Alkali lignin, a high-volume byproduct generated from pulp and paper manufacturing as well as biomass refining, represents a promising natural feedstock for aromatic hydrocarbon production in liquid fuels owing to its high energy density and abundant aromatic moieties. However, its highly cross-linked polymeric structure hinders its efficient valorization and high-value conversion. In this work, alkali lignin was used as the feedstock to investigate the catalytic conversion into bio-oil under in situ H₂ supply from formic acid. A series of Ni-Mo/h-BN bimetallic catalysts with varied metal ratios were synthesized by the impregnation method, and their physicochemical properties were systematically characterized by XPS, XRD and other techniques. The effects of reaction parameters on H₂ production via aqueous-phase reforming of formic acid were first evaluated. The optimal H₂ yield was achieved at a formic acid-to-water molar ratio of 1∶10 and a Ni/Mo atomic ratio of 3∶1. The H₂ yield increased monotonically with temperature in the range of 220–280 °C, reaching a maximum of 38.48 mmol. Subsequently, the influences of reaction temperature and residence time on bio-oil production from alkali lignin were examined. The highest heavy bio-oil yield (18.93%) and the maximum relative content of aromatic hydrocarbons (13.81%) were both achieved at 280 °C. Prolonged reaction time reduced the heavy bio-oil yield and the relative abundance of aromatic hydrocarbons, while favoring the formation of furan derivatives. This work demonstrates good synergy and consistency between in situ hydrogen generation from formic acid and lignin hydrogenation in the temperature range of 240–280 °C.

Chemical looping, with its advantages of efficient product separation and low energy consumption, has emerged as a crucial technical pathway for the CO₂ conversion and hydrogen production via water splitting. Oxygen carriers, functioning within the chemical looping cycle, undertaking oxygen storage and transfer and participating in the redox reactions, serve as a key component that determines the reaction rates and product selectivity. To screen high-performance oxygen carriers, this study employed the Gibbs free energy analysis method to thermodynamically investigate the chemical looping reactions involving typical metals and corresponding oxides for the CO₂ conversion and hydrogen production via water splitting. Based on the Ellingham diagrams, the feasibility of the conversion reactions and the performance of various metal-based oxygen carriers were evaluated. The Gibbs free energies for the oxidation of metals (such as iron, vanadium, manganese, cobalt, copper and cerium) and that for the reactions between these metals and water were obtained. The feasibility and critical temperatures of the reactions of various metals and their oxides with CO₂ and water across different temperature ranges were clarified, which revealed the superior capability of the Fe/Mn-based oxygen carriers for CO₂ reduction at high temperatures and the great hydrogen production potential of Mn/Cu/Ce-based oxygen carriers at low temperatures. The results indicate that high temperatures can promote the reduction of CO₂ by iron, manganese, and their reduced oxides, with initial reaction temperatures of 708 °C for Fe and 935°C for Mn; in addition, the promoting effect becomes more pronounced at higher temperatures. Thermodynamically, manganese, copper, and cerium can spontaneously decompose water to produce hydrogen at relatively low temperatures. This study delineates the relative redox performance of various metals with CO₂ and H₂O at different temperatures, providing a thermodynamic theoretical basis for the screening and optimization of oxygen carriers in the chemical looping reactions.

Acid etching enables the enrichment of abundant hydroxyl nests on the surface of titanium silicalite (TS). These hydroxyl nests offer stable anchoring sites for the immobilization of active components, facilitating synergistic interaction with PtZn bimetallic species and enhancing propane dehydrogenation (PDH) catalytic performance. Based on this concept, in the present work, TS supports were etched using hydrochloric acid with varying concentrations, and a series of PtZn/TS bimetallic catalysts were fabricated via the co-impregnation method. The effects of acid concentration on the catalyst structure, surface properties, and PDH catalytic performance were systematically investigated. Comprehensive characterization results reveal that acid etching induces the formation of surface-enriched hydroxyl nest structures on TS supports. These structures act as efficient anchoring centers for PtZn species, leading to enhanced dispersion and increased exposure of metallic Pt⁰ species, thereby lowering the energy barrier for the PDH reaction. This study thus provides a feasible and rational strategy for the design of high-performance PDH catalysts.

