Machine Learning for Real-Time Diagnostics of Cold Atmospheric Plasma Sources

Abstract

Real-time diagnostics of cold atmospheric plasma (CAP) sources can be challenging due to the requirement for expensive equipment and complicated analysis. Data analytics that rely on machine learning (ML) methods can help address this challenge. In this paper, we demonstrate the application of several ML methods for real-time diagnosis of CAPs using information-rich optical emission spectra and electro-acoustic emission. We show that data analytics based on ML can provide a simple and effective means for estimation of operation-relevant parameters such as rotational and vibrational temperature and substrate characteristic in real-time. Our findings indicate a great potential promise for ML for real-time diagnostics of CAPs.