A multi-process model for the photocatalytic reduction of CO₂

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Photocatalytic conversion of carbon dioxide (CO2) offers a sustainable route for CO2 utilization and chemical production.
• Complex multi-processes and physical phenomena in photocatalysis hinder performance optimization.
• A comprehensive computational model is needed to understand and enhance CO2 reduction efficiency.

Purpose of the Study:

• To develop a multi-process computational framework for simulating the entire photocatalytic CO2 reduction process.
• To analyze the performance of rGO-MoS2/PPy catalysts using the developed multiscale model.
• To identify key factors affecting catalyst activity, selectivity, and energy loss.

Main Methods:

• Density Functional Theory (DFT) for catalyst property determination.
• A framework combining optical absorption, carrier transport, micro-kinetic, and continuum transport models.
• Simulation and analysis of photocatalytic CO2 reduction using the multiscale model.

Main Results:

• The computational framework successfully simulated the photocatalytic CO2 reduction process.
• Key factors influencing catalyst activity and selectivity for rGO-MoS2/PPy were identified.
• Energy loss mechanisms across different stages of the photocatalytic process were clarified.

Conclusions:

• The developed multiscale model serves as a valuable theoretical tool for analyzing and improving photocatalytic materials.
• Understanding energy loss pathways can guide the design of more efficient CO2 conversion systems.
• This approach facilitates the advancement of sustainable CO2 utilization technologies.