Development and testing of an efficient data acquisition platform for machine learning of optical emission spectroscopy of plasmas in aqueous solution

Abstract

This study presents the development of an efficient data acquisition platform and discusses machine learning of optical emission spectroscopy (OES) of plasma in aqueous solution to demonstrate the multivariate analysis of spectra. A specially designed platform for efficient acquisition of spectra emanated from plasmas in solutions is developed, and several machine learning algorithms are tested for plasma analysis. This platform enables acquiring up to 10k spectra solutions with various pH values under constant solution conductivity, or vice versa, within 15 min. This rapid acquisition scheme provides a sufficient dataset for testing machine learning algorithms. We test the OES of plasmas ignited in solutions with designated conductivities with pH of 2.2-5.2. A total 40k spectra are collected and tested with principal component analysis (PCA) and an artificial neural network (ANN) to predict the conductivity of the solution. In PCA, the results show that most data points are overlapped in the score plot constructed using principal components 1 and 2, implying that PCA cannot discriminate the conductivity based on the spectra. In an ANN, several network structures are constructed and tested. The results show that the deep ANN significantly improves the accuracy of conductivity prediction in terms of mean squared error by three orders of magnitude compared with the method of using single emission line. Regularization techniques including dropout and early stopping show promise for mitigating overfitting in a deep ANN. Such improvements suggest that a deep ANN considers the nonlinear behaviors of plasma and can handle datasets with high complexity. In addition, the application of a deep ANN with a large number of parameters makes using this experimental platform for efficient spectra acquisition highly desirable.

Machine Learning with Explainable Artificial Intelligence Vision for Characterization of Solution Conductivity Using Optical Emission Spectroscopy of Plasma in Aqueous Solution

“…Further, the ReLU in the hidden layers could be used to perform the nonlinear transformation of spectra, thereby handling the plasma nonlinearity. In our previous study, [27] a deep ANN model with three hidden layers showed higher conductivity prediction accuracy than a shallow ANN model with one hidden layer. Increasing the number of hidden layers and parameters could lead to overfitting.…”

“…This platform can acquire plasma OES with high efficiency; its details have been reported in our previous study. [27] This platform consisted of a plasma generation unit and a spectrometer. The plasma generation unit was a 30 mL glass cell with discharge and ground electrodes inside.…”

“…The high accuracy of the prediction by the ANN model indicated the advantages of using ANN for spectral analysis, as reported in our previous study. [27] The behaviors of plasma and OES were highly coupled with various factors in surrounding environments, such as solution properties, solution compositions, temperature, volage and current waveforms, bubble dynamics, etc. [1,13,15,16] These factors resulted in the nonlinearity and nonuniformity of plasma and OES.…”

“…[25,26] In addition, our previous study introduced the algorithm of artificial neural network (ANN) for plasma OES analysis to predict the conductivity of aqueous solution in which plasma is ignited. [27] The results show that employing ANN greatly improves the prediction accuracy of the solution conductivity, with the mean square error (MSE) being three orders of magnitude lower. Even though impressive results are demonstrated, how or why the machine learning models make certain predictions remains unclear because these typically operate as black-boxes.…”

Machine Learning Prediction of Electron Density and Temperature from Optical Emission Spectroscopy in Nitrogen Plasma

“…In nitrogen plasmas, the atomic nitrogen species are mainly produced by electron-impact processes such as the dissociative collisions between electrons and nitrogen molecules or between electrons and nitrogen molecular ions (dissociative recombination) [8]. The most complete studies on the dissociative recombination of the nitrogen molecular cations can be found in Little et al [19] and Abdoulanziz et al [20]. Thus, the SPS and FNS depend on the electron temperature, and this trend is in good agreement with the electron temperature of the PN-ICP system (in Figure 2b).…”