Photoelectrochemical (PEC) water splitting is an effective approach to directly convert solar energy into clean hydrogen fuel. As a visible-light-responsive p-type semiconductor, CuBi₂O₄ possesses a suitable bandgap and good stability. However, its performance is inhibited by high interfacial resistance and severe charge carrier recombination. In this study, a CuO interlayer was introduced between fluorine-doped tin oxide (FTO) and CuBi₂O₄ to construct CuO/CuBi₂O₄ photocathodes, aiming to improve interfacial charge transfer. The results showed that CuO/CuBi₂O₄-200 exhibited a photocurrent density of −1.71 mA/cm² at 0 V vs. RHE, which was more than 3.5 times higher than that of bare CuBi₂O₄. The incident photon-to-current efficiency (IPCE) at 365 nm was enhanced to ~13% and the maximum applied bias photon-to-current efficiency (ABPE) reached 0.17%. Water splitting experiments revealed a hydrogen yield of 2.05 μmol/cm², significantly surpassing that of the unmodified photoelectrode. The enhanced PEC performance indicated that the CuO layer established a favorable band alignment, promoted hole transport toward the FTO substrate and effectively suppressed interfacial carrier recombination. This work demonstrated a simple and efficient interfacial engineering strategy, offering new insights and guidance for the design and development of high-performance semiconductor-based PEC photoelectrodes.

The electrocatalytic reduction of nitric oxide for ammonia synthesis (NORR) is a key green energy conversion technology. Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield ($ {{Y}}_{{\text{NH}}_{3}} $) and Faradaic efficiency ($ {{F}}_{{\text{NH}}_{3}} $). However, conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs. Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms, to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset. Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R² of 0.9223 and an RMSE of 0.0608 for predicting $ {{Y}}_{{\text{NH}}_{3}} $ on the test set, whereas the Stacked-RF model achieved an R² of 0.9042 and an RMSE of 0.0900 for predicting $ {{F}}_{{\text{NH}}_{3}} $. The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting. SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance. Moreover, the combination of the wet chemical reduction synthesis, a carbon fiber (CF) conductive substrate, and HCl electrolyte is more favorable for enhancing catalytic activity. Additionally, moderately lowering the working potential, controlling the electrolyte volume at low to medium levels, reducing catalyst loading, and increasing electrolyte concentration were found to synergistically enhance both $ {{Y}}_{{\text{NH}}_{3}} $ and $ {{F}}_{{\text{NH}}_{3}} $.

Methane dehydroaromatization (MDA) presents a promising carbon-neutral pathway for benzene, toluene, and xylene (BTX) production, alternative to petroleum-derived routes. Elucidating the regulatory mechanisms of Brønsted acid site (BAS) strength on reaction pathways, alongside the spatial proximity effects between BAS and Mo active sites in bifunctional synergy, remains a critical scientific challenge in catalyst design. This study systematically tunes both BAS strength (via isomorphous metal substitution) and Mo-BAS spatial proximity in zeolites, integrating MDA catalytic evaluations with density functional theory (DFT) calculations to dissect their individual contributions. Strongly acidic BAS catalysts (compared to moderately acidic Fe/Ga-substituted counterparts) exhibit superior performance, evidenced by enhanced aromatic yields. Conversely, weakly acidic B-substituted zeolites demonstrate optimal mono-/bifunctional synergy, outperforming moderate-acid systems. DFT results reveal that acid strength dictates C−H activation mechanisms by modulating the energy barriers of rate-determining steps. While Al-zeolites deliver the highest activity, B-substituted systems display unique potential for mechanistic investigations. Spatial proximity analysis indicates that micrometer-scale Mo-BAS distances hinder effective synergy due to exceeding electron interaction and mass transfer limits, whereas nanometer-scale proximity enhances activity (via accelerated intermediate transport) and suppresses coke formation. These findings establish a theoretical framework for rationalizing zeolite catalyst optimization through BAS property engineering and spatial control of Mo-BAS cooperation, providing actionable guidelines for designing next-generation MDA catalysts.