The HZSM-5 zeolite was co-modified with levulinic acid (LA) and Zn to obtain L/Zn0.2-ZSM5. Comprehensive characterization was conducted using XRD, N₂ adsorption-desorption, NH₃-TPD, Py-FTIR, Raman, XPS and TGA to investigate the effects of co-modification on the catalyst’s structure, physicochemical properties, catalytic performance, and coke-deposition behavior. The results indicate that L/Zn0.2-ZSM5 possesses a specific surface area comparable to that of HZSM-5, while exhibiting significantly larger pore volume and average pore size than HZSM-5 and the singly modified catalysts (L-ZSM5 and Zn0.2-ZSM5). In addition, co-modification markedly decreases the total acid amount and acid strength, while establishing a moderate Brønsted-to-Lewis (B/L) acid ratio. These structural and acidic modifications endow L/Zn0.2-ZSM5 with a BTX (benzene, toluene, xylene) selectivity of 58.75% and a catalytic lifetime of 128 h, both of which surpass those of the singly modified catalysts and those with different Zn loadings. The enhanced catalytic performance of L/Zn0.2-ZSM5 correlates with its significantly lower proportion of internal coke and greater coke storage capacity compared with the unmodified and singly modified catalysts. This behavior is attributed to the co-modification-induced pore enlargement, which effectively reduces the diffusion resistance of coke precursors and suppresses the graphitization process of coke. Furthermore, LA strengthens the interaction between Zn species and the zeolite framework, promotes Zn dispersion, and substantially increases the proportion of [ZnOH]⁺ species to 61.36%.

This study reports a streamlined route for synthesizing plate-like α-Al₂O₃ via co-combustion activation of coal gangue and corn stalk, enabling the high-value utilization of solid wastes. The results demonstrate that the introduction of corn stalk significantly reduces the apparent activation energy of coal gangue combustion and increases the acid leaching yield of aluminum to 81.9%. Mechanism analysis reveals that titanium and iron ions in the co-combustion ash leachate act as natural morphology regulators, facilitating the formation of a plate-like structure in the alumina product, with titanium exhibiting leaching behavior consistent with that of aluminum. Furthermore, it is found that a high content of AlO₆ structural units in the precursor effectively promotes the direct conversion into dense α-Al₂O₃ crystals during thermal treatment, thereby enhancing the product density. Under optimized conditions (800 °C, 1 h), the as-prepared α-Al₂O₃ exhibits a plate-like morphology, with a median particle size (d₅₀) of 5.70 μm and a density of 4.94 g/cm³. This work provides a new approach for the synergistic resource utilization of coal gangue and biomass waste.

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.

Catalytic oxidation of CO, as a key reaction for reducing vehicle emissions, has been extensively studied. Introducing single atoms into ceria (CeO₂) is expected to combine the excellent oxygen storage/release capacity, reversible Ce⁴⁺/Ce³⁺ redox properties, and abundant surface defects of CeO₂, thereby further enhancing its catalytic performance in the low-temperature CO oxidation. However, existing first-principles calculation data remain scattered and systematic data for the single-atom doped CeO₂ catalysts (M-CeO₂) in the CO oxidation are lacking, which severely restricts the in-depth understanding of structure-performance relationships and reaction mechanisms concerning M-CeO₂. To address this gap, this study establishes a database covering a series of M-CeO₂ catalysts. Through DFT calculations, a multi-dimensional dataset was generated, which encompassed the model structures, elementary reaction energies, frequency analyses, density of states, bonding strengths and Bader charges. A rigorous quality control system was implemented for both computational parameters and output results to ensure the data integrity and reliability. This database, providing complete raw calculation files, offers a reliable foundation for exploring the catalytic performance, structure-performance relationships, and reaction mechanisms of M-CeO₂ catalysts in the low-temperature CO oxidation.