High-Throughput Photocatalysis for Generating Reliable Datasets Analyzed by Machine Learning.

ABSTRACT: Photocatalysis is an environmentally conscious tool for removing contaminants from water. Novel photocatalytic materials are often measured on ability to degrade a small number of analytes, which may not be indicative of broader applicability. In this work, an experimental method dubbed high-throughput photocatalysis (HTP) is introduced to assay photocatalytic materials against a range of analytes in a time effective manner. HTP is modular; experimental parameters, including matrix, can be changed to fit a proposed application. The photodegradation of each analyte is attained in a consistent manner such that machine learning (ML) models can be applied to the obtained datasets. Three out of the box ML models-linear regression, random forest (RF), and neural network (NN)-are tasked with esti

SUBMITTER: Croxall M 

PROVIDER: S-EPMC12677721 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature