Hao Jiang

High-entropy oxides (HEOs) have recently been proposed as promising catalysts for tar cracking, a major challenge in biomass gasification. However, their catalytic performance and reaction mechanisms remain insufficiently understood. In this work, fluorite-dominated high entropy composite oxides (HECOs) with different fuel-to-oxidizer oxygen-equivalent ratios (F/O) were synthesized by the glycine-nitrate process (GNP). The as-prepared HECOs (Fe0.2Co0.2Ni0.2Mg0.2Ce0.2)O2–δ were applied as oxygen carriers (OCs) for the chemical looping steam reforming of toluene (CL-SRT), a model compound of biomass tar. Results showed that the synthesized HEOs presented abundant surface porosity and uniform elemental distribution. The higher F/O ratio and temperature improved the CL-SRT performance, while the S/C ratio effectively tuned product composition. Under the conditions of 800 °C and S/C of 2.4, the OC of F/O = 2.0 achieved the optimal reforming performance, with a syngas yield and purity of 4.01 L/g and 86.30%, respectively. The longtime life cycle performance test showed that the toluene conversion stayed at about 97.36% and syngas purity remained near 86%, with only slight carbon deposition observed. The structural characterization suggested that the dynamic exsolution of FeNi alloy may generate additional active sites for toluene conversion, contributing to the enhanced reforming performance. These results highlight fluorite-dominated HECOs as viable oxygen carriers for efficient biomass tar reforming and H2-rich syngas production.

CO2 methanation is a key route for CO2 valorization and renewable hydrogen storage. Machine learning (ML) provides an analytical framework for understanding catalytic systems. However, conventional ML models usually use one-hot encoding, which splits intrinsically related catalytic components into independent sparse binary variables and limits the understanding of complex relationships among catalysts, reaction operating conditions, and CO2 conversion. In this study, an interpretable Categorical Boosting model using native categorical encoding (CatBoost-NCE) was applied to a dataset containing 4026 experimental entries. By preserving native categorical descriptors, the model enabled direct quantitative analysis of active metals, supports, promoters, and preparation methods, while maintaining reliable predictive performance across three independent data splits (mean test R2 = 0.918 ± 0.003). PDP and SHAP analyses showed that operating conditions were the dominant factors, while catalyst composition and preparation descriptors also made substantial contributions. Among categorical catalyst descriptors, the relative influence followed the order support > active metal ≈ preparation method > promoter. Beyond global feature interpretation, active metal-conditioned temperature analysis revealed distinct temperature response patterns among different catalyst systems. Metal-conditioned support substitution analysis further showed that support effects were closely coupled with active metal. For Ni-based catalysts, CeO2 exhibited a positive and directionally stable substitution effect. The model guided X-Ni–CeO2 catalysts showed good agreement with the predicted conversion trend, with an external validation R2 of 0.876. Characterization and in situ DRIFTS measurements further supported the interactions between active metal and support, and elucidated the evolution of reaction intermediates. ReaxFF molecular dynamics simulations performed on bare Ni clusters provided atomistic insights into plausible reaction events and pathways over intrinsic Ni active sites during CO2 methanation. This study converts accumulated catalyst data into actionable design rules and provides a reliable tool for the data-driven discovery of heterogeneous catalysts.