Machine learning for CO<sub>2</sub> conversion driven by dielectric barrier discharge plasma and Cs<sub>2</sub>TeCl<sub>6</sub> photocatalysts
Yangyi Shen, Chengfan Fu, Wen Luo, Zhiyu Liang, Z. Wang, Qiang Huang
Abstract
An effective prediction model was established based on the BPANN to reduce the consumption of experimental resources. The effect of each process parameter on conversion efficiency was also quantified, which could facilitate future experimental design.
Topics & Concepts
Dielectric barrier dischargePlasmaDielectricProcess (computing)Materials scienceComputer scienceOptoelectronicsPhysicsQuantum mechanicsOperating systemMachine Learning in Materials ScienceCO2 Reduction Techniques and CatalystsGas Sensing Nanomaterials and Sensors