Improving silicon probe performance through layer-by-layer coating

Fully comprehending brain function, as the scale of neural networks, will only be possi-ble with the development of tools by micro and nanofabrication. Regarding specifically silicon microelectrodes arrays, a significant improvement in long-term performance of these implants is essential. This project aims to create a silicon microelectrode coating that provides high-quality electrical recordings, while limiting the inflammatory response of chronic implants. To this purpose, a combined chitosan and gold nanoparticles coating was produced allied with electrodes modification by electrodeposition with PEDOT/PSS in order to reduce the im-pedance at 1kHz. Using a dip-coating mechanism, the silicon probe was coated and then charac-terized both morphologically and electrochemically, with focus on the stability of post-surgery performance in anesthetized rodents. Since not only the inflammatory response analysis is vital, the electrodes recording degradation over time was also studied. The produced film presented a thickness of approximately 50 μm that led to an increase of impedance of less than 20 kΩ in average. On a 3 week chronic implant, the impedance in-crease on the coated probe was of 641 kΩ, compared with 2.4 MΩ obtained for the uncoated probe. The inflammatory response was also significantly reduced due to the biocompatible film as proved by histological tests.

Modeling and optimization of extracellular polysaccharides production by Enterobacter A47

Polysaccharides are gaining increasing attention as potential environmental friendly and sustainable building blocks in many fields of the (bio)chemical industry. The microbial production of polysaccharides is envisioned as a promising path, since higher biomass growth rates are possible and therefore higher productivities may be achieved compared to vegetable or animal polysaccharides sources. This Ph.D. thesis focuses on the modeling and optimization of a particular microbial polysaccharide, namely the production of extracellular polysaccharides (EPS) by the bacterial strain Enterobacter A47. Enterobacter A47 was found to be a metabolically versatile organism in terms of its adaptability to complex media, notably capable of achieving high growth rates in media containing glycerol byproduct from the biodiesel industry. However, the industrial implementation of this production process is still hampered due to a largely unoptimized process. Kinetic rates from the bioreactor operation are heavily dependent on operational parameters such as temperature, pH, stirring and aeration rate. The increase of culture broth viscosity is a common feature of this culture and has a major impact on the overall performance. This fact complicates the mathematical modeling of the process, limiting the possibility to understand, control and optimize productivity. In order to tackle this difficulty, data-driven mathematical methodologies such as Artificial Neural Networks can be employed to incorporate additional process data to complement the known mathematical description of the fermentation kinetics. In this Ph.D. thesis, we have adopted such an hybrid modeling framework that enabled the incorporation of temperature, pH and viscosity effects on the fermentation kinetics in order to improve the dynamical modeling and optimization of the process. A model-based optimization method was implemented that enabled to design bioreactor optimal control strategies in the sense of EPS productivity maximization. It is also critical to understand EPS synthesis at the level of the bacterial metabolism, since the production of EPS is a tightly regulated process. Methods of pathway analysis provide a means to unravel the fundamental pathways and their controls in bioprocesses. In the present Ph.D. thesis, a novel methodology called Principal Elementary Mode Analysis (PEMA) was developed and implemented that enabled to identify which cellular fluxes are activated under different conditions of temperature and pH. It is shown that differences in these two parameters affect the chemical composition of EPS, hence they are critical for the regulation of the product synthesis. In future studies, the knowledge provided by PEMA could foster the development of metabolically meaningful control strategies that target the EPS sugar content and oder product quality parameters.