The efficient extraction and separation of valuable metal elements from coal gasification fine slag (CGFS) are crucial for the comprehensive high-value utilization of its constituents. This study focused on the carbon-rich components of CGFS (CGFS-H) and systematically investigates the selective leaching behavior of Fe³⁺, Al³⁺ and Ca²⁺ using three organic acid extractants, i.e., citric acid, tartaric acid, and tetrasodium iminodisuccinate. Additionally, the stepwise leaching of iron, aluminum and calcium from CGFS-H is explored. The selective dissolution mechanisms of these metals by different organic acids are elucidated through X-ray diffraction (XRD), X-ray fluorescence (XRF), and scanning electron microscopy (SEM) analyses. The results indicate that tetrasodium iminodisuccinate exhibits the highest leaching selectivity for Fe³⁺, while tartaric acid demonstrateds a comparable affinity for both Fe³⁺ and Al³⁺. In contrast citric acid shows superior selectivity toward Ca²⁺. The leaching yield of Fe³⁺, Al³⁺ and Ca²⁺ after sequential leaching with the three organic acids were 79.8%, 65.08% and 78.6%, respectively. These findings confirm that effective and selective separation of Fe³⁺, Al³⁺ and Ca²⁺ from CGFS-H can be achieved via optimized organic acid-based leaching strategies. This advancement provides a critical foundation for developing Ca/Fe/Al hydrotalcite materials using CGFS-H as a sustainable feedstock, thereby facilitating the transformation of waste residue into high-value functional materials and promoting resource-efficient utilization of coal gasification fine slag.

CuZnAl (CZA) is a classic industrial catalyst widely used for the synthesis of methanol from syngas, but its catalytic performance is not optimal for the hydrogenation of CO₂ to methanol. Meanwhile, understanding the catalytic mechanism of Cu species in the CZA catalyst remains a great challenge. In this study, we systematically investigated the valence state change of active Cu species in CZA catalyst and their influence on catalytic performance by modifying the catalysts with varying amounts of electron donor K, thus identifying the catalytic function of Cu species with different valence states. H₂-TPR, XPS and HR-TEM characterizations reveal that the highly dispersed K species supported on CZA catalysts will inhibit the reduction of CuO, resulting in a small amount of Cu₂O active species being produced under reaction conditions thus causing a decrease in catalytic activity. Furthermore, XRD and Cu LMM spectra show that the proportion of Cu⁰ in K-modified CZA catalysts increases with K loading, but a higher proportion of Cu⁰ species on the surface obviously promotes the reverse water gas shift (RWGS) reaction. According to the results of in situ infrared spectroscopy, CZA catalyst follows the reaction pathway mediated by HCOO* in the hydrogenation of CO₂ to methanol.

This study systematically investigates the cyclization reaction mechanisms between n-C₄H₃ (1-buten-3-yn-1-yl) and i-C₄H₃ (2-buten-3-yn-1-yl) radicals with acetylene (C₂H₂) using density functional theory (DFT) and transition state theory (TST). The results reveal that the reaction of n-C₄H₃ with acetylene proceeds via a radical chain mechanism through an addition-cyclization pathway, yielding phenyl (six-membered ring), fulvenyl (five-membered ring), and four-membered ring intermediates. The product formation rates follow the order: fulvenyl (five-membered ring) > phenyl (six-membered ring) > four-membered ring. For i-C₄H₃, the intermediate structures depend on the carbon position of i-C₄H₃ where acetylene addition occurs: addition at the C2 position predominantly generates fulvenyl (five-membered ring) as the primary product, whereas addition at the C4 position may lead to phenyl (six-membered ring), fulvenyl (five-membered ring), or four-membered ring intermediates, with the four-membered ring forming most rapidly and the six-membered ring the slowest. Theoretical analyses demonstrate that the selectivity of reaction pathways is primarily governed by structural differences between the isomers. This work provides atomic-scale insights into the cyclization processes between acetylene and C₄H₃ species, establishing a foundation for refining models of soot precursor formation.

Diesel accounts for over 60% of the products derived from direct coal liquefaction (DCL). Compared to petroleum-based diesel, DCL diesel exhibits a cetane number ranging from 30 to 40, which fails to meet the automotive diesel standard requirement of ≥45. Therefore, rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending. Thought traditional methods like gas chromatography offer high accuracy, they are unsuitable for rapid online analysis under industrial conditions. Near-infrared (NIR) spectroscopy can provide advantages in rapid, non-destructive analysis. Its application however, is limited by the complexity of spectral data interpretation. Machine learning (ML) is effective method for extracting valuable information from spectra and establishing high-precision prediction models. This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel. Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality. Subsequently, the predictive performance of three ML models—Lasso, SVR and XGBoost—was compared. Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy, increasing the test set R² from 0.85 to 0.96. After feature extraction, the optimal number of wavelength variables was reduced to 177, substantially improving computational efficiency. Among the models evaluated, the SVR-MI-0.9 model, based on mutual information feature selection, demonstrated the best performance, achieving training and test set R² values both exceeding 0.98. This model enables precise prediction of paraffin, naphthene, and aromatic hydrocarbon contents. This research provides a robust methodology for intelligent online quality monitoring. An intelligent NIR spectroscopy data analysis software was independently developed based on the established model. Compared with comprehensive two-dimensional gas chromatography, the software reduced the analysis time by over 98%, with an absolute prediction error below 0.2%. Thus, rapid analysis of DCL diesel components was successfully realized.

