A machine learning approach for the estimation of photocatalytic activity of ALD ZnO thin films on fabric substrates

Abstract

Research in the field of photocatalytic wastewater treatment is striving to enhance catalyst materials to achieve high-performance systems. A promising approach to this goal has been immobilizing photocatalytic materials on fibrous substrates via atomic layer deposition (ALD). Nevertheless, both the ALD process and the assessment of photocatalytic performance involve a multitude of parameters necessitating thorough investigation. In this study, we employ popular machine-learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), to predict the photocatalytic activity of ALD-coated textiles. The photocatalytic activity is evaluated through methylene blue and methyl orange degradation tests. Machine learning algorithms are tested and trained using the k-fold cross-validation technique. The findings demonstrate that the ANN and SVR methods utilized in this research can predict catalytic activity with mean absolute percentage errors (MAPE) of 2.35 and 3.25, respectively. This study illuminates that, within the defined range of process parameters, the photocatalytic activity of ALD-coated textiles can be precisely estimated with suitable machine-learning algorithms.