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.
Dry reforming of methane (DRM), which simultaneously converts CH4 and CO2 into syngas, represents a promising strategy for greenhouse gas mitigation and carbon-neutral feedstock production. However, Ni-based catalysts commonly suffer from severe sintering and carbon deposition under high-temperature, leading to rapid deactivation. In this work, MgO-MgAl2O4 composite supports are employed to regulate Ni loading. Ce, La and Zr are introduced as promoters to investigate their effects on DRM activity, structural stability, and surface oxygen species. The results show that an optimal Ni loading of 12.5% offers highest CH4 and CO2 conversions. Although the introduction of promoters slightly suppresses catalytic activity in the low-temperature region, they substantially modify the local structural order of the support and the metal-support interfacial environment, thereby improving NiO dispersion and increasing the concentration of surface oxygen vacancies and active oxygen species (Oβ). These changes enhance CO2 adsorption-activation and effectively suppress carbon deposition. After 20 h of DRM reaction, the Ce-promoted catalyst exhibits the smallest Ni particle growth (6.23→8.07 nm) and the lowest carbon deposition, demonstrating superior structural stability and anti-coking capability. This study elucidates how Ce, La and Zr promoters enhance the resistance to sintering and coking through interfacial electronic modulation and improved oxygen storage/release capacity, offering valuable guidelines for the rational design of highly stable Ni-based DRM catalysts.
Slag gasification represents one of the key technologies for the clean and resource-oriented treatment of organic solid wastes with high heavy-metal content, such as landfilled municipal solid waste. Using rice husk as a silicon source, this work investigated the effect of rice-husk addition on the vitrification of landfilled-waste slag and the solidification behavior of heavy metals. The results demonstrated that the incorporation of rice husk (5%−10%) lowered the flow temperature of the slag and effectively suppressed heavy-metal leaching. Specifically, the leaching concentrations of Cr and Zn decreased from 41.60 and 108.00 mg/L to 5.89 and 7.10 mg/L, respectively. The active SiO2 introduced by rice husk promoted the formation of highly polymerized siloxane networks (Q3, Q4), which inhibited gasification-induced migration of heavy metals and enhanced physical encapsulation of them. Concurrently, it facilitated the incorporation of Zn2+, Cr3+ and Cu2+ via substitution or solid-solution mechanisms into stable mineral phases such as Zn2SiO4 and CuFe2O4, immobilizing them in garnet and spinel structures. This process increased the residual fraction and reduced the bioavailability of the heavy metals. The present study elucidates the mechanistic role of rice husk in modulating slag structure and heavy-metal immobilization, thereby provides a theoretical basis for the clean and efficient co-treatment of landfilled waste and biomass through a “treating waste with waste” strategy.
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.
Levulinic acid (LA) is a promising platform product with wide industrial applications. In recent years, the efficient conversion of cellulose into LA has become a research hotspot. However, traditional experimental optimization methods are often time-consuming and inefficient, therefore limiting its further upgrading. In this study, we integrated multidimensional data, including 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. Among them, the gradient boosting regression model achieved the best performance, with a test-set determination coefficient of 0.94 and the lowest root-mean-square error. SHapley Additive exPlanations and partial dependence analysis revealed that water fraction, catalyst dosage, and reaction temperature were the key factors influencing LA formation. By integrating the gradient boosting model with the particle swarm optimization algorithm, RuCl3 was identified as an efficient catalyst under high-temperature and short-reaction-time conditions. This study demonstrates the potential of applying the machine learning method in cellulose conversion research, and provides a data-driven strategy and theoretical guidance for the efficient and green production of LA.
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 promotes the formation of more O– 2 species on the catalyst surface, which in turn significantly enhances both the activity and selectivity of La2O3 catalysts.
The sulfur dioxide produced by the combustion of sulfur-containing aromatic compounds in fuels are a major contributor to atmospheric pollution. Due to its mild reaction conditions and excellent efficiency in removing aromatic sulfides, oxidative desulfurization has become a crucial complement to hydrodesulfurization technology. Metal doping represents an effective strategy for modulating the electronic structure of catalysts and enhancing their catalytic performances. In this study, Mn-doped Co-V-O metal oxide (Mn-Co-V-O) was successfully synthesized via a reflux method followed by high-temperature calcination. The structure, morphology, and surface chemical composition of Mn-Co-V-O were systematically characterized using FT-IR, XRD, SEM, XPS and UV-vis DRS. The oxidative desulfurization (ODS) performance of the catalyst toward dibenzothiophene (DBT) was evaluated using oxygen as a green oxidant. Results indicated that Mn doping significantly enhanced the ODS activity of Mn-Co-V-O compared to the undoped Co-V-O. Under optimized reaction conditions, the direct removal rate of DBT reached 81.6%. When combined with an extraction process, the desulfurization rate was further increased to 98.0%. Mechanistic studies revealed that Mn doping effectively increased the surface oxygen vacancy concentration, facilitating the activation of oxygen to generate superoxide radicals (·O– 2). Free radical trapping experiments confirmed that ·O– 2 served as the key active species responsible for the selective oxidation of DBT to DBTO2. This study provides an important reference for the design of highly efficient metal oxide catalysts applicable to deep oxidative desulfurization in the future.
