Photocatalytic Activity: Computational Estimation, Optimization, and Degradation Processes

The unique hybridization capability, high surface-to-volume ratio, and control over porosity in self-assembled carbon scaffolds (SAC) have made them a promising photocatalytic material. The chapter provides a coupled experimental and computational analysis of these materials using density functional theory (DFT) and machine learning (ML) approaches. Features like optimization of light absorption, charge carrier separation, and reactivity of the surface have been addressed. Enhanced hydrogen evolution, pollution degradation, and CO 2 reduction by means of structural modification and defect engineering were also discussed. Molecular dynamics simulations provided insights into interfacial charge transfer, exciton dynamics, and phonon-assisted transitions. Synergistic modulation in the optical framework with carbon frameworks was shown with the help of computational modeling. Reactor design considerations, including radiative transfer modeling and mass transport analysis, were addressed for scale-up potential. All the discussions provided showed the roadmap for SAC in getting developed showing superior photocatalytic efficiency, stability, and recyclability, paving the way for eco-friendly, large-scale energy and environmental applications.