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Published Research PapersFiltered: Year 2026 • 54 • 7

Showing 22 of 106 peer-reviewed papers with full Graphical Abstracts.

Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60651-2• Jan 15, 2026

Catalytic Conversion of Carbohydrates: Opportunities and Challenges en Route to Fuels and Chemicals

Authors: LUO Yang, LING Wenmeng, WANG Chenguang

Carbohydrates, derived from abundant biomass resources, hold great promise for conversion into fine platform chemicals and fuels, which is crucial for sustainable development. The processes for carbohydrate conversion are predominantly driven by catalysis, with active components such as Brønsted acids and Lewis acids. This review provides a comprehensive overview of the catalytic conversion of various carbohydrates (monosaccharides, disaccharides, and polysaccharides) into high-value-added compounds. It elaborates on the specific pathways and mechanisms involved in reactions like hydrolysis, isomerization, and dehydration for target molecules such as 5-hydroxymethylfurfural, lactic acid, and furfural. Furthermore, the subsequent derivatization of these platform compounds and their application prospects in energy-related fields, including bio-fuels and batteries, are discussed. Finally, the current challenges in research are summarized, and future directions for the development of low-cost and high-performance catalytic systems are outlined.

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Catalytic Conversion of Carbohydrates: Opportunities and Challenges en Route to Fuels and Chemicals

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60679-2• Jan 15, 2026

Research Progress on Supports for Rh-Based Catalysts in Heterogeneous Hydroformylation of Olefins

Authors: WANG Wei, FENG Rui, LI Tianbo, HU Xiaoyan, YAN Xinlong, LU Shijian

Olefin hydroformylation is a pivotal process for synthesizing high-value-added aldehydes, with applications extending from short-chain to long-chain olefins (C6+). Traditional homogeneous catalytic systems suffer from difficulties in separating and recovering precious rhodium (Rh), driving research toward heterogeneous catalytic systems. This review summarizes recent progress in supports for heterogeneous Rh-based catalysts, focusing on the influence of structural regulation strategies of inorganic oxide-supported, porous carbon-supported, organic porous polymer-based, zeolite-supported, and composite-supported catalysts on active site dispersion, regioselectivity, and cycle stability. Key findings include enhanced linear-to-branched (n/i) ratios and turnover frequencies (TOF) achieved through tailored support design. For instance, Rh1/CeO2 with morphology effects demonstrates molecular-level understanding of support effects, while Rh/activated carbon with surface oxygen groups improves catalytic performance in 1-hexene hydroformylation. Porous monophosphine polymers confine atomically dispersed Rh, achieving regioselective hydroformylation. Additionally, Rh-N4 single atoms and Rh clusters dual-active sites on supports yield ultra-high TOF. The review aims to provide insights for rational design of high-performance heterogeneous hydroformylation catalysts, addressing industrial challenges of catalyst recovery and stability.

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Research Progress on Supports for Rh-Based Catalysts in Heterogeneous Hydroformylation of Olefins

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60655-X• Jan 15, 2026

Machine Learning-Assisted Discovery of Lewis Base Additives for Defect Passivation in Perovskite Solar Cells

Authors: MENG Jiangtao, DING Bin, LUO Shulin, ZHANG Qi, LI Yuanliang, WU Shuangjia, ZHAO Zhiyu, WANG Gangcheng, LIU Jun, WU Guixuan, DING Yong, ZHANG Wenyuan

Defect-induced nonradiative recombination critically restricts the power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs). Lewis base additives show great promise in defect passivation, but current screening methods rely heavily on empirical trial and error and lack clear design principles, making it difficult to efficiently discover high-performance candidate materials. Here, we present a machine learning (ML) framework to intelligently screen Lewis base molecules for defect passivation. We trained six ensemble models on a dataset of 146 experimental data points, with Light Gradient Boosting Machine (LightGBM) yielding the best classification performance (87% accuracy). Shapley Additive Explanations (SHAP) interpretability analysis subsequently identifies the highest occupied molecular orbital (HOMO) energy (−7.5 to −6.3 eV), additive concentration (2.5 to 6.5 mg/mL), and simplified molecular backbones (O atom ≤ 2, C atom < 5) as critical design criteria. The ML prediction was experimentally validated: (S)-pyrrolidine-3-carboxylic acid ((S)-PCA) and 2-methyl-1,3-cyclopentanedione (MCPD) (Class Ⅱ) improved PCE by 2.22% and 2.01%, respectively, while 3-hydroxymethyl-3-methylbutanenitrile (3-HMBN) (Class Ⅰ) showed minimal gain. Density functional theory (DFT) calculations further confirmed the stronger binding affinities and elevated defect formation energies of Class Ⅱ additives. Notably, the champion (S)-PCA device achieved a PCE of 24.05%. This work established an ML-accelerated paradigm for the rational design of defect passivators, bridging data science and photovoltaics.

