The extensive emission of greenhouse gases, primarily CO₂ and CH₄, has contributed to intensified global warming. Dry reforming of methane (DRM, CH₄ + CO₂ → 2CO + 2H₂) offers a pathway for the synergistic utilization of these two major greenhouse gases, presenting important implications for both environmental protection and energy sustainability. However, the catalysts still face the challenges such as carbon deposition and sintering of active metals, which adversely affect the catalytic performance and long-term stability. Oxygen vacancies, which are common lattice defects in metal oxides, have been demonstrated to improve the DRM performance by modulating the surface and interfacial properties of the catalysts. This review systematically summarizes research progresses in DRM over the past decade, outlines the major challenges and emphasizes the critical roles of oxygen vacancies in suppressing carbon deposition and inhibiting metal sintering. Furthermore, the mechanisms through which oxygen vacancies influence DRM reactions are discussed, combined with their formation pathways and regulation strategies. These insights provide essential theoretical foundations for the design and synthesis of highly efficient and stable DRM catalysts.

The catalytic conversion of C1 molecules (e.g., CO, CO₂) represents a pivotal technology in the energy and chemical sectors, essential for achieving sustainable C1 chemistry and low-carbon transformation. A profound understanding of the underlying microscopic reaction mechanisms necessitates systematic support from both theoretical and experimental data support. This study constructs a “Computational Dataset for C1 Molecular Catalytic Conversion Based on Iron-Based Catalysts,” focusing on Fe₅C₂-based catalysts and systematically integrating multidimensional information on C1 molecular adsorption, dissociation, and formation reactions across different crystal surfaces (001, 111, 510). The dataset includes: the number of polycrystalline surface structures (Fe₅C₂(001) with 96 configurations, Fe₅C₂(111) with 93, and Fe₅C₂(510) with 472); species involved in the full reaction pathways under complex co-adsorption environments (adsorbed states such as COH and H₂, dissociated states like CO under 2H conditions, and formation states such as CH/CH₃ under 2H and H₂O conditions); and a standardized hierarchical storage system categorizing reaction types, crystal surfaces, and structural parameters. This dataset not only serves as a benchmark for validating quantum chemical calculation methods but also provides critical data support for catalyst design and reaction pathway optimization in C1 molecular catalytic conversion by uncovering the coupling effects of crystal surfaces and reaction mechanisms.

The coupling of CO₂ with olefins to produce cyclic carbonates has emerged as an important research topic in sustainable chemistry, owing to its high atom economy and the wide applicability of the resulting products. However, this reaction faces a significant challenge due to the mismatch between the rates of the epoxidation and cycloaddition steps. In this work, a series of TS-1 zeolite catalysts encapsulating different amounts of molybdenum acetylacetonate were prepared through hydrothermal synthesis followed by post-treatment, with the aim of elucidating the rate balance between the epoxidation and cycloaddition steps and the underlying regulation mechanism. Characterization results show that the Mo species were present as highly dispersed molybdenum acetylacetonate complexes that were stably confined within the TS-1 framework. These complexes interact electronically with the tetra-coordinated Ti sites to form synergistic active centers, while imposing negligible effects on the zeolite structure and porosity. In the CO₂-styrene coupling reaction, tuning the Mo loading enabled effective control over the epoxidation rate, thereby achieving an appropriate balance with the subsequent cycloaddition step. The optimized catalyst delivered excellent performance under mild conditions, with a styrene conversion of 83.4% and a selectivity of 75.3%, and also exhibited outstanding recyclability. Overall, this encapsulated catalyst successfully addresses the dual challenges of rate matching and active-site stability in CO₂–olefin coupling, providing valuable insights for the rational design of efficient, durable bifunctional catalysts.

