De nombreux systèmes électrochimiques sont composés d'un grand nombre d'éléments électroactifs en interaction. Si la réaction électrochimique possède une cinétique non linéaire, des comportements coopératifs complexes peuvent émerger suivant la nature et l’intensité des interactions entre les éléments du système. L'objectif de cette thèse est de comprendre l'influence de la taille finie de l’électrode et des interactions entre les microélectrodes sur le comportement coopératif d'un groupe de microélectrodes de platine soumis à un couplage global. Les réactions choisies pour cette étude sont l’électrooxydation du monoxyde de carbone (CO), une réaction avec une cinétique bistable et l’électrooxydation du formaldéhyde (HCHO), qui présente des oscillations de potentiel sous contrôle galvanostatique. Au cours de l’électrooxydation galvanodynamiques du CO sur une seule microélectrode de Pt, la branche S-NDR a pu être mise en évidence contrairement au comportement observé sur une macroélectrode de Pt. En outre, les nouveaux comportements coopératifs comme l'activation séquentielle des microélectrodes, des oscillations de courant et de potentiel spontanées et un régime de commutation dynamique entre les électrodes ont été découverts pour cette réaction lorsque quatre électrodes ont été couplées globalement. Pendant l’électrooxydation de HCHO, l'introduction du couplage global à deux électrodes conduit à des oscillations de courant en opposition de phase. / Many electrochemical systems are composed of a large number of interacting electroactive elements. If the reaction taking place on them has nonlinear kinetics and their interactions allow them to exchange information, complex cooperative behaviors can emerge. The objective of this thesis is to understand the influence of finite-size effects and cooperative phenomena on the global behavior of a group of coupled Pt microelectrodes. The reactions chosen for this study were CO electrooxidation, a reaction with current bistability, and HCHO electrooxidation, which exhibits oscillations under galvanostatic control. During the galvanodynamic electrooxidation of CO on a single microelectrode the S-NDR branch could be evidenced, on macroelectrodes this is not possible due to the formations of stationary domains. Additionally, novel cooperative behaviors (i.e., sequential activation, oscillations and complex switching) were discovered for this reaction when four electrodes were globally coupled. During HCHO electrooxidation the introduction of global coupling to two electrodes led to anti-phase current oscillations.

Bioimpedance presents a versatile, label-free means of monitoring biological cells and their responses to physical, chemical and biological stimuli. Breast cancer is the second most common type of cancer among women in the United States. Although significant progress has been made in diagnosis and treatment of this disease, there is a need for robust, easy-to-use technologies that can be used for the identification and discrimination of critical subtypes of breast cancer in biopsies obtained from patients. This dissertation makes contributions in three major areas towards addressing the goal. First, we developed miniaturized bioimpedance sensors using MEMS and microfluidics technology that have the requisite traits for clinical use including reliability, ease-of-use, low-cost and disposability. Here, we designed and fabricated two types of bioimpedance sensors. One was based on electric cell-substrate impedance sensing (ECIS) to monitor cell adhesion based events and the other was a microfluidic device with integrated microelectrodes to examine the biophysical properties of single cells. Second, we examined a panel of triple negative breast cancer (TNBC) cell lines and a hormone therapy resistant model of breast cancer in order to improve our understanding of the bioimpedance spectra of breast cancer subtypes. Third, we explored strategies to improve the sensitivity of the microelectrodes to bioimpedance measurements from breast cancer cells. We investigated nano-scale coatings on the surface of the electrode and geometrical variations in a branched electrode design to accomplish this. This work demonstrates the promise of bioimpedance technologies in monitoring diseased cells and their responses to pharmaceutical agents, and motivates further research in customization of this technique for use in personalized medicine. / Ph. D.

