Arbitrary EEDF determination of atmospheric-pressure plasma by applying machine learning to OES measurement

A new method to determine the arbitrary electron energy distribution function (EEDF) from the optical emission spectroscopic measurement in atmospheric-pressure plasma is introduced. The optical emission spectroscopy (OES) continuum emission spectrum, dominated by electron-neutral bremsstrahlung radiation, is analyzed to inspect the usefulness of the conventional OES measurement range for EEDF determination. The EEDF is reconstructed from the OES continuum radiation spectrum by applying machine learning to solve the bremsstrahlung emissivity equation inversely. Through iterative statistical analysis, the presented genetic algorithm can locate the EEDF reliably. Verification of the algorithm shows that theoretical Maxwellian and Druyvesteynian EEDFs can be partially reconstructed from a realistic OES measurement range. Furthermore, preliminary experimental EEDF results of an argon dielectric barrier discharge (DBD) OES measurement are given. The electron energy range and resolution of the determined EEDF are discussed. The results in this paper show potential for accurate determination of the arbitrary EEDF in atmospheric-pressure plasma using simple OES equipment.

Automated summary

This study introduces a new method for determining the arbitrary electron energy distribution function in atmospheric-pressure plasma using optical emission spectroscopic measurement. By analyzing the continuum emission spectrum and applying machine learning techniques, the EEDF can be reconstructed and reliably located using a genetic algorithm. This research shows the potential for accurate determination of the EEDF in atmospheric-pressure plasma using simple OES equipment.