Single-atom catalysts (SACs) have shown great promise for ethane dehydrogenation (EDH), owing mainly to their near-100% atomic utilization, precisely tunable active sites, and superior catalytic performance. Studies reveal that the nature of active metals, the properties of support and the coordination environments are critical factors affecting EDH performance. Among various supports, graphene has emerged as an ideal support material due to its excellent thermal stability and flexible, tunable coordination structure. While previous research has explored the effects of different active metals and coordination environments, a systematic understanding of their underlying principles is still lacking due to fragmented data. To bridge this gap, this work constructed a systematic, comprehensive database of heteroatom-doped graphene-supported SACs, covering five representative metal single atoms and 51 distinct coordination environments grouped into six major categories. Through high-throughput calculations, multi-dimensional data were systematically obtained, including elementary reaction energies, vibrational frequencies, density of states and Bader charges. A rigorous quality control system was implemented at both the parameter-setting and computational result levels. The database also provides complete raw calculation files, offering reliable data support for in-depth analysis of the catalytic performance, structure-performance relationship and reaction mechanisms of heteroatom-doped graphene-supported SACs in EDH.
This dataset compiles the 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.
Carbohydrates, derived from abundant biomass resources, hold great promise for their conversion into fine platform chemicals and fuels, which is crucial for the sustainable development. The processes for the conversion of carbohydrates 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.
Methanol steam reforming (MSR) stands as a pivotal process for efficient hydrogen production. In this study, density functional theory (DFT) calculations were employed to conduct a comparative analysis of the MSR reaction mechanism on PdCu(111) and PtCu(111) bimetallic surfaces. The investigation unveiled the underlying mechanism by which alloying modulates reaction pathways and overall catalytic performance. Notably, Cu sites were found to stabilize adsorption of OH and CH2O species, whereas Pd/Pt sites exhibited a preferential affinity for CO molecule. This spatial site separation facilitates progression along the formate pathway. PdCu(111) demonstrated 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 the 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 the rational design of high-performance bimetallic catalysts for MSR.
Hierarchical aluminum-rich zeolites show promising potential in the Knoevenagel condensation; however, their synthesis is hindered by high preparation cost and energy consumption. To address these issues, a green synthetic route has been developed for preparing hierarchical NaY zeolite using submolten salt (SMS) activated perlite as the sole source of silicon and aluminum. Comprehensive characterization shows that the NaY zeolite synthesized from SMS activated perlite exhibits high purity and crystallinity. This aluminum-rich NaY zeolite, with a framework SiO2/Al2O3 molar ratio of approximately 4.2, features intercrystalline mesopores, a large external surface and abundant basic sites. Investigation into the crystallization process of the hierarchical NaY zeolite indicates that during the initial stage, small crystals assemble and grow on the surface of SMS activated perlite. As crystallization proceeds, the zeolite crystals rapidly grow and aggregate around the activated sample, ultimately forming a crystal-packed morphology. In the Knoevenagel condensation of benzaldehyde with ethyl cyanoacetate, the synthesized hierarchical NaY zeolite demonstrates higher benzaldehyde conversion compared to conventional counterparts.
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.
The CO2 dry reforming of methane (DRM) reaction represents a pivotal technology for CO2 utilization technology within the dual-carbon framework, offering significant advantages in carbon reduction, emission mitigation, and the production of value-added chemicals. However, research reports on shaped catalysts suitable for industrial-scale DRM processes remain limited. In this work, a monolithic catalyst was constructed using honeycomb cordierite as the structural support, and the effects of organic and inorganic binders on the coating structure and catalytic performance were systematically investigated. Comparative studies revealed that the active coating fabricated with an inorganic aluminum sol exhibited a continuous uniform morphology and demonstrated excellent adhesion strength. Simultaneously during high-temperature calcination, elemental diffusion within the Al2O3 networks bridged the cordierite surface with active catalyst particles, forming a (Ni-Mg)AlxO4 composite structure. This created robust “metal-support” interactions between active sites and the residual alumina matrix. The interconnected mesoporous framework provided superior pore confinement, which ultimately contributed to strong coating adhesion, enhanced activity and improved resistance to carbon deposition in the monolithic m-NCM-Al-sol catalyst. In contrast, mesoporous network coatings derived from inorganic silica sol suffered from coating detachment and catalytic activity loss due to heterogeneous surface structures and poor adhesion. Organic binders demonstrated inferior performance compared to inorganic binders in macroscopic coating uniformity, adhesion strength, mesoporous confinement capability and localized electronic effects, resulting in the poorest catalytic performance among modified samples. Furthermore, by optimizing the aluminum sol coating process parameters such as binder content, active component dosage, and coating cycles, a synergistic balance between coating thickness and mass transfer performance was achieved. The optimized integrated catalyst demonstrated excellent performance in DRM reactions. This work provides valuable insights for controlling and constructing high-performance shaped catalysts with cordierite coatings.
