Probabilistic Features of Photocatalytic CO₂ Conversion

Photocatalytic CO₂ conversion involves stochastic events comprising interactions of incident photons, charge carriers, and surface molecules. However, the probabilistic features of photocatalytic processes remain unclear. Herein, a probabilistic framework is proposed to analyze the random characteristics of photocatalytic CO₂ conversion by coupling charge carrier random behaviors with surface reaction kinetics. The probabilistic mechanism of the Gibbs energy change ΔG = −nFE is elucidated in photocatalytic CO₂ conversion. Parameter correlation analysis shows that the surface electron density is primarily influenced by the light intensity and active site density. Crucially, a criterion for screening optimal cocatalysts of various photocatalysts─including oxides, sulfides, titanates, and nitrides─is established by optimizing the potential difference between the photocatalyst and cocatalyst. Moreover, regulation strategies are suggested to minimize irreversible losses in light absorption, carrier transport, and surface reactions for photocatalysts. The experimental results validate the universality of the method, further clarifying that the cause of the limited number of electron transfers in the reaction is the mismatch between the reactant supply and carrier transfer probabilities. This work establishes a probabilistic approach to analyzing the stochastic energy losses in photocatalytic CO₂ reduction, providing guidance for efficient catalyst design and optimization.