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Machine Learning-Assisted Discovery of Lewis Base Additives for Defect Passivation in Perovskite Solar Cells

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60680-9• Jan 15, 2026

High-throughput screening of SrxA1−xFeyB1−yO3 perovskites for low-temperature chemical looping air separation using graph neural networks

Authors: ZHAO Jie, DONG Changqing, XUE Junjie, HU Xiaoying, ZHANG Junjiao

Low-temperature chemical looping air separation (CLAS) is a promising technology for producing oxygen-enriched gas streams, utilizing the redox properties of solid oxygen carriers to selectively capture and release oxygen from air. Oxygen vacancy formation energy (Eovf) is a key descriptor for evaluating the ease of oxygen release. In this study, the applicable range of Eovf for CLAS oxygen carriers was determined to be <2.3 eV via thermodynamic calculations. A graph neural network (GNN) model, specifically the ALIGNN architecture, was trained to predict Eovf with a mean absolute error (MAE) of 0.26 eV on the test set. Using this model, a high-throughput screening of 3,649 compositions of SrxA1−xFeyB1−yO3 perovskites was conducted to identify promising CLAS oxygen carriers. The predictions revealed that doping with Ba and Ca at the A-site and Co at the B-site effectively reduces Eovf. The screening criterion of Eovf < 2.3 eV successfully rediscovered several previously reported low-temperature CLAS oxygen carriers, validating the approach. This work demonstrates that GNN-based Eovf prediction can significantly accelerate the discovery of CLAS materials, with broader implications for other chemical looping applications such as full oxidation and syngas production.

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High-throughput screening of SrxA1−xFeyB1−yO3 perovskites for low-temperature chemical looping air separation using graph neural networks

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60676-7• Jan 15, 2026

Investigating the migration mechanisms of heavy metals under silicate polymerization in gasification slag from landfilled municipal solid waste with rice husk addition

Authors: HUANG Qinwei, TANG Longfei, LIU Xia, PAN Weitong, CHEN Xueli, WANG Fuchen

Landfilled municipal solid waste (MSW) in China exceeds 8 billion tons, with high moisture (30–50%) and ash content (>50%), complicating conventional treatment. Slag gasification offers a clean and resource-oriented route, but heavy metal leaching from the resulting slag poses environmental risks. This study investigates the effect of rice husk addition (5–15%) on the vitrification of landfilled-waste slag and the immobilization of heavy metals (Cr, Zn, Cu). Results show that adding 5–10% rice husk lowers the slag flow temperature to a minimum of 1213 °C, attributed to active SiO2 reacting with CaO and Fe2O3 to form low-melting eutectics like anorthite. Leaching concentrations of Cr and Zn decrease from 41.60 and 108.00 mg/L to 5.89 and 7.10 mg/L, respectively, with 10–15% rice husk. The amorphous SiO2 enhances silicate polymerization (Q3, Q4 networks), promoting physical encapsulation and chemical incorporation of heavy metals into stable phases such as Zn2SiO4 and CuFe2O4, increasing the residual fraction and reducing bioavailability. At temperatures >1400 °C, volatilization of Cu and Zn increases, with residual rates dropping to 33–60% and 31–55%, respectively, while Cr remains stable (70–123%). This work elucidates the mechanistic role of rice husk in slag structure modulation and heavy metal immobilization, providing a theoretical basis for the co-treatment of landfilled waste and biomass via a 'treating waste with waste' strategy.