Volatile organic compounds (VOCs) originating from diverse sources, exert a profound detrimental impact on the atmospheric environment and pose grave threats to human health. Manganese (Mn)-based catalysts, celebrated for their exceptional structural diversity and abundant redox versatility, are extensively employed in the catalytic combustion of VOCs. This review summarizes the catalytic performance of diverse Mn-based catalysts. It emphasizes strategies for preparing high-performance Mn-based catalysts and provides a systematic analysis of how the construction of active sites influences their catalytic combustion performance for VOCs. Furthermore, the catalytic oxidation mechanisms of VOCs are expounded in detail. This work offers valuable insights and practical guidance for the remediation of VOCs pollution.

Aromatic hydrocarbons, as crucial chemical feedstocks, are widely utilized in the production of fuels, synthetic fibers, and pharmaceuticals, mainly originate from petroleum refining. The catalytic conversion of lignin to produce aromatic hydrocarbons represents one of the important pathways for achieving green production of these chemicals. The core of catalytic pyrolysis of lignin to produce aromatic hydrocarbons lies in the regulation of catalyst acidity and pore structure, optimization of reaction conditions, and the understanding of the conversion mechanisms. Therefore, this review summarizes the influence of pyrolysis methods, catalysts, and reaction conditions on the catalyzed pyrolysis of lignin for aromatic hydrocarbon production, systematically outlines the catalytic reaction mechanism, and provides an outlook on future key research directions and ideas, in order to give theoretical and technological guidance for this process.

The performance of industrial zeolite catalysts, typically exemplified by fluid catalytic cracking (FCC) catalysts, is governed by microscopic behaviors such as mass transfer, acidity and coking. Conventional characterization techniques exhibit limitations in elucidating the structure-performance relationships. So, recent advances in super-resolution fluorescence imaging for deciphering the key microscopic processes are systematically summarized in this review: (1) Mass transfer diffusion behavior: the heterogeneous diffusion of reactant molecules within hierarchical pore networks are revealed; (2) Acid site accessibility: nanoscale localization of acid sites and visualization of their accessibility are achieved; (3) Coking behavior: the spatiotemporal evolution mechanism of coke species on the catalysts are identified. Furthermore, the review elaborates on how super-resolution imaging deepens the understanding of fundamental catalytic mechanisms at the nanoscale, providing theoretical support for the rational design of high-performance catalysts-such as pore structure optimization, acid site regulation, and coking suppression-and concludes with an outlook on current challenges and future directions.

In the biomass utilization process, the high-temperature behavior of ash significantly influences the operational efficiency of gasifiers. This study examined the variations in the high-temperature behavior of straw ash and the underlying mechanisms using an intelligent ash fusion analyzer, a Theta high-temperature rotating viscometer, X-ray diffraction (XRD), SEM-EDS and FactSage thermodynamic simulations. The results revealed that the high-temperature behavior of corn straw ash is mainly controlled by thermodynamic equilibrium, whereas that of wheat straw ash is primarily governed by kinetic factors. Both types of biomass ash contain a high proportion of K₂O (>30%), which leads to relatively low ash flow temperatures (<1300 ℃). Interestingly, corn straw ash exhibited a higher flow temperature than wheat straw ash, despite its a higher K₂O and lower SiO₂ content. This anomalous behavior was attributed to the elevated levels of CaO (10.39%) and MgO (7.33%) in corn straw ash, which promote the formation of high-melting-point silicates such as K₂MgSiO₄, K₂Ca₂Si₂O₇ and CaSiO₃ at elevated temperatures. In contrast, wheat straw ash, with comparatively lower CaO (4.92%) and MgO (2.82%) contents, tended to generate low-melting-point potassium silicates under similar thermal conditions. At high temperatures, both corn and wheat straw ashes exhibited typical crystalline slag melting characteristics, with viscosity increasing linearly as the temperature decreased below a certain threshold. The abrupt viscosity increase in corn straw ash slag was primarily caused by the rapid nucleation and growth of silicate crystals during cooling. Due to the “chemical dilution effect” caused by the high P₂O₅ content, KAlSiO₄ persisted throughout the cooling process in wheat straw ash slag, which resulted in a generally higher viscosity and a sharp viscosity increase at the final stage of cooling. This study elucidated the mechanism by which the ash chemical composition, through determining high-temperature phase equilibrium and non-equilibrium kinetics, macroscopically influences the ash melting and flowing behaviors. This research provided a theoretical foundation for further exploration of the high-temperature properties of biomass ash.

