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.

Lignin contains abundant aromatic ring structures, which can be converted into green sustainable aviation fuel-range arenes through hydrodeoxygenation (HDO). A series of supported FeMoS/NC catalysts were synthesized by a hydrothermal method. The HDO performance of the catalysts was evaluated using 4-ethylguaiacol as a model compound at 340 °C under 3 MPa H₂. The MoS₂/NC catalyst exhibited a deoxygenation degree of 83.4%, whereas the Fe-modified catalyst (Fe_0.3MoS/NC) attained complete deoxygenation (100%) with an arenes selectivity of 78.6%. Beyond the optimal ratio, the deoxygenation degree is inversely proportional to the Fe/Mo molar ratio. The catalysts were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), low-temperature nitrogen adsorption (BET method) and X-ray photoelectron spectroscopy (XPS). The characterization results indicated that the introduction of Fe enhanced the uniform dispersion of MoS₂ on the NC support surface. This modification further increased the acidity of the catalyst surface and raised the concentration of sulfur vacancies, thereby promoting the adsorption of oxygen-containing compounds. Furthermore, the HDO performance of the Fe_0.3MoS/NC catalyst was evaluated using actual lignin as a feedstock under the conditions of 340 °C, 3 MPa H₂ and 12 h. The results showed a green hydrocarbon yield of 65.5%, of which the C₈–C₁₆ fraction accounted for 54.4% of the total hydrocarbons. Within this fraction, aromatic compounds constituted 63.4%, suggesting its potential use as green aviation fuel-range arenes. This work thus establishes a viable catalytic pathway for efficient conversion of lignin to arenes.

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.

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.

Chemical looping methane steam reforming (CL-MSR) has garnered significant attention owing to its ability to sequentially produce syngas with high selectivity and high-purity hydrogen through redox cycling. To overcome the limitations of single iron-based oxygen carriers, including poor cycling stability, low reactivity and susceptibility to sintering, this study employed a dip-coating method to modify Fe₂O₃/Al₂O₃ oxygen carriers by incorporating three distinct metal additives: Cu, La and Ce. The composite oxygen carriers were systematically characterized and evaluated under redox conditions to investigate the structure-activity relationships between the physicochemical properties, reactivity, and hydrogen production performance. Results revealed that the spinel-phase CuFe₂O₄ exhibited higher reactivity than the perovskite-phase LaFeO₃ and CeO₂, promoting the deeper reduction of Fe₂O₃. Fe58Cu2Al exhibited an oxygen storage capacity as high as 6.5 mmol/g. During the CH₄ reaction stage, Fe58Cu2Al achieved the highest oxygen loss of 12.1 g/100 g oxygen carrier, accompanied by a syngas yield of 5.15 mmol/g—1.33 times and 1.59 times greater than that of Fe60Al. In the hydrogen production stage, the 2% Cu-modified oxygen carrier demonstrated optimal performance, yielding 5.13 mmol/g of hydrogen, which was 1.51 times that of the pristine sample. Even after ten cycles, the H₂ yield remained at 3.61 mmol/g, surpassing the single-cycle output of the pristine sample and the H₂ purity consistently exceeded 98%.

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.

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}} $.

This study presents the successful synthesis of a novel Z-scheme heterojunction composite film consisting of Ag/Bi₂MoO₆/BiOBr through electrochemical processes and ion-exchange techniques, followed by the photodeposition of noble metal silver (Ag) onto the composite structure. The catalytic efficiency of semiconductor photocatalysts is greatly improved by utilizing the localized surface plasmon resonance (LSPR) effect observed in Ag nanoparticles (NPs). Furthermore, the noble metal Ag serves as an intermediary bridge facilitating charge transfer between Bi₂MoO₆ and BiOBr, while the formation of a Schottky barrier effectively inhibits the recombination of photo-generated electron-hole pairs. As a result, the Ag-deposited Bi₂MoO₆/BiOBr film exhibits superior photocatalytic performance in the reduction of CO₂ compared to its unmodified counterpart. Our experimental results indicate a non-linear relationship between Ag deposition and the efficiency of photocatalytic CO₂ reduction to CO, characterized by an initial increase in efficiency followed by a decline. The optimized 1.5%-Ag/Bi₂MoO₆/BiOBr film demonstrates exceptional photocatalytic activity, attaining a CO production rate of 13.65 μmol/(g·h). This research explores the fundamental mechanisms that lead to improved photocatalytic CO₂ reduction capabilities of the Ag/Bi₂MoO₆/BiOBr film. Our research offers important perspectives for the thoughtful design and production of highly efficient photocatalysts, which are essential for advancing sustainable energy solutions.