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Investigating the migration mechanisms of heavy metals under silicate polymerization in gasification slag from landfilled municipal solid waste with rice husk addition

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60661-5• Jan 15, 2026

Green Synthesis of Hierarchical NaY Zeolite from Perlite for Enhanced Knoevenagel Condensation

Authors: DONG Peng, ZHU Lin, LI Tiesen, CUI Qingyan, YUE Yuanyuan

Hierarchical aluminum-rich zeolites are promising catalysts for Knoevenagel condensation, but their synthesis is often costly and energy-intensive. This work reports a green route to hierarchical NaY zeolite using submolten salt (SMS) activated perlite as the sole silicon and aluminum source. The product exhibits high purity and crystallinity, with a framework SiO2/Al2O3 molar ratio of approximately 4.2, intercrystalline mesopores centered at about 20 nm, large external surface area, and abundant basic sites. Crystallization studies reveal that small crystals initially assemble on the activated perlite surface, then grow and aggregate to form a crystal-packed morphology with intercrystalline mesopores. In the Knoevenagel condensation of benzaldehyde with ethyl cyanoacetate, the hierarchical NaY zeolite achieves higher benzaldehyde conversion than conventional NaY zeolites, attributed to improved mass transfer and increased basic site accessibility. This work provides a cost-effective and sustainable catalyst while valorizing natural perlite.

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Green Synthesis of Hierarchical NaY Zeolite from Perlite for Enhanced Knoevenagel Condensation

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60656-1• Jan 15, 2026

Construction of Calcium-Manganese Composite Desulfurizer for Synergistic Removal of SO2/Hg0

Authors: LIU Shuaipeng, LI Dalai, REN Zihan, LIU Jie, WANG Lidong

Under the carbon neutrality strategy, biomass boilers have emerged as key facilities for renewable energy utilization, yet are characterized by low-concentration SO2 emissions. Ca-based dry desulfurization presents a promising technology for biomass boiler flue gas purification due to its compact structure, low capital investment and simple operation and maintenance. However, it is generally limited by the low adsorbent utilization and insufficient desulfurization efficiency. Herein, this study developed a novel Ca-Mn composite adsorbent through a synergistic strategy integrating F127 surfactant to optimize dispersion and Mn loading to enhance oxidation efficiency. The resulting adsorbent not only significantly increased the breakthrough sulfur capacity of the Ca-based material but also markedly improved the synergistic removal of Hg0. It was demonstrated that the introduction of Mn elements and F127 effectively suppressed the agglomeration of Ca(OH)2 crystallites and induced an oxygen vacancy-rich structure, while simultaneously optimizing the pore structure of the adsorbent. The modified adsorbent exhibited the enlarged specific surface area and pore volume, which favored to enhance the reaction mass transfer and effectively prevent the pore blockage and coverage of active sites by desulfurization products. The Mn sites and oxygen vacancies formed catalytic centers, which not only accelerated the desulfurization reaction by promoting SO2 oxidation but also enabled the adsorbent to couple with Hg0 catalytic oxidation functionality. Consequently, the simultaneous removal of SO2 and Hg0 was significantly enhanced on the Ca-Mn composite adsorbent.

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Construction of Calcium-Manganese Composite Desulfurizer for Synergistic Removal of SO2/Hg0

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60665-2• Jan 15, 2026

Machine learning-based prediction and optimization of the cellulose conversion process for levulinic acid production

Authors: ZHAO Huiting, XIE Yujiao, XU Dongqian, DONG Fangxu, CUI Hongyou

Levulinic acid (LA) is a promising platform product with wide industrial applications. Efficient conversion of cellulose into LA has become a research hotspot, yet traditional experimental optimization is time-consuming and inefficient. This study integrates multidimensional data—reaction conditions, solvent properties, and physicochemical characteristics of metal salts—to construct a systematic dataset. Six machine learning models (decision tree, gradient boosting regression, K-nearest neighbors, multilayer perceptron, random forest, and support vector machine) were developed to predict LA yield. The gradient boosting regression (GBR) model achieved the best performance, with a test-set determination coefficient (R²) of 0.94 and the lowest root-mean-square error (RMSE). SHapley Additive exPlanations (SHAP) and partial dependence analysis identified water fraction, catalyst dosage, and reaction temperature as the key factors influencing LA formation. By integrating the GBR model with particle swarm optimization (PSO), RuCl₃ was identified as an efficient catalyst under high-temperature and short-reaction-time conditions. This study demonstrates the potential of machine learning in cellulose conversion research, providing a data-driven strategy and theoretical guidance for efficient and green LA production.

