Functionalized conducting polymers in photocatalysis and opportunities for artificial intelligence applications

Abstract

Water systems are being polluted by emerging contaminants at a staggering rate of 500 million tons annually. Various techniques have approached the treatment of pollutants, with photocatalysis being a viable method. Current research delved into the functionalization and nanohybridization of conducting polymers through photocatalysis in the degradation of dyes, heavy metals, and other pollutants. This review investigated the recent advancements in using functionalized conducting polymers and their composites in removing contaminants and highlights their various environmental benefits. Conducting polymers integrate dopants and heterojunctions to optimize the performance, which can, in turn, reduce wastewater pollution. Conducting polymers play a role in hydrogen production and carbon dioxide (CO₂) reduction. This review presented the role of artificial intelligence models in the optimization and prediction of degradation rates in photocatalytic processes. This review concludes that amongst the models reviewed, artificial neural networks and genetic algorithms appeared to achieve the most accurate results with an R-squared and error value of 0.998 and 1.83×10⁻⁴, respectively. Despite the progress achieved in this direction, various issues pertaining to the generalization of the experimental data to a larger scale persist. This exposes the challenges in designing well-optimized photocatalytic-membrane systems.