Adaptation d'un algorithme de deuxième ordre pour l'analyse haute-résolution de courbes électrochimiques

Ce mémoire présente une nouvelle méthode d'analyse des courbes de voltampérométrie cyclique. Cette méthode utilise deux algorithmes distincts afin de permettre la caractérisation automatique et précise des pics gaussiens d'oxydoréduction qui sont liés à la concentration des molécules en solution. En premier lieu, des améliorations significatives sont apportées à un algorithme de suppression de la courbe de fond qui fonctionne par approximation polynomiale itérative. Avec les améliorations proposées, l'algorithme isole les pics d'oxydoréduction à partir des mesures de voltampérométrie cyclique automatiquement. La variation de l'amplitude des pics en fonction de la concentration est alors mieux conservée et les erreurs d'estimation sont diminuées par rapport à l'algorithme initial. Ensuite, le développement d'un algorithme qui permet de caractériser des pics gaussiens basé sur l'algorithme de deuxième ordre MUSIC est présenté. Cet algorithme est adapté de manière à caractériser avec une haute précision le nombre, la position, la largeur et l'amplitude des pics d'oxydoréduction. Finalement, les performances de cet algorithme sont comparées à celles d'autres algorithmes similaires à l'aide de courbes simulées et expérimentales. L'algorithme proposé permet une meilleure caractérisation des pics sans chevauchement ainsi que des pics déformés. Il permet aussi de diminuer la fréquence des fausses détections et d'obtenir une haute précision de la mesure de position, et ce même lorsque les signaux sont bruités.This master's thesis describes a new method for analyzing cyclic voltammetry curves for an efficient peak detection and automatic baseline substraction. This method uses two distinct algorithms for a precise characterization of Gaussian redox peaks which are correlated with molecules' concentration in a solution. First, significant improvements are made to an existing algorithm that uses iterative polynomial approximations to suppress the baseline automatically from the voltammetric curves. With these enhancements, the algorithm extracts redox peaks from cyclic voltammetry measurements automatically and allows a better representation of the variation of peak's amplitude according to concentration. In addition, the approximation errors are reduced compared to the initial algorithm. Then, the development of an algorithm for characterizing Gaussian peaks based on the MUSIC second-order algorithm is presented. This algorithm is adapted to characterize the number, position, width and amplitude of redox peaks with high accuracy. Finally, the performances of this algorithm are compared with those of other similar algorithms using simulated and experimental curves. The suggested algorithm leads to a better characterization of non-overlapping peaks as well as distorted peaks. It also reduces the frequency of false detections and allows the precise measurement of peaks' positions in noisy signals

Simulation Methods for the Transient Analysis of Synchronous Alternators

The integration of unconventional renewable energy sources on the electrical grid poses challenges to the electrical engineer. This chapter focuses on the transient modeling of electrical machines. These models can be used for the design of generator control, the definition of the protection strategies, stability studies, and the evaluation of the electrical; mechanical; and thermal constraints on the machine. This chapter presents three modeling techniques: the standard d-q equivalent model, the coupled-circuit model, and the finite element model (FEM). The consideration of magnetic saturation for the different models is presented. The responses of the different models during three-phase, two-phase, and one-phase sudden short circuit are compared

Electrochemical Detection of Dopamine Based on Functionalized Electrodes

The rapid electrochemical identification and quantification of neurotransmitters being a challenge in the ever-growing field of neuroelectronics, we aimed to facilitate the electrochemical selective detection of dopamine by functionalizing commercially available electrodes through the deposition of a thin film containing pre-formed gold nanoparticles. The influence of different parameters and experimental conditions, such as buffer solution, fiber material, concentration, and cyclic voltammetry (CV) cycle number, were tested during neurotransmitter detection. In each case, without drastically changing the outcome of the functionalization process, the selectivity towards dopamine was improved. The detected oxidation current for dopamine was increased by 92%, while ascorbic acid and serotonin oxidation currents were lowered by 66% under the best conditions. Moreover, dopamine sensing was successfully achieved in tandem with home-made triple electrodes and an in-house built potentiostat at a high scan rate mode

Electrochemical imaging for microfluidics : a full-system approach

Electrochemistry is developed as a new chemical imaging modality for microfluidics. The technique is based on multipoint voltammetry using an embedded 20 × 10 miniature electrode array implemented on a customized printed circuit board. Electrode durability was enhanced by chemical modification of the electrode surfaces, which enabled continuous, stable use for over 2 months. A system-level approach enables automatic calibration, data acquisition and data processing through a graphical user interface. Following data processing, redox currents and peak positions are extracted from location-specific voltammograms and converted into pixels of an “electrochemical image”. The system is validated by imaging steady-state and dynamic laminar flow patterns of flow-confined solutions of the redox pairs FeIJCN)6 3−/4− or multi-redox environments that include coflowing RuIJNH3)6 2+/3+ solutions. The images obtained are compared with flow simulations and optical images for validation. A strategy to achieve measurements with spatial resolution smaller than the individual electrodes is also demonstrated as an avenue to enhance image spatial resolution. It is expected that this new approach to chemical imaging will expand the applicability of microfluidics in certain areas of chemistry and biology without requiring expertise in electrochemistr

Miniaturized FDDA and CMOS Based Potentiostat for Bio-Applications

A novel fully differential difference CMOS potentiostat suitable for neurotransmitter sensing is presented. The described architecture relies on a fully differential difference amplifier (FDDA) circuit to detect a wide range of reduction-oxidation currents, while exhibiting low-power consumption and low-noise operation. This is made possible thanks to the fully differential feature of the FDDA, which allows to increase the source voltage swing without the need for additional dedicated circuitry. The FDDA also reduces the number of amplifiers and passive elements in the potentiostat design, which lowers the overall power consumption and noise. The proposed potentiostat was fabricated in 0.18 µm CMOS, with 1.8 V supply voltage. The device achieved 5 µA sensitivity and 0.99 linearity. The input-referred noise was 6.9 µV rms and the flicker noise was negligible. The total power consumption was under 55 µW. The complete system was assembled on a 20 mm × 20 mm platform that includes the potentiostat chip, the electrode terminals and an instrumentation amplifier for redox current buffering, once converted to a voltage by a series resistor. the chip dimensions were 1 mm × 0.5 mm and the other PCB components were off-chip resistors, capacitors and amplifiers for data acquisition. The system was successfully tested with ferricyanide, a stable electroactive compound, and validated with dopamine, a popular neurotransmitter

A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research

Neurotransmitters as electrochemical signaling molecules are essential for proper brain function and their dysfunction is involved in several mental disorders. Therefore, the accurate detection and monitoring of these substances are crucial in brain studies. Neurotransmitters are present in the nervous system at very low concentrations, and they mixed with many other biochemical molecules and minerals, thus making their selective detection and measurement difficult. Although numerous techniques to do so have been proposed in the literature, neurotransmitter monitoring in the brain is still a challenge and the subject of ongoing research. This article reviews the current advances and trends in neurotransmitters detection techniques, including in vivo sampling and imaging techniques, electrochemical and nano-object sensing techniques for in vitro and in vivo detection, as well as spectrometric, analytical and derivatization-based methods mainly used for in vitro research. The document analyzes the strengths and weaknesses of each method, with the aim to offer selection guidelines for neuro-engineering research

Towards a Multifunctional Electrochemical Sensing and Niosome Generation Lab-on-Chip Platform Based on a Plug-and-Play Concept

In this paper, we present a new modular lab on a chip design for multimodal neurotransmitter (NT) sensing and niosome generation based on a plug-and-play concept. This architecture is a first step toward an automated platform for an automated modulation of neurotransmitter concentration to understand and/or treat neurodegenerative diseases. A modular approach has been adopted in order to handle measurement or drug delivery or both measurement and drug delivery simultaneously. The system is composed of three fully independent modules: three-channel peristaltic micropumping system, a three-channel potentiostat and a multi-unit microfluidic system composed of pseudo-Y and cross-shape channels containing a miniature electrode array. The system was wirelessly controlled by a computer interface. The system is compact, with all the microfluidic and sensing components packaged in a 5 cm × 4 cm × 4 cm box. Applied to serotonin, a linear calibration curve down to 0.125 mM, with a limit of detection of 31 μ M was collected at unfunctionalized electrodes. Added sensitivity and selectivity was achieved by incorporating functionalized electrodes for dopamine sensing. Electrode functionalization was achieved with gold nanoparticles and using DNA and o-phenylene diamine polymer. The as-configured platform is demonstrated as a central component toward an “intelligent” drug delivery system based on a feedback loop to monitor drug delivery