Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine
Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine
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DOI：
10.1002/aisy.202100112
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发表时间：
2021-10
影响因子：
7.4
通讯作者：
Li Lin;D. Yan;Taeyoung Lee;M. Keidar
中科院分区：
文献类型：
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作者：
Li Lin;D. Yan;Taeyoung Lee;M. Keidar
基于等离子体的生物医学应用依赖于冷大气等离子体中产生的活性氧和氮物质，其中发生复杂的化学动力学方案。因此，每个特定的生物医学目的都需要优化血浆药物。从药理学的角度来看，就是对药物活性成分进行优化。因此，这项工作是利用机器学习技术的最新发展来完成这种复杂任务的第一次尝试。本文提出了一种真实的实时被动等离子体化学诊断和优化的通用方法。基于自发辐射光谱，人工神经网络提供气体化学成分沿着其他信息，如温度。该信息进一步通过第二神经网络，该第二神经网络输出对包括能量、气体注入和提取的外部控制输入的调整，以优化等离子体化学。
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.