V. Rai, F. Olivier, A. El Maangar, J. Duhamet, T. Zemb, and J. C. P. Gabriel, "First online X-ray fluorescence characterization of liquid-liquid extraction in microfluidics," Nano Select, no. March, pp. 1-12, 2021, doi: 10.1002/nano.202100133

ORAL PRESENTATIONS

Study of interface kinetics of liquid-liquid extractions using X-ray

ANNEX 5: EXPERIMENTAL DETERMINING OF THE DIFFUSION COEFFICIENT OF METAL IONS IN THE ORGANIC PHASE

The description of the viscosity measurement is adapted and extracted from the Supplementary material from Maurice et al. [START_REF] Maurice | First online X-ray fluorescence characterization of liquidliquid extraction in microfluidics[END_REF] .

To estimate the diffusion coefficient in the organic phase, we measured the viscosity of the extractant solution using a simple microfluidic circuit and a pressure sensor (Figure 88.(a)). According to Poiseuille equation the pressure drop ΔP induced by a laminar flow in a long cylindrical pipe is expressed as:

Where L is the length of the pipe, A is cross-sectional area, Q the volumetric flow rate and µ is the dynamic viscosity.

By measuring two liquids such as water (ΔPwater, µwater) and the organic solution mixed with the extractants (ΔPorg, µorg), we can simplify and obtain:

Knowing the viscosity of water at 25°C, we can calculate the viscosity of the organic solution mixed with the extractants.