La gestion durable des boues résiduaires est l'un des grands enjeux du 21ème siècle. Si toutes les eaux usées produites par la population urbaine mondiale étaient traitées, ce qui est l’objectif ultime de l’OMS, 83 millions de tonnes de matières sèches seraient produites annuellement à l’horizon 2017. Pour réduire les coûts du traitement et de la valorisation des boues qui représentent la moitié de la facture de l’assainissement collectif, l’optimisation des procédés est nécessaire. Les ingénieurs ont donc besoin d’outils permettant d’adapter le fonctionnement des technologies de traitement aux caractéristiques des matériaux. Concrètement, les technologues ont besoin de connaître ou d’estimer le comportement en écoulement des boues, c’est-à-dire leurs propriétés rhéologiques, à chaque étape du traitement. Celles-ci ne pouvant être déterminées en continu, l’objectif de cette thèse est de démontrer la faisabilité d’utilisation des propriétés électriques pour prédire les écoulements des boues. Au cours de ce travail de thèse, les comportements rhéologique et électrique d’une boue type ont été établis : ainsi les propriétés d’écoulement d’une boue résiduaire peuvent être décrites par un modèle d’Herschel-Bulkley modifié (de façon à prendre en compte les cisaillements très élevés) tandis que sa signature électrique pourra être représentée par un circuit équivalent de Maxwell. L’impact de paramètres majeurs comme la température et la teneur en matières sèches sur les propriétés rhéologiques et électriques des boues et sur leur corrélation a été étudié. Les résultats obtenus nous ont également permis de montrer comment la structure du matériau est liée à ces caractéristiques. Enfin, des outils spécifiques ont été mis au point, pour la mesure simultanée des propriétés électriques et rhéologiques des fluides organiques complexes. L’ensemble des résultats obtenus ouvre de nombreuses perspectives, opérationnelles et fondamentales. / Sludge management is one of the 21st century’s main issues. If the whole wastewater produced by worldwide urban population was treated, which is the ultimate goal of WHO, 83 million tons of dry matter could be annually produced until 2017. In order to reduce the costs of sludge treatment and reuse, which represent half of the collective sanitation bill, process optimization is needed. Engineers are in need of tools allowing them to adapt treatment technologies to material characteristics. More specifically, technologists need to know or to estimate sludge flow behavior, i.e. their rheological properties, at each step of the process. However, these properties can’t be continuously acquired. The purpose of this thesis is then to demonstrate the feasibility of the usage of sludge electrical properties to predict their flow. During this work, rheological and electrical behavior of one sludge type had been established: so flow properties of sludge can be described by means of a modified Herschel-Bulkley model (so that high shear rates are taken into account), whereas electrical properties can be represented by Maxwell’s equivalent circuit. Major parameters impacts, as temperature and dry matter content on sludge rheological and electrical properties and their correlation have been studied. The results we obtained allowed us to highlight the way the structure of the material is related to these properties. Finally, specific tools have been developed for simultaneous measurement of rheological and electrical properties of complex organic fluids. All the obtained results open many operational and fundamental prospects.

ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY (EIS) has been widely used to study the electrical properties of biological material due to its non-invasive nature and experimental reliability. However, most of the precision impedance analyzers used in EIS only provide single- or two-channel measurements which are inadequate for larger-scale multiplexed measurements, such as those found in modern microfluidic cell culture experiments. The Biomedical Microsystems Laboratory has developed a 16-channel cell culture platform with integrated electrode arrays for monitoring cell growth and electrical properties (i.e., the so-called “electrical phenotype”). In this paper, a system consisting of a 16-channel solid-state analog multiplexer (MUX)paired with a low-cost, impedance analyzer is developed to replace high-cost physical relay MUX and impedance analyzer systems. System requirements and design constraints for monitoring biological systems are considered and a prototype device was fabricated. Initial testing was performed on a breadboard to verify the feasibility of the design idea. Results identified measurement errors due to parasitic elements in the system. Software compensation successfully corrected for parasitic capacitance in the analog MUX design. The accuracy of the measurement system was evaluated on a developed Printed Circuit Board Assembly (PCBA) by comparing theoretical values to MUX compensated data. Finally, an EIS experiment was carried out with tap water with the PCBA system, and measurement results were analyzed using an equivalent Circuit Model (ECM). These results successfully captured the dynamics of charge transport in the electrical double layer, consistent with a modified-Randlecell ECM.