Optogenetics is a set of technologies that enable optically triggered gain or loss of function in genetically specified populations of cells. Optogenetic methods have revolutionized experimental neuroscience by allowing precise excitation or inhibition of firing in specified neuronal populations embedded within complex, heterogeneous tissue. Although optogenetic tools have greatly improved our ability manipulate neural activity, they do not offer control of neural firing in the face of ongoing changes in network activity, plasticity, or sensory input. In this thesis, I develop a feedback control technology that automatically adjusts optical stimulation in real-time to precisely control network activity levels. I describe hardware and software tools, modes of optogenetic stimulation, and control algorithms required to achieve robust neural control over timescales ranging from seconds to days. I then demonstrate the scientific utility of these technologies in several experimental contexts. First, I investigate the role of connectivity in shaping the network encoding process using continuously-varying optical stimulation. I show that synaptic connectivity linearizes the neuronal response, verifying previous theoretical predictions. Next, I use long-term optogenetic feedback control to show that reductions in excitatory neurotransmission directly trigger homeostatic increases in synaptic strength. This result opposes a large body of literature on the subject and has significant implications for memory formation and maintenance. The technology presented in this thesis greatly enhances the precision with which optical stimulation can control neural activity, and allows causally related variables within neural circuits to be studied independently.

This dissertation presents an investigation of micromachined three-dimensional microelectrode arrays (3-D MEAs) targeted toward in-vitro and in-vivo biomedical applications. Current 3-D MEAs are predominantly silicon-based, fabricated in a planar fashion, and are assembled to achieve a true 3-D form: a technique that cannot be extended to micro-manufacturing. The integrated 3-D MEAs developed in this work are polymer-based and thus offer potential for large-scale, high volume manufacturing. Two different techniques are developed for microfabrication of these MEAs - laser micromachining of a conformally deposited polymer on a non-planar surface to create 3-D molds for metal electrodeposition; and metal transfer micromolding, where functional metal layers are transferred from one polymer to another during the process of micromolding thus eliminating the need for complex and non-repeatable 3-D lithography processes. In-vitro and in-vivo 3-D MEAs are microfabricated using these techniques and are packaged utilizing Printed Circuit Boards (PCB) or other low-cost manufacturing techniques. To demonstrate in-vitro applications, growth of 3-D co-cultures of neurons/astrocytes and tissue-slice electrophysiology with brain tissue of rat pups were implemented. To demonstrate in-vivo application, measurements of nerve conduction were implemented. Microelectrode impedance models, noise models and various process models were evaluated. The results confirmed biocompatibility of the polymers involved, acceptable impedance range and noise of the microelectrodes, and potential to improve upon an archaic clinical diagnostic application utilizing these 3-D MEAs.

Establishing and maintaining successful communication stands as a critical prerequisite for achieving the goals of inducing and studying advanced computation in small-scale living neuronal networks. The following work establishes a novel and effective method for communicating arbitrary "sensory" input information to cultures of living neurons, living neuronal networks (LNNs), consisting of approximately 20 000 rat cortical neurons plated on microelectrode arrays (MEAs) containing 60 electrodes. The sensory coding algorithm determines a set of effective codes (symbols), comprised of different spatio-temporal patterns of electrical stimulation, to which the LNN consistently produces unique responses to each individual symbol. The algorithm evaluates random sequences of candidate electrical stimulation patterns for evoked-response separability and reliability via a support vector machine (SVM)-based method, and employing the separability results as a fitness metric, a genetic algorithm subsequently constructs subsets of highly separable symbols (input patterns). Sustainable input/output (I/O) bit rates of 16-20 bits per second with a 10% symbol error rate resulted for time periods of approximately ten minutes to over ten hours. To further evaluate the resulting code sets' performance, I used the system to encode approximately ten hours of sinusoidal input into stimulation patterns that the algorithm selected and was able to recover the original signal with a normalized root-mean-square error of 20-30% using only the recorded LNN responses and trained SVM classifiers. Response variations over the course of several hours observed in the results of the sine wave I/O experiment suggest that the LNNs may retain some short-term memory of the previous input sample and undergo neuroplastic changes in the context of repeated stimulation with sensory coding patterns identified by the algorithm.

Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signalto-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently iv proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the v planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research.