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Machine learning-based prediction and optimization of the cellulose conversion process for levulinic acid production

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60657-3• Jan 15, 2026

Manganese Promoter Hinders Carbon Permeation on Iron-Based Catalyst Surfaces: A First-Principles Study

Authors: YANG Tao, MA Huan, LIU Xingchen

Fe-Mn catalysts have attracted considerable attention for industrial Fischer-Tropsch synthesis (FTS) due to their ability to modulate product spectra. Carbon adsorption and permeation on catalyst surfaces are critical elementary steps in the in situ formation of active iron carbide phases. Here, density functional theory (DFT) calculations systematically investigate the atomistic structures, thermodynamic stabilities, and electronic properties of carbon-deposited Fe-Mn alloy surfaces at the early stage of carburization. These surfaces exhibit distinct thermodynamic sensitivity to carbon atoms adsorbed on the surface and permeating into interstitial sites. By combining DFT with minima-hopping structural searches, we demonstrate that the initial stage of carbon permeation cannot trigger surface reconstruction to form iron carbide phases. The addition of manganese thermodynamically hinders carbon permeation. Although deposited carbon atoms modulate the electronic structure of metals, manganese retards the shift of d-band centers toward those of bulk iron carbide phases. This study provides atomic-scale insight into the in situ evolution of Fe-Mn catalyst surfaces during carbon deposition, indicating that manganese promoter has a noticeable effect on carbon permeation.

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Manganese Promoter Hinders Carbon Permeation on Iron-Based Catalyst Surfaces: A First-Principles Study

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60677-9• Jan 15, 2026

Tuning surface oxygen species via Mg-Ba co-doping on La2O3 to enhance oxidative coupling of methane performance

Authors: WANG Ke, ZHANG Qi, NIU Pengyu, LIN Minggui, JIA Litao, LI Debao, ZHANG Riguang

Mg-, Ca-, Sr-, and Ba-single-doped La2O3 as well as Mg-Ba co-doped La2O3 catalysts were synthesized via a hydrothermal method and evaluated for the oxidative coupling of methane (OCM). The experimental results revealed that the Mg-modified La2O3 catalyst activates O2 and CH4 effectively, yet achieves only moderate C2+ selectivity. Conversely, the Ba-modified analogue affords high C2+ selectivity, albeit at the expense of lower reaction activity. Notably, the Mg-Ba co-doped La2O3 catalyst strikes an effective balance between activity and selectivity, enhancing catalytic performance while maintaining a high C2+ selectivity. Specifically, at a Mg/Ba molar ratio of 1:1 and 700 °C, it achieved a CH4 conversion of 29.5%, a C2+ selectivity of 54.5% and a corresponding C2+ yield of 16.1%. The characterization results indicate that Mg and Ba co-doped La2O3 catalysts promote the formation of more superoxide (O2−) species on the catalyst surface, which in turn significantly enhances both the activity and selectivity of La2O3 catalysts. In situ DRIFTS revealed the presence of superoxide species on the surface of both Mg- and Ba-doped catalysts, with the co-doped system exhibiting a significantly more intense signal for the superoxide species. O2/H2-TPR studies revealed that Mg and Ba co-doped La2O3 catalysts exhibit superior O2 activation capabilities compared to those doped with Mg or Ba alone. CH4/O2 pulse experiments revealed that the co-doped catalysts facilitate faster establishment of oxygen adsorption equilibrium, thereby enhancing CH4 activation and the subsequent formation of C2 products. This work establishes that co-doping La2O3 with Mg and Ba represents an effective strategy for improving catalytic performance in OCM, primarily by modulating the generation and stabilization of key active oxygen species.

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Tuning surface oxygen species via Mg-Ba co-doping on La2O3 to enhance oxidative coupling of methane performance

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60752-9• Jan 15, 2026

Mechanistic Insights into Methanol Steam Reforming on PdCu(111) and PtCu(111) Bimetallic Catalysts

Authors: WANG Ruiying, GAO Fene, GUO Junlan, ZHANG Xi, LU Sitian, LIU Yanan, JIA Jianfeng

Methanol steam reforming (MSR) is a pivotal process for efficient hydrogen production. This study employs density functional theory (DFT) calculations to comparatively analyze the MSR reaction mechanism on PdCu(111) and PtCu(111) bimetallic surfaces. The investigation unveils how alloying modulates reaction pathways and overall catalytic performance. Notably, Cu sites stabilize adsorption of OH and CH2O species, whereas Pd/Pt sites exhibit preferential affinity for CO. This spatial site separation facilitates progression along the formate pathway. PdCu(111) demonstrates superior overall catalytic performance compared to PtCu(111), with water dissociation identified as the rate-determining step (RDS), featuring an activation energy of only 0.74 eV. The bimetallic synergy breaks the inherent contradiction between activity and selectivity of monometallic catalysts: Cu sites serve as a source of hydroxyl groups, while Pd/Pt sites enhance C–H bond cleavage efficiency, ultimately enabling high methanol conversion alongside low CO formation. From the perspectives of electronic structure and geometric configuration, this study establishes a theoretical framework to guide rational design of high-performance bimetallic catalysts for MSR.

