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Thermocatalytic CO 2 hydrogenation to methanol is an attractive defossilization technology to combat climate change while producing a valuable platform chemical and energy carrier. However, predicting the performance of catalytic systems for this process remains a challenge. Herein, we present a machine learning framework to predict catalyst performance from experimental descriptors. A database of Cu-, Pd-, In 2 O 3 -, and ZnO-ZrO 2 -based catalysts with 1425 datapoints is compiled from literature and subjected to data mining. Accurate ensemble-tree models (R 2 > 0.85) are developed to predict the methanol space-time yield (STY) from 12 descriptors, where the significance of space velocity, pressure, and metal content is revealed. The model prediction and its insights are experimentally validated, with a root mean squared error of 0.11 g MeOH h−1 g cat −1 between the actual and predicted methanol STY. The framework is purely data-driven, interpretable, cross-deployable to other catalytic processes, and serves as an invaluable tool for guided experiments and optimization. [Display omitted] • Machine learning framework for CO 2 hydrogenation to methanol devised. • Database for Cu-, Pd-, In 2 O 3 - and ZnO-ZrO 2 -based catalysts compiled. • Generalized model to predict methanol space-time yield (STY) developed. • Efficacy and fidelity of the model experimentally validated. • Model enhancements to aid the discovery of novel catalysts required. [ABSTRACT FROM AUTHOR]

5-Methylfurfural (MF) is a critical commodity chemical used as food additive and synthetic intermediate. Opening a sustainable route towards highly selective synthesis of MF from 5-hydroxymethylfurfural (HMF) is of great significance, but has demonstrated extremely challenging. Herein, we report an efficient process for the selective production of MF from HMF over an anatase-supported gold catalyst (Au/a-TiO 2). During the hydrogenolysis of HMF, the desired C O bond is maintained and MF can be obtained in a high yield of 83.1 %, exhibiting excellent stability both in batch and flow operation. A combination of experimental and theoretical analyses attribute the superior performance of Au/a-TiO 2 to the synergistic effect of limited H access, low adsorption of reactants, and avoiding condensation, driven by the high concentration of oxygen vacancies in the support. This study highlights a novel approach of tailoring the coverage of active species for the selective transformation of renewable compounds into high-value-added chemicals. [Display omitted] • Selective production of MF from HMF via direct hydrogenolysis of the hydroxyl group using the Au/a-TiO 2 catalyst. • The C=O functional group was preserved under reductive conditions and MF can be obtained in a high yield of 83.1%. • The Au/a-TiO 2 catalyst shows high activity, remarkable selectivity, and excellent stability both in batch and flow operation. • The synergistic effect of limited H access, low adsorption of reactants, high concentration of oxygen vacancies. • This study paves the way for the design of new bifunctional catalysts toward selective hydrogenolysis of biomass. [ABSTRACT FROM AUTHOR]

The conversion of wet waste (e.g., food waste, sewage sludge, and animal manure) into bioenergy is a promising strategy for sustainable energy generation and waste management. Although experimental efforts have driven waste conversion technologies (WCTs) to various degrees of maturity, computational modeling has equally contributed to this endeavor. This review focuses on the application of modeling techniques, including computational fluid dynamics (CFD), process simulation (PS), and machine learning (ML) on WCTs including anaerobic digestion, hydrothermal carbonization, gasification, pyrolysis and incineration. It addresses in a concise manner on how CFD models aid in understanding of the complex process and their molecular kinetics; while PS and ML models help in understanding the reaction kinetics, variable-response relationship, techno-economic assessment and sensitivity analysis. Relevant modeling approaches with their pros and cons are summarized and case studies are presented for each WCT. Moreover, a comparative evaluation among the three modeling techniques, along with their recent and ongoing developments are highlighted. Hybrid frameworks derived by combining mechanistic and ML models are proposed, which are expected to advance future wet waste valorization strategies for sustainable clean energy production and waste management. [ABSTRACT FROM AUTHOR]

The development of efficient CO 2 conversion catalysts is a long-lasting desire. Herein, we introduce an atomic Pd-promoted ZnZrO x solid solution catalyst (Pd-ZnZrO x), which shows markedly enhanced rate of methanol production compared to bare ZnZrO x , as well as excellent stability over 100 h on stream. Up to 0.8 at% (i. e. 0.6 wt%), Pd can be atomically dispersed in ZnZrO x , leading to more oxygen vacancies on the mixed oxide that foster methanol production. Kinetic analysis and in situ DRIFTS reveal that hydrogen activation is limited on ZnZrO x , but Pd doping facilitates H 2 dissociation as well as the consequent formation of HCOO*, thus boosting CO 2 conversion to methanol. DFT analyses suggest that the presence of atomic Pd enables a more exothermic H 2 dissociation, which increases the availability of surface H and facilitates CO 2 hydrogenation on adjacent Zn sites, providing rationale on the high activity and robustness of Pd-ZnZrO x in CO 2 hydrogenation. [Display omitted] • Coprecipitation of Zn, Zr, and Pd precursors forms atomically dispersed Pd on ZnZrO x. • Atomic Pd-doping leads to higher concentration of surface oxygen vacancies. • Atomic Pd-promoted ZnZrO x shows excellent CO 2 hydrogenation activity and lifetime. • Pd promotes H 2 splitting and the stabilization of key intermediates for CH 3 OH. [ABSTRACT FROM AUTHOR]