Ce travail de thèse traite de la caractérisation par spectroscopie d'impédance d'échantillons biologiques de très faibles dimensions. Les phénomènes de polarisation, classiques en spectroscopie d'impédance, créent une contrainte et augmentent l'erreur de mesure en basses fréquences. L'objectif principal de cette thèse est l'optimisation géométrique de la structure d'un capteur à électrodes interdigitées afin d'élargir la bande de fréquence utile. Le premier chapitre synthétise les données fondamentales relatives au comportement électrique des tissus biologiques ainsi que leurs propriétés électriques. Un état de l'art des techniques fondamentales de mesure d'impédance basées sur les électrodes micrométriques est aussi décrit. Le deuxième chapitre concerne une approche théorique pour l'optimisation du capteur. Cette optimisation sert à élargir la bande de fréquence utile de mesure. Elle consiste à choisir un rapport optimum de distance inter-électrodes sur largeur d'électrode. Une modélisation tridimensionnelle du système d'électrodes chargé par un milieu biologiquea été simulée sous ConventorWare©. Les résultats de cette simulation sont discutés. Le troisième chapitre traite de la conception et de la réalisation des biocapteurs. Les dispositifs technologiques développés sont décrits. La conception et la fabrication des composants sont présentées. Dans le dernier chapitre, une campagne de mesure sur des micro-volumes de fluides (solutions étalons, sang humain) est réalisée. Les mesures sont effectuées à l'aide de cinq micros capteurs à électrodes interdigitées. Les mesures réalisées sur des échantillons (solution étalons, sang humain) ainsi que la validation des dispositifs sont discutées. Les résultats obtenus sont comparés à des valeurs publiées dans la littérature et la théorie d'optimisation développée est validée et justifiée expérimentalement / The characterization by impedance spectroscopy of biological micro samples is one of the objectives of this thesis. The well known polarization phenomena at low frequency is a constraint that increases the measurement error. The main objective of this thesis is to optimize the geometric structure of an interdigitated electrode sensor in order to obtain confident experimental results in an extended useful frequency band without a significant influence of the polarization effect. The first chapter synthesizes fundamental data on the electrical behavior of biological tissues and their electrical properties. A state of the art techniques of the fundamental impedance measurement based on micrometric electrodes is also presented. The second chapter developps a theoretical approach for optimization of the biosensor. This optimization is used to expand the usable measurement. Indeed, it leads to determine the optimum distance between electrodes versus electrode width. A 3D modeling of the electrode system loaded by a biological medium was simulated using ConventorWare and the results are discussed. In the third chapter, the devices developed in this work are described. Design and manufacture of the biosensor components are described. In the last chapter, measurements on microvolumes of fluids (standard solutions, human blood) using five micros sensors with interdigitated electrodes are presented and discussed. Finally we compare these results to published values in the literature and the optimization theory is experimentally validated

Atopic dermatitis (AD) and other atopic diseases are strongly related to skinbarrier dysfunction, a biomeasure which has limited and unsatisfactory assessmenttechniques. Machine learning (ML) powered electrical impedance spectroscopy(EIS) has been shown to differentiate defective barrier function in mice and adults. Techniques such as principal component analysis (PCA) andsupport vector classifiers (SVC) can be used as ML tools to evaluate EIS measurements. EIS measurements taken on unaffected skin of children aged between 4 monthsand 3 years were collected and analysed using the children’s AD status. Measurements were grouped into one of four groups based on this AD status; No AD (No AD was developed up until 2 years of age), Pre AD (measurements takenbefore the onset of AD), AD remission (measurements taken after the onset ofAD) and AD flare (measurements taken during active AD). A SVC model was trained on the raw EIS measurement data to distinguish measurements from twoof the binarized AD status groups; AD flare and No AD. An additional SVC model was trained on the No AD group, distinguishing measurements based onbinarized age groups: measurements taken at 4 months against measurements taken at 3 years of age. The AD model tested on AD flare against No AD within the test set yielded AUC of 0.92, with a sensitivity and specificity of 89.29% and 88.89% respectively. When testing on all groups of the test set, Pre AD and AD remission groups had group means between the AD flare and No AD groups. No data bias against age was detected in the model. The results of the age model showed that age could be chronologically identified by the age model. The AD model was able to differentiate active AD children from children never experiencing AD symptoms on visually unaffected skin and in turn detecting skin barrier dysfunction. Separate studies would need to be conducted to test the predictive power and