Ethylene (C₂H₄) in vehicle exhaust is a highly reactive volatile organic compound (VOC). Its photo-oxidative reaction with NO_x contributes to the formation of O₃ and secondary organic aerosols (SOA), the latter being a key precursor of PM_2.5. In this study, a novel MgO-supported Ag-Cu bimetallic catalyst was designed and investigated using density functional theory (DFT). The effects of Ag and Cu loading on the geometric structure, stability, and reactant adsorption characteristics of the catalyst were analyzed, and the catalytic oxidation pathways of C₂H₄ over AgCu-MgO was elucidated. The results indicate that loading Ag significantly enhances the adsorption of C₂H₄. The incorporation of Cu into Ag-MgO to form a AgCu-MgO bimetallic catalyst (dual atom catalyst, DACS) further improves the oxidative activity toward C₂H₄. Based on the binding energies of the Ag and Cu bimetallic sites and the adsorption energies of C₂H₄ and O₂, three representative configurations were selected for detailed reaction pathway analysis. Among them, Configuration 6 of AgCu-MgO exhibited the highest catalytic oxidation performance. This study provides new atomic-scale insights for the rational design of efficient catalysts targeting olefinic pollutants in automotive emissions and offers valuable guidance for advancing exhaust after-treatment technologies.

Photosynthesis of H₂O₂ via sustainable biomass-derived carbon catalysts facilitate the conversion of renewable resources into valuable chemicals. However, the regulatory function of surface functional groups over reaction kinetics has not been sufficiently investigated. Herein, hydrothermal carbon spheres (CS) rich in oxygen-containing functional groups demonstrated a remarkably high H₂O₂ production rate (653 μmol/(g·h)) in both pure water and actual seawater, even in the absence of any sacrificial agent. Meanwhile, the catalyst demonstrates outstanding activity (92% conversion and >99% selectivity) in the visible-light-driven photocatalytic oxidation of benzylamine to imines. Comprehensive analysis reveals that CS was rich in surface oxygen-containing functional groups, a feature strongly associated with its high photocatalytic efficiency. The observed positive Zeta potential of CS in seawater likely diminished the electrostatic repulsion against the positively charged $\cdot {\mathrm{O}}_2^- $ intermediates, thereby facilitating their accumulation at the liquid-solid interface. This work proposes a strategic framework for developing metal-free photocatalysts from biomass, offering a sustainable pathway for photocatalytic applications.

Under the context of global energy transition and carbon neutrality, controlling nitrogen oxide (NO_x) emissions from biomass combustion is of great significance, and the development of high-efficiency low-temperature catalysts has become a current research focus. In this study, Nb was used to dope and modify the Mn₇-Cu₃/BCN catalyst to construct the Mn₇-Cu₃-Nb_x/BCN system. The doping amount was optimized through selective catalytic reduction (SCR) activity tests. The reaction mechanism was explored by combining in situ DRIFTS and density functional theory (DFT) simulations. Experimental findings revealed that the catalyst doped with 0.05% Nb achieved the optimal performance, sustaining a NO conversion efficiency of ≥94% within the temperature window of 150−275 °C while demonstrating improved resistance to alkali metal K poisoning. Mechanistic analyses showed that at low temperatures, the catalyst facilitated the SCR reaction via both the Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) pathways, with the synergistic interaction between multiple active sites driving the efficient conversion of NH₃ and NO. DFT calculations further confirmed that both pathways had the characteristics of low reaction energy barriers and significant exothermicity, ensuring the high activity and feasibility of the low-temperature reaction. The findings provided foundational theoretical support for the design of Nb-doped Mn-Cu-supported catalysts and the exploration of the underlying working mechanisms.

Temperature-programmed desorption (TPD) is a fundamental technique in surface science and heterogeneous catalysis for characterizing adsorption behavior, and for extracting key parameters such as adsorption energy. However, the majority of existing TPD data is accessible in the form of published images, which lacks structured and quantitative datasets. This constrains its utility for rigorous quantitative analysis and computational modelling. Using carbon monoxide (CO) which is a widely adopted probe molecule, a curated and standardized dataset of CO-TPD is constructed, encompassing 14 transition-metal single-crystal surfaces, including copper (Cu) and ruthenium (Ru). By systematically extracting numerical data points from published spectra and applying normalization, essential spectral features such as peak shape are fully preserved. The dataset also documents relevant experimental parameters, including heating rates, and was developed using a standardized protocol for data collection and quality control. This resource serves as both a reference library to support the deconvolution of TPD spectra from complex catalysts and an experimental benchmark for calibrating parameters in theoretical models. By providing a reliable and accessible data function, this work advances the microscopic understanding and the rational design of catalyst active centers.