Deciphering of bio-electrochemical activities generated during Argon-Plasma-Jet deformation of bacteria cell morphology using Machine Learning

This project utilizes and explores the potential use of machine learning techniques to predict the activities during an experiment of bacterial-cell inactivation using argon-plasma-jet. Our experiments have confirmed that argon-plasma-jet can be used for bacterial cell deformation or killing microorganisms. A targeted high-energy low-temperature argon-plasma-jet is projected on microorganisms and it completely destroys those organisms. There are more than 250 known and unknown chemical reactions occuring during that process depending on different operating conditions. Beside these, high local-electric-field and photons coming out from the jet front also active at the same time. Experimentally, it is quite impossible to track the synergic effect of plasma species and the jets electric field on the cell. This proposed work is the initial step to understand and predict this bio-electrochemical process through computer simulation, to have a better control on the jet-surface interaction process, and its further application. We will do the electro dynamic modelling of the plasma jet using differential machine learning modeling technique. This project is collaborated with Kanazawa university Japan (

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