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Mechanistic Insights into Methanol Steam Reforming on PdCu(111) and PtCu(111) Bimetallic Catalysts

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60672-X• Jan 15, 2026

Mechanism of Ce/La/Zr doping on the structure and anti-coking performance of Ni/MgO-MgAl2O4 catalyst

Authors: ZHANG Guopei, WANG Cong, ZHANG Xiaoyang, LI Zhaomin

Dry reforming of methane (DRM) converts CH4 and CO2 into syngas, offering a route to mitigate greenhouse gases. Ni-based catalysts suffer from sintering and carbon deposition at high temperatures. This work employs MgO-MgAl2O4 composite supports to regulate Ni loading and introduces Ce, La, and Zr as promoters to investigate their effects on DRM activity, structural stability, and surface oxygen species. Optimal Ni loading of 12.5% yields highest CH4 and CO2 conversions. Promoter introduction slightly suppresses low-temperature activity but substantially modifies support local structure and metal-support interface, improving NiO dispersion and increasing surface oxygen vacancies and active oxygen species (Oβ). These changes enhance CO2 adsorption-activation and suppress carbon deposition. After 20 h DRM, Ce-promoted catalyst shows smallest Ni particle growth (6.23→8.07 nm) and lowest carbon deposition, demonstrating superior stability and anti-coking capability. The study elucidates how Ce, La, and Zr enhance sintering and coking resistance via interfacial electronic modulation and improved oxygen storage/release, guiding rational design of stable Ni-based DRM catalysts.

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Mechanism of Ce/La/Zr doping on the structure and anti-coking performance of Ni/MgO-MgAl2O4 catalyst

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60743-8• Jan 15, 2026

A dataset for CO2 cycloaddition with ethylene oxide over metal oxide catalysts

Authors: XI Jianying, WANG Baojun, ZHANG Riguang

Metal oxide catalysts have emerged as promising materials for CO2 cycloaddition reactions due to their tunable composition, facile separation, reusability, and low cost. However, systematic investigations remain limited, and a comprehensive understanding of reaction mechanisms is hindered by the lack of extensive, well-curated datasets. This study establishes a systematic dataset of 102 metal oxide catalysts, including layered double hydroxide (LDH) and ZnO, with variations in metal dopant type and ratio, defect characteristics, and crystal plane orientation. Using high-throughput first-principles calculations, we generated a multi-dimensional dataset containing elementary reaction energies, vibrational frequencies, Bader charges, and density of states. A rigorous two-tiered quality control protocol ensures data integrity. The dataset reveals structure-performance relationships linking catalyst structural features to electronic descriptors (e.g., Bader charge transfer, p-band centers of O atoms, d-band centers of metal atoms) and catalytic activity. This work provides a reliable foundation for exploring catalytic performance and reaction mechanisms, and demonstrates how high-throughput calculations can generate domain-specific, mechanistically explicit data. Future efforts will focus on developing feature extraction code for seamless integration with machine learning frameworks, and the dataset will be continuously enriched through experimental validation and remain openly accessible.

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A dataset for CO2 cycloaddition with ethylene oxide over metal oxide catalysts

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60710-4• Jan 15, 2026

Reaction Network Database for Fast Pyrolysis of Vanillyl Alcohol Based on Molecular Wavefunction Descriptors

Authors: SHI Jingyuan, LI Chenchen, CHENG Xiaoxue, LING Qifan, MU Mao, WANG Shuang, JIANG Ding

Lignin pyrolysis is a promising route for sustainable production of high-value phenolic chemicals, yet the intricate radical reaction network remains a major bottleneck to optimizing product selectivity. This work constructs a standardized DFT computational database that systematically describes the fast pyrolysis of vanillyl alcohol at 823.15 K. The database features three key components: primary reaction pathways, thermodynamic energy barriers, and atomic-level electronic fingerprints. The dataset covers primary reaction pathways, secondary rearrangements, and both global and local reactivity indices of key intermediates. Notably, it innovatively integrates electronic-structure fingerprints, filling the gap in reaction-network–electronic-property correlation data. Standardized computational workflows and rigorous quality control ensure accuracy, consistency, and reproducibility. The public release of this dataset provides a reliable theoretical benchmark for mechanistic studies of lignin pyrolysis and offers foundational data support for rational design of new catalysts and refinement of reaction kinetic models. Ultimately, this database not only provides an important reference for data-driven catalyst development but also lays a theoretical foundation for precise regulation of lignin depolymerization.

