Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine

Abstract

Plasma‐based biomedical applications rely on the reactive oxygen and nitrogen species generated in cold atmospheric plasmas, where complex chemical kinetic schemes occur. The optimization of plasma medicine is thus required for each specific biomedical purpose. In the view of pharmacology, it is to optimize the active pharmaceutical ingredients. This work is thus the first attempt of such a complex task utilizing the recent development of machine learning technologies. Herein, a general method of passive plasma chemical diagnostics and optimization in real time is proposed. Based on spontaneous emission spectroscopy, an artificial neural network provides the gas chemical compositions along with other information such as temperatures. The information further passes through the second neural network which outputs the adjustments of external control inputs including energy, gas injections, and extractions to optimize the plasma chemistry.