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Abstract In order to deal with the global energy crisis and environmental problems, reducing carbon dioxide through artificial photosynthesis has become a hot topic. Lead halide perovskite is attracted people's attention because of its excellent photoelectric properties, but the toxicity and long‐term instability prompt people to search for new photocatalysts. Herein, a series of inorganic double perovskites Cs4Mn1‐xCuxSb2Cl12 microcrystals (x = 0, 0.1, 0.2, 0.3, 0.4, and 0.5) are synthesized and characterized. Among them, Cs4Mn0.7Cu0.3Sb2Cl12 microcrystals have the best photocatalytic performance, and the yields of CO and CH4 are 503.86 and 68.35 µmol g−1, respectively, after 3 h irradiation, which are the highest among pure phase perovskites reported so far. In addition, in situ Fourier transform infrared (FT‐IR) spectroscopy and electron spin resonance (ESR) spectroscopy are used to explore the mechanism of the photocatalytic reaction. The results highlight the potential of this class of materials for photocatalytic reduction reactions.

CsPbBr3 quantum dots were grown on ReS2 nanosheets to form CsPbBr3@ReS2 heterojunctions using an anti-solvent method. The composition, morphology, spatial distribution, and optical absorption of samples were characterized. CsPbBr3@ReS2-15 exhibits not only a higher photocatalytic performance than CsPbBr3 due to the improved optical absorption and Z-scheme charge migration, but also a higher CO2 conversion ratio (35.60%) and energy efficiency (13.10%) in the dielectric barrier discharge (DBD) plasma due to superior photocatalytic activity, increased micro-discharge time, and improved discharge uniformity. This work provides a strategy for plasma photocatalytic CO2 conversion. [ABSTRACT FROM AUTHOR]

Although the combination of halide perovskite photocatalysts and plasma ensures the effective conversion of CO2, there is still much room to improve its conversion ratio and energy efficiency. The traditional experimental trial-and-error method is extremely demanding for researchers in each experimental operation and result analysis, while the experiments greatly consume time and raw materials and require complex equipment. In this paper, for the first time, we modeled the process of CO2 conversion synergistically driven by dielectric barrier discharge (DBD) plasma and a Cs2TeCl6 photocatalyst via machine learning. K-fold cross-validation combined with the coefficient of determination (R2) was used to evaluate the regression algorithms, and the BPANN with the best performance was selected to establish a model for predicting the CO2 conversion ratio and energy efficiency. In order to make the predictions more accurate, genetic algorithms, particle swarm optimization and Bayesian optimization were applied to improve the hyperparameters of the neural network, and the GA-BPANN model achieved an R² of 0.9713 and 0.9622 on the training and testing sets, respectively, while its practical application was also demonstrated. In addition, the effect of each process parameter on conversion efficiency was quantified by the Spearman correlation coefficients, which could provide insights into the roles of different process parameters in CO2 conversion. This work provides a new approach for boosting CO2 conversion, which could facilitate future experimental design and process optimization to promote carbon dioxide utilization. [ABSTRACT FROM AUTHOR]