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Reaction Network Database for Fast Pyrolysis of Vanillyl Alcohol Based on Molecular Wavefunction Descriptors

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60658-5• Jan 15, 2026

Effect of Mn doping on the structure and oxidative desulfurization properties of Co-V-O binary metal oxides

Authors: DONG Zhehan, WANG Hongli, LI Xiuping, ZHAO Rongxiang

Sulfur dioxide emitted from combustion of sulfur-containing aromatic compounds in fuels is a major contributor to atmospheric pollution. Oxidative desulfurization (ODS) has become a crucial complement to hydrodesulfurization (HDS) due to its mild reaction conditions and high efficiency in removing refractory aromatic sulfides. Metal doping is an effective strategy to modulate the electronic structure of catalysts and enhance catalytic performance. In this study, Mn-doped Co-V-O metal oxide (Mn-Co-V-O) was synthesized via a reflux method followed by high-temperature calcination. The structure, morphology, and surface chemical composition were characterized by FT-IR, XRD, SEM, XPS, and UV-vis DRS. The ODS performance toward dibenzothiophene (DBT) was evaluated using molecular oxygen as a green oxidant. Results indicated that Mn doping significantly enhanced the ODS activity compared to undoped Co-V-O. Under optimized conditions (110 °C, 0.03 g catalyst, 150 mL/min O2 flow, 20 mL model oil), a direct DBT removal rate of 81.6% was achieved. When combined with extraction, the desulfurization rate increased to 98.0%. Mechanistic studies revealed that Mn doping increased the surface oxygen vacancy concentration, facilitating oxygen activation to generate superoxide radicals (·O2−). Radical trapping experiments confirmed that ·O2− was the key active species responsible for selective oxidation of DBT to DBTO2. This study provides a reference for designing efficient metal oxide catalysts for deep oxidative desulfurization.

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Effect of Mn doping on the structure and oxidative desulfurization properties of Co-V-O binary metal oxides

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.3724/2097-213X.2026.JFCT.0003• Jan 15, 2026

Effect of Phosphorus and Transition Metal-Modified ZSM-5 on Catalytic Pyrolysis of C7 Hydrocarbons with Different Structures

Authors: HOU Kaijun, LIU Meijia, WANG Zhifeng, CAI Jinjun, GUO Rong, GAO Jinsen, WANG Gang, MA An

Phosphorus and transition metal-modified ZSM-5 zeolites were prepared via incipient wetness impregnation and characterized by XRD, N2 adsorption-desorption, and pyridine-IR spectroscopy. The effects of modified zeolites on catalytic pyrolysis of n-heptane, 3-methylhexane, and methylcyclohexane were systematically investigated, revealing the structure-activity relationship between acid properties and catalytic performance. Results indicate that phosphorus and transition metal modification can regulate the L/B acid ratio of ZSM-5, which significantly influences product distribution. An excessively high L/B acid ratio promotes hydrogen and coke formation, leading to pore blockage and reduced conversion, whereas an appropriate L/B acid ratio facilitates mild dehydrogenation, enhancing ethylene and propylene yields. Reactant conversion followed the order: n-heptane > 3-methylhexane > methylcyclohexane. Efficient conversion of 3-methylhexane and methylcyclohexane requires catalysts with high L acid amount, strong Brønsted acid amount, optimized L/B acid ratio, as well as high specific surface area and micropore specific surface area. This study provides critical insights for designing high-efficiency alkane pyrolysis catalysts.

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Effect of Phosphorus and Transition Metal-Modified ZSM-5 on Catalytic Pyrolysis of C7 Hydrocarbons with Different Structures

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60762-1• Jan 15, 2026

Binder-Mediated Regulation of Coating Structure over Monolithic Catalyst and Its Performance in CH4-CO2 Reforming

Authors: LIU Junyang, LIU Yupeng, XUE Rulin, LIU Lingji, ZHANG Chaoyang, LI Feng, LI Guoqiang, GENG Xiangdong, LI Lei, WANG Changzhen

