Computation-assisted performance optimization for photoelectrochemical photoelectrodes

The generation rate and collection efficiency of photocarriers fatally determine the photoelectrochemical performance of photoelectrodes. However, it is challenging to simultaneously reach a high generation rate and a high collection efficiency due to their conflictive dependence on the thickness of photocatalytic films, especially for metal oxide photocatalysts. Therefore, it is critical to select an appropriate thickness to reach the highest photocatalytic rate under certain light illustration. Herein, we proposed a physical model to predict the optimal thickness of photocatalytic films by combining computation and experiments. In this model, a photoelectrode was investigated by thoroughly considering the electric potential distribution in the whole photocatalytic film rather than only considering the depletion layer as previously. We solved the continuity equation and got the distribution of minority carriers in photocatalytic films. The used parameters for calculation were obtained through density functional theory calculation and experiments. The optimal thickness of photocatalytic films can be predicted with this model. We have used CuFeO2 films as the model material to verify the accuracy of the proposed model. Compared to the traditional trial-and-error process, our computation-assisted approach is highly efficient and can be broadly employed to other materials.