The dense crystalline structure and limited accessibility of cellulose severely hinder its efficient catalytic conversion. In response, biomimetic solid acid catalysts inspired by cellulase binding domains (CBDs) have emerged as a promising strategy to enhance cellulose hydrolysis by mimicking the substrate recognition and enrichment functions of natural enzymes. This review systematically summarizes recent advances in the design of CBD-mimetic solid acids based on four representative strategies: electrostatic anchoring, hydrophobic microenvironment engineering, spatial confinement and covalent lock-and-key mechanisms. The underlying principles of these approaches, including substrate-specific recognition, local concentration enhancement, and synergistic “adsorption-catalysis” effects, are critically discussed to elucidate their contributions in improving catalytic affinity, selectivity, and durability. Despite significant progress, challenges such as mass-transfer resistance and insufficient structural robustness remain in complex biomass conversion systems. Looking forward, the integration of sub-enzymatic materials, such as carbon quantum dots (CQDs), into biomimetic catalysts offers new opportunities to achieve efficient, recyclable and hierarchically organized catalytic systems, thereby providing a powerful route for the sustainable valorization of cellulose and other lignocellulosic resources.

In recent years, the growing urgency for carbon capture has placed chemical absorption at the forefront of post-combustion CO₂ capture technologies, valued for its high efficiency and process maturity. However, traditional amine-based methods face significant challenges during rich solvent regeneration, including high energy demand, solvent degradation, and substantial operating costs, which limit their scalability. To overcome these limitations, catalytic CO₂ desorption has emerged as an innovative approach. By incorporating solid acid catalysts into the CO₂-rich amine solvent regeneration process, this technique lowers the energy barrier for CO₂ release, enhances reaction kinetics, and enables efficient solvent regeneration at low temperatures. As a result, it considerably cuts energy consumption and solvent loss. This paper systematically reviews the research on key catalyst materials in catalytic desorption technology over the past five years, with a focus on the structure-activity relationships of different types of solid catalysts. It highlights the synergistic mechanisms of catalytically active sites (such as Lewis acid, Brønsted acid, and basic sites) in catalytic CO₂ desorption reactions and their influence on desorption pathways. Furthermore, this paper analyzes the key scientific and technological challenges currently faced by this technology and provides an outlook on its industrial application prospects in the field of low-cost carbon capture.

In this study, near-infrared spectroscopy was used along with various machine learning algorithms to develop a fast and reliable framework for identifying coal components, which significantly improved the accuracy and efficiency of predictions. Support vector machine (SVM), random forest (RF), temporal convolutional network (TCN), one-dimensional convolutional neural network (1D CNN), and gated recurrent unit (GRU) algorithms were used for regression modeling of coal properties. Among the various models considered herein, the TCN model was shown to provide the lowest Mean Absolute Error (MAE) value, which was 0.505. In addition, the RF model demonstrated the lowest Root Mean Square Error (RMSE) value of 0.618, indicating its robustness in prediction on the test set. The TCN model, on the other hand, had the lowest Coefficient of Variation (CV) value of 0.042, which demonstrated its good generalization ability and stability. Furthermore, the developed single-output models revealed that different models have different predictive effects on coal components. For example, it was found that the TCN model is optimal for predicting ash content, while the RF model is the most accurate model for predicting fixed carbon and low calorific value. Taking into account the quantity of model parameters, computation time, and accuracy analysis, the results showed that the RF, 1D CNN and TCN models are more efficient. Finally, the influence of key characteristic peaks on prediction results was revealed through SHAP value analysis. The results showed a relationship between absorption intensity and composition of organic and inorganic values in coal to near-infrared light, suggesting important characteristic spectral ranges for ash, moisture, and fixed carbon. This research not only provides a new method for rapid detection in the coal industry, but also serves as a reference for component analysis in other fields.