Conducting polymers are promising candidates for aqueous ion batteries, owing to their high conductivity, environmental friendliness, and flexibility. Electropolymerization offers a facile route to construct conducting polymers at the surface of conductive substrates with controlled loading, morphology and structure. This dataset compares the electrochemical energy storage performance of some conducting polymers in aqueous electrolytes. Monomers to construct conducting polymers involve 1,10-phenanthroline, 5-amino-2-naphthalenesulfonic acid, o-aminophenol, 1,5-diaminonapthalene, s-triazine, and some aromatic molecules with multiple carbonyl and imino groups. The electrolytes used to investigate the charge storage performance of these conducting polymers include aqueous sulfuric acid, zinc sulfate, ammonium sulfate, potassium hydroxide, and zinc trifluoromethanesulfonate solutions. Galvanostatic charge-discharge method at various active material mass-normalized current densities is used to evaluate the specific capacities of these conducting polymers. The total volume of this dataset is 266 MB with 490 files. This dataset can serve as the reference for the future design of electrode materials for electrochemical energy storage devices including aqueous ion batteries and supercapacitors.
Cyclohexene is an important raw material for nylon production, and the selective hydrogenation of benzene is a key route for preparing cyclohexene. 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 and constructed 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, with the aim of providing a reusable data foundation for subsequent statistical analysis, machine learning modeling, experimental design, and catalyst screening.
Fe-Mn catalysts have garnered considerable attention for industrial applications in the Fischer-Tropsch synthesis (FTS) process. Carbon adsorption and permeation on catalyst surfaces constitute critical elementary steps in the in situ formation of active phases in iron-based FTS catalysts. Herein, density functional theory (DFT) calculations are employed to systematically investigate the atomistic structures, thermodynamic stabilities, and electronic properties of carbon-deposited Fe-Mn alloy surfaces at the early stage of carburization. These Fe-Mn alloy surfaces show distinct thermodynamic sensitivity to carbon atoms adsorbed on the surface and permeating into the interstitial sites. By combining DFT with minima-hopping structural searches, we demonstrate that the initial stage of carbon permeation cannot trigger the reconstruction of surface regions to form surface iron carbide phases. The addition of manganese thermodynamically hinders the carbon permeation process. Although deposited C atoms modulate the electronic structure of metals, the presence of manganese retards the shift of d-band centers of metals to those of the bulk iron carbide phases. Our study provides an atomic-scale insight into the in situ evolution of Fe-Mn catalyst surfaces during the carbon deposition process and indicates that the manganese promoter has a noticeable effect on carbon permeation.
Lignin pyrolysis is a promising avenue for the sustainable production of high-value phenolic chemicals. Nevertheless, unraveling the intricate radical reaction network remains a major bottleneck to optimizing product selectivity. This work constructs a standardized DFT computational database, which systematically describes the fast pyrolysis of vanillyl alcohol at 823.15 K. The database features out three key components, including primary reaction pathways, thermodynamic energy barriers, and atomic-level electronic fingerprints. Ultimately, the construction of this database not only provides an important reference for data-driven catalyst development, but also lays a theoretical foundation for the precise regulation of lignin depolymerization.
Metal oxide catalysts have emerged as highly promising materials for the CO2 cycloaddition reaction, owing to their tunable composition, facile separation, reusability and low cost. Previous studies have identified that the type and ratio of metal dopants, surface defect characteristics and crystal plane orientation are critical factors affecting catalytic performance. Despite this potential, systematic investigations into metal oxide catalysts for CO2 cycloaddition remain limited and a comprehensive understanding of the underlying reaction mechanisms is hindered by the lack of extensive, well-curated datasets. To address this gap, this study establishes a systematic and comprehensive dataset of metal oxides including layered double hydroxide (LDH) and ZnO catalysts, encompassing variations in metal dopant type and ratio, defect characteristic and crystal plane orientation. Through high-throughput calculations, we have generated a robust multi-dimensional dataset containing elementary reaction energies, vibrational frequencies, Bader charges and density of states. A rigorous two-tiered quality control protocol is applied to both computational parameter settings and output results, ensuring the integrity and reliability of the data. This dataset, providing complete raw calculation files, offers a reliable foundation for exploring catalytic performance, structure-performance relationships and reaction mechanisms of metal oxide catalysts in CO2 cycloaddition.