external validity of the model. Age is a significant factor to consider when designing ML models using EIS data in children, with proper balance in the training set a data bias within the model can be avoided. EIS is a versatile technique due to its data rich nature. Machine learning powered electrical impedance spectroscopy measurements are able to detect skin barrier dysfunction. Age is a significant factor when measuring EIS on children, but can be managed. / Atopisk dermatit (AD) och andra atopiska sjudomar är starkt kopplade tillhudbarrärdefekter, en hudegenskap där diagnosverktygen är begränsade och otillräckliga. Maskininlärning (ML) i kombination med elektrisk impedansspektroskopi (EIS) har visats att kunna differentiera defekta hudbarrärer i möss och hos vuxna människor. Tekniker såsom principalkomponentanalys (PCA) och stödvektormaskiner (SVM) kan användas som ML verktyg för att utvärdera EIS-mätningar. Statistiska analysverktyg såsom reciever operator characteristics (ROC) tillsammans med arean under kurvan (AUC) och andra precisionsmått kan användas för att utvärdera ML modeller. EIS-mätningar utförda på opåverkad hud hos barn mellan 4 månader och 3 års ålder samlades och analyserades med hjälp av barnens atopiska status. Samtliga mätningarna grupperades i en av totalt fyra grupper baserade på deras atopiska status; Ingen AD (Ingen AD hade utvecklats upp till 2 års ålder), Pre AD (mätningarna togs innan sjukdomsförloppet), AD remission (mätningarna togs efter sjukdomsförloppet) och AD aktiv (mätningarn togs under sjukdomsförloppet). En SVM modell tränades på EIS-rådata för att särskilja mellan två av grupperna binärt; AD aktiv och ingen AD. En ytterliggare SVM modell tränades på endast ingen AD-gruppen för att särskilja mellan mätningarna tagna vid åldrarna 4 månader och 3 år, omgjorta till binära grupper.  AD modellen testades med en jämförelse mellan AD aktiv-gruppen och ingen AD-gruppen med test data och resulterade i en AUC på 0.92, med en respektive sensitivitet och specificitet på 89.29\% samt 88.89\%. Modellen testades även på de ytterliggare grupperna i test datan, Pre AD och AD remission som båda hade gruppmedelvärden som låg mellan AD aktiv-gruppens och ingen AD-gruppens. Inga systematiska fel med avseende på barnens ålder hittades i AD-modellen. Resultaten från ålders-modellen visade på att modellen kunde rangordna mätningarna baserat på ålder. AD-modellen kunde differentiera barn med aktiv AD mot barn som aldrig hade uppvisat atopiska symptom på opåverkad hud, vilket innebär en differentiering av hudbarrärdefekter. Separata studier är nödvändiga för att testa prediktiv prestanda hos modellen samt dess externa validitet. Ålder är en signifikant faktor när ML modeller med EIS-data ska designas, men med tillräcklig balans i träningsdatan kan systematiska fel förhindras. EIS är en versatil teknik med avseense på dess datakomplexitet. EIS tillsammans med maskininlärning kan särskilja defekta hudbarriärer hos barn. Ålder är en signifikant faktor när EIS mäts på barn, men kan hanteras.

Submitted by OSWALDO NUNES NETO null (netfisic@fc.unesp.br) on 2016-08-13T20:37:55Z No. of bitstreams: 1 Tese_Doutorado_Oswaldo.pdf: 4276326 bytes, checksum: e73a2086ffde0d12d2f5875fb168f8c1 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-08-16T14:27:21Z (GMT) No. of bitstreams: 1 nunesneto_o_dr_bauru.pdf: 4276326 bytes, checksum: e73a2086ffde0d12d2f5875fb168f8c1 (MD5) / Made available in DSpace on 2016-08-16T14:27:21Z (GMT). No. of bitstreams: 1 nunesneto_o_dr_bauru.pdf: 4276326 bytes, checksum: e73a2086ffde0d12d2f5875fb168f8c1 (MD5) Previous issue date: 2016-04-28 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Materiais e dispositivos baseados em compostos orgânicos desempenham um importante papel em diversas áreas da aplicação tecnológica devido às suas interessantes propriedades eletro-magneto- ópticas, adicionadas às suas características mecânicas únicas, facilidade de processamento, versatilidade de síntese e baixo custo relativo. Apesar do proeminente campo de aplicação destes materiais, muitos aspectos associados à sua ciência básica são ainda pouco compreendidos. Nesse cenário destaca-se o fenômeno de Magnetoresistência Orgânica (OMAR, da sigla em inglês). Tal fenômeno encontra-se associado a variações significativas da condutividade elétrica de dispositivos orgânicos induzidas por pequenos campos magnéticos externos em temperatura ambiente e tem sido observado em diversificados materiais poliméricos e moleculares. No presente trabalho avaliou-se o fenômeno de OMAR apresentado por um Diodo Emissor de Luz baseado na molécula de Alq3. Medidas de Espectroscopia de Impedância Elétrica na presença de um Campo Magnético estático externo (EIE-CM) foram realizadas sobre o referido dispositivo para diferentes temperaturas. Métodos diferenciados de aquisição e manipulação de dados foram empregados a fim de remover a dependência temporal dos sinais tipicamente observados. Os seguintes Efeitos de Campo Magnético (MFE, da sigla em inglês) foram observados sobre a resposta elétrica do dispositivo: (i) redução de cerca de 1% na resistência, efeito praticamente constante para todo o espectro de frequência e; (ii) variações significativas na capacitância, com intensificação do efeito de Capacitância Negativa em baixas frequências. Como suporte para a interpretação dos