The CO2 dry reforming of methane (DRM) is pivotal for CO2 utilization within the dual-carbon framework, offering advantages in carbon reduction and value-added chemical production. However, shaped catalysts suitable for industrial-scale DRM remain limited. This work constructs a monolithic catalyst using honeycomb cordierite as the structural support, systematically investigating the effects of organic and inorganic binders on coating structure and catalytic performance. Comparative studies reveal that the active coating fabricated with inorganic aluminum sol exhibits a continuous uniform morphology and excellent adhesion strength. During high-temperature calcination, elemental diffusion within Al2O3 networks bridges the cordierite surface with active catalyst particles, forming a (Ni-Mg)AlxO4 composite structure. This creates robust metal-support interactions between active sites and the residual alumina matrix. The interconnected mesoporous framework provides superior pore confinement, contributing to strong coating adhesion, enhanced activity, and improved resistance to carbon deposition in the monolithic m-NCM-Al-sol catalyst. In contrast, coatings derived from inorganic silica sol suffer from detachment and activity loss due to heterogeneous surface structures and poor adhesion. Organic binders demonstrate inferior performance in macroscopic coating uniformity, adhesion strength, mesoporous confinement, and localized electronic effects, resulting in the poorest catalytic performance. By optimizing aluminum sol coating parameters—binder content, active component dosage, and coating cycles—a synergistic balance between coating thickness and mass transfer is achieved. The optimized catalyst demonstrates excellent DRM performance, providing insights for constructing high-performance shaped catalysts with cordierite coatings.

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Binder-Mediated Regulation of Coating Structure over Monolithic Catalyst and Its Performance in CH4-CO2 Reforming

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60688-3• Jan 15, 2026

A Comprehensive Database for Ethane Dehydrogenation over Heteroatom-Doped Graphene-Supported Single-Atom Catalysts

Authors: CAO Yajie, WANG Baojun, ZHANG Riguang

Single-atom catalysts (SACs) exhibit near-100% atomic utilization, precisely tunable active sites, and superior catalytic performance, making them promising for ethane dehydrogenation (EDH). The nature of active metals, support properties, and coordination environments critically influence EDH performance. Graphene, with its excellent thermal stability and tunable coordination structure, serves as an ideal support. However, systematic understanding is lacking due to fragmented data. This work constructs a comprehensive database of heteroatom-doped graphene-supported SACs, encompassing five representative metal single atoms and 51 distinct coordination environments grouped into six major categories. High-throughput first-principles calculations yield multi-dimensional data including elementary reaction energies, vibrational frequencies, density of states, and Bader charges. A rigorous quality control system ensures reliability at both parameter-setting and computational result levels. The database provides complete raw calculation files, enabling in-depth analysis of catalytic performance, structure-performance relationships, and reaction mechanisms. Electronic structure analyses (DOS and Bader charge) elucidate the physical mechanisms underlying performance differences, establishing a structure-performance relationship characterized by 'dopant type → electronic state of active metal center → catalytic activity'. This database supports rational catalyst design and data-driven research paradigms, with future plans for feature extraction code and experimental validation.

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A Comprehensive Database for Ethane Dehydrogenation over Heteroatom-Doped Graphene-Supported Single-Atom Catalysts

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60670-6• Jan 15, 2026

Na Promoter Synergistic Tuning Co-Fe Alloy-Carbide Dual Sites: Directing Syngas Conversion to C2+ Alcohols

Authors: ZHANG Hengxuan, SUN Yan, SUN Qiwen, WU Jianmin

Direct conversion of syngas to higher alcohols (C2+ alcohols) is critical for coal-based resource utilization and energy security. Here, a series of Na-modified CoFe/Al2O3 catalysts were synthesized via incipient wetness impregnation and evaluated for syngas-to-alcohol reactions. Multiple characterizations (XRD, N2 adsorption-desorption, H2-TPR, XPS, DRIFTS, in situ Raman, Mössbauer spectroscopy) elucidated synergistic effects of Na and Fe promoters. Na facilitated formation of Co-Fe alloy sites during reaction, while Fe modified electronic state of Co and promoted transformation of lattice oxygen to adsorbed oxygen, increasing surface oxygen vacancies. Synergistic interaction between alloy and carbide sites enhanced CO insertion into olefin intermediates, improving C2+ alcohol selectivity. Under 260 °C and 2 MPa, Co1Fe1Na1 catalyst (n(Co):n(Fe):n(Na)=1:1:1) achieved total alcohol selectivity of 45%, with C2+ alcohols comprising 94.1% of total alcohols. This study provides insights into rational design of Co-based catalysts for efficient syngas conversion to C2+ alcohols.