The successful development and application of CO₂ hydrogenation to light olefins can not only alleviate the environmental issues caused by the massive emission of greenhouse gases, but also open up a new way for the preparation of important high-value chemicals. Due to the chemical inertness of CO₂ molecules, selectively activation and conversion of CO₂ into target light olefins remain highly challenging. In this work, we review the current research progress in the hydrogenation of CO₂ to light olefins, with a focus on analyzing the advantages and disadvantages of the Fischer-Tropsch synthesis (CO₂-FTO) route and the methanol intermediate (CO₂-MTO) route. The key factors, such as the type of additives, the structure of the carrier and the concentration of surface defect sites, affecting the activation of CO₂ and the formation of light olefins are discussed in detail. Meanwhile, essential role of the pore structure and acidity of zeolites in conversion of methanol intermediates to olefins is analyzed.

Ammonia borane (AB) is an excellent hydrogen storage material with low molecular weight and high hydrogen content. To fully unleash its value, the development of low-cost and highly active catalysts for AB hydrolysis hydrogen production is precisely the core path to promote the industrialization of AB hydrolysis hydrogen production technology. This paper used anhydrous copper chloride as raw material, and prepared copper oxide nanosheets (CuO NS) precursor by solvothermal method under alkaline conditions. Through low-temperature phosphating method, CuO NS was converted into Cu₃P@CuO nanosheets (Cu₃P@CuO NS). The microstructure and structural composition of Cu₃P@CuO NS were characterized by SEM, TEM, AFM, XRD and XPS. The effects of as-prepared Cu₃P@CuO NS for hydrogen evolution reaction (HER) from AB hydrolysis were investigated. Additionally, the hydrogen evolution rate and turnover frequency (TOF value) of Cu₃P@CuO NS catalyzed AB hydrolysis were explored under different reaction conditions. The results showed that when the phosphating ratio was adjusted to m (CuO NS)/m (NaH₂PO₂)=1 (m (CuO NS)=m (NaH₂PO₂)=0.1 g), Cu₃P@CuO NS exhibited the optimal catalytic activity for AB hydrolysis. Cu₃P@CuO NS exhibited a high turnover frequency (TOF), with a TOF value as high as 57.23 min⁻¹ and an apparent activation energy of 44.31 kJ/mol. The hydrolysis of AB followed pseudo-first-order kinetics with respect to the catalyst dosage, while it exhibited pseudo-zero-order kinetics with respect to the AB dosage. The excellent performance exhibited by Cu₃P@CuO NS could be attributed to the abundant active sites exposed by their nanosheet structure. Given the remarkably low cost of this catalyst, the Cu₃P@CuO NS catalyst is highly anticipated to serve as a viable substitute for noble metal catalysts. It is poised to emerge as an ideal catalytic material for the hydrogen evolution reaction from AB, leveraging its cost-effectiveness and catalytic efficiency to address the challenges associated with high-cost noble metal-based systems.

To achieve the efficient utilization and development of red mud as a resource, this study used red mud as the raw material and starch as the carbon source. By employing sol-gel technology and carbon thermal reduction processes, Fe_xO_y/TiO₂/C composite microwave absorption materials were successfully prepared. By adjusting the calcination temperature and raw material ratios, the minimum reflection loss value (RL_min) and effective absorption bandwidth (EAB) of the Fe_xO_y/TiO₂/C composite material. Experimental results indicate that RmCT-5.4-700 exhibits the best microwave absorption performance, with RL_min reaching −30.2 dB and EAB reaching 5.3 GHz when the coating thickness is 2.0 mm. The excellent absorption performance of the Fe_xO_y/TiO₂/C composite material stems from the synergistic effects of multiple loss mechanisms. Defects generated by carbon consumption during the carbon thermal reduction process provide dipole sites, thereby inducing dipole polarization phenomena, effectively dissipating electromagnetic wave energy and enhancing absorption performance. Graphitized carbon, Fe₃O₄, and Fe particles collectively form a conductive network, with electrons migrating and hopping within the network, thereby generating conductive loss. Additionally, Fe₃O₄, Fe and TiO₂ particles are uniformly distributed on the red mud matrix, forming numerous heterogeneous interfaces, thereby inducing interface polarization. The magnetic loss characteristics of Fe₃O₄ and Fe particles effectively improve the impedance matching properties, enabling more electromagnetic energy to enter the composite material and be efficiently absorbed. The research approach in this work not only opens up new avenues for the resource utilization of red mud but also promotes the high-value utilization of solid waste.