resultados experimentais foram realizadas simulações empregando-se duas abordagens: Circuitos Equivalentes e Análise de perturbações de pequenos sinais (em inglês, Small Signal Analysis ) via soluções numéricas das equações de transporte de Boltzmann numa aproximação por Drift-Diffusion empregando-se dispositivos simplificados. As análises sugerem que os MFE evidenciados podem estar associados a um aumento da mobilidade efetiva dos portadores de carga e a uma redução na taxa de recombinação bimolecular no dispositivo. Os resultados foram interpretados em termos dos modelos atualmente aceitos para o fenômeno de OMAR. Esta tese também apresenta um estudo de processos de geração e transferência de carga em corantes Cianinas, materiais promissores para aplicações em células solares com absorção no infravermelho. Técnicas de Ressonância de Spin Eletrônico induzida por Luz foram empregadas em blendas destes corantes com o polímero MEH-PPV e com o fulereno (C60) a fim de avaliar, respectivamente, o caráter aceitador e doador de elétrons das Cianinas. / Materials and devices based on organic compounds play an important role in various technological applications, mainly due to their interesting electrical-magneto-optical properties combined with their unique mechanical properties, easy processing, versatility of synthesis and relatively low cost. Despite the prominent application field of these materials many aspects associated with their basic science are still not well understood. In this context the Organic Magnetoresistance phenomenon (OMAR) deserves to be highlighted. This phenomenon is associated with significant changes in the electrical conductivity of organic devices induced by the presence of small external magnetic fields at room temperature, being observed in various polymeric and molecular materials. In this study we have investigated the OMAR phenomenon in Alq3-based OLEDs. Electrical impedance spectroscopy technique in the presence of an external static magnetic field (EIS-MF) was employed in the experiments; distinct temperatures were considered. Differentiated methods of acquisition and data manipulation were employed to remove the typically observed signal time dependence. The following magnetic field effects (MFE) were observed on the electrical response of the device: (i) a constant reduction of around 1% in the resistance over the entire frequency spectrum and; (ii) significant changes in the capacitance followed by an intensification of the negative capacitance effect at low frequencies. Simulations employing two different approaches were carried out for the interpretation of the experimental results: (i) Equivalent Circuits and (ii) Small Signal Analysis via numerical solutions of the Boltzmann transport equations by Drift-Diffusion approach. The results suggest that the observed MFE can be associated with an increase in the effective mobility of the charge carriers and a reduction in the bimolecular recombination rate in the device. The results were interpreted in terms of the currently accepted models for the OMAR phenomenon. This thesis also presents a study about generation and charge transfer processes in cyanine dyes (near infrared absorbing compounds) which are promising materials for applications in solar cells. Light induced Electron Spin Resonance (L-ESR) technique was employed to study the presence/formation of paramagnetic centers in blends of these dyes with MEH-PPV polymer and fullerene (C60) to evaluate, respectively, the electron acceptor and donor character of cyanine dyes. / FAPESP: 2011/21830-6 / CNPq: 204432/2013-8

The growing urgency of climate change has led to growth in the electrification technology field, where batteries have emerged as an essential role in the renewable energy transition, supporting the implementation of environmentally friendly technologies such as smart grids, energy storage systems, and electric vehicles. Battery cell degradation is a common occurrence indicating battery usage. Optimizing lithium-ion battery degradation during operation benefits the prediction of future degradation, minimizing the degradation mechanisms that result in power fade and capacity fade. This degree project aims to investigate battery degradation prediction based on capacity using deep learning methods. Through analysis of battery degradation and health prediction for lithium-ion cells using non-destructive techniques. Such as electrochemical impedance spectroscopy obtaining ECM and three different deep learning models using multi-channel data. Additionally, the AI models were designed and developed using multi-channel data and evaluated performance within MATLAB. The results reveal an increased resistance from EIS measurements as an indicator of ongoing battery aging processes such as loss o active materials, solid-electrolyte interphase thickening, and lithium plating. The AI models demonstrate accurate capacity estimation, with the LSTM model revealing exceptional performance based on the model evaluation with RMSE. These findings highlight the importance of carefully managing battery charging processes and considering factors contributing to degradation. Understanding degradation mechanisms enables the development of strategies to mitigate aging processes and extend battery lifespan, ultimately leading to improved performance.