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Na Promoter Synergistic Tuning Co-Fe Alloy-Carbide Dual Sites: Directing Syngas Conversion to C2+ Alcohols

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60748-7• Jan 15, 2026

Collection, Processing, and Sharing of a Dataset for the Selective Hydrogenation of Benzene to Cyclohexene

Authors: SUN Chao, ZHANG Bin

Cyclohexene is a crucial raw material for nylon production, and the selective hydrogenation of benzene is a key route for its preparation. To promote data sharing and reuse in this field, we collected and standardized experimental data on the hydrogenation of benzene to cyclohexene from publicly available literature, constructing a comprehensive dataset containing catalyst composition, reaction conditions, and reaction results (conversion, selectivity, and yield). This data descriptor details the source, field definitions, generation and processing workflow, quality control, sharing approach, and usage recommendations of the dataset, aiming to provide a reusable data foundation for subsequent statistical analysis, machine learning modeling, experimental design, and catalyst screening. The dataset is provided in Excel format and is accessible via GitHub and ScienceDB. It addresses the lack of unified field definitions, unit systems, and organizational formats in scattered literature data, enabling direct statistical analysis, correlation mining, and predictive modeling. The dataset is expected to accelerate research in optimizing ruthenium-based catalytic systems for selective benzene hydrogenation, which currently suffer from limited cyclohexene yield despite the use of aqueous-phase systems and inorganic salt additives. By offering a quality-controlled, structured dataset, this work supports data-driven approaches to overcome the thermodynamic favorability of complete hydrogenation to cyclohexane and to improve process economics.

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Collection, Processing, and Sharing of a Dataset for the Selective Hydrogenation of Benzene to Cyclohexene

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60674-3• Jan 15, 2026

A Dataset of Conducting Polymers Synthesized by Electropolymerization for Electrochemical Energy Storage in Aqueous Electrolytes

Authors: ZHU Bo, LU Xinyu, WANG Chao

Conducting polymers are promising electrode materials for aqueous ion batteries and supercapacitors due to their high conductivity, environmental friendliness, and flexibility. Electropolymerization enables controlled deposition of these polymers onto conductive substrates, tuning loading, morphology, and structure. This dataset systematically compares the electrochemical energy storage performance of conducting polymers derived from monomers including 1,10-phenanthroline, 5-amino-2-naphthalenesulfonic acid, o-aminophenol, 1,5-diaminonapthalene, s-triazine, and aromatic molecules with multiple carbonyl and imino groups. Aqueous electrolytes investigated include sulfuric acid, zinc sulfate, ammonium sulfate, potassium hydroxide, and zinc trifluoromethanesulfonate solutions. Galvanostatic charge-discharge at various mass-normalized current densities was employed to evaluate specific capacities. The dataset comprises 266 MB across 490 files, providing key parameters such as specific capacity, rate capability, and cycling stability. Analysis of this data enables inference on the influence of polymer structure and electrolyte composition on charge storage. This resource serves as a reference for the rational design of high-performance conducting polymer electrodes for aqueous energy storage devices.

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A Dataset of Conducting Polymers Synthesized by Electropolymerization for Electrochemical Energy Storage in Aqueous Electrolytes

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Original ResearchVol. 54, Issue 7 • pp. 100-112DOI: 10.1016/S1872-5813(26)60693-7• Jan 15, 2026

A dataset for electrocatalytic hydrogen evolution reaction performance of non-noble transition metal phosphides

Authors: ZHU Teng, WANG Huanhuan, LI Yuming

This dataset compiles the hydrogen evolution reaction (HER) performance data of 203 non-noble transition metal phosphide (TMP) catalysts, covering detailed information on catalyst preparation (e.g., phosphating temperature, precursor, synthesis method), chemical composition (mass fractions of elements such as Ni, Co, Fe, P, Mo, W and Zn), and testing conditions (e.g., electrolyte type and concentration, electrode substrate). The key parameters for catalytic performance include the overpotential at 10 mA/cm2 (η10) and the Tafel slope. This dataset has been rigorously extracted, cleaned, and standardized to ensure a high degree of structure and machine readability. This provides a reliable data foundation for data-driven methods, such as machine learning and statistical modeling, enabling rapid screening and design of high-performance HER catalysts, supporting performance prediction, in-depth structure-activity analysis and the rational development of novel catalysts.

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A dataset for electrocatalytic hydrogen evolution reaction performance of non-noble transition metal phosphides

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