Long persistent luminescence materials (LPLMs) have demonstrated significant potential in the field of photo- and electro-catalysis due to their unique capability of storing and controllably releasing photogenerated charge carriers. These materials offer innovative solutions for environmental remediation and sustainable energy technologies. This review systematically summarizes recent advances in the application of LPLs in photo- and electro-catalysis, outlining their developmental history and underlying mechanisms. Emphasis is placed on their applications in organic pollutant degradation, photocatalytic hydrogen evolution, and photovoltaic cells. Furthermore, design strategies and research frameworks for LPLs are discussed. The current limitations and challenges in this field are examined, and future research directions are proposed to facilitate the transition of LPLMs from fundamental research to practical applications in energy and the environment.

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.

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

The selective hydrogenation of biomass-derived compounds is pivotal for the synthesis of high-value-added chemicals. The controllable hydrogenation in furfural for furfuryl alcohol or tetrahydrofurfuryl alcohol is highly desirable yet challenging. In this study, Ni2Al-LDO（layered double oxide） metal oxide catalyst featuring highly dispersed surface NiO was synthesized via a structural topological transformation using layered double hydroxides. The catalyst exhibited excellent performance in the hydrogenation of furfural, achieving 91.42% yield of furfuryl alcohol (FOL) at 160 °C and 1.4 MPa H₂. Gradual reduction of Ni2Al-LDO produced Ni/NiO mixtures, enabling tunable shift from FOL to tetrahydrofurfuryl alcohol (HFOL) as the NiO content decreased and the Ni content increased. After a two-hour reduction at 700 °C, the HFOL yield reached 93.95%. CO₂-TPD, NH₃-TPD and FT-IR measurements revealed that variations in reduction degree on nickel influenced the adsorption behavior of furfural. NiO species could only adsorb the C=O group of furfural, isopropanol serves as the hydrogen source, and furfural undergoes hydrogenation to form furfuryl alcohol following the MPV conversion pathway. Whereas the flat adsorption mode on the metallic Ni surface facilitated the simultaneous adsorption and activation of both the furan ring and the carbonyl group of furfural. In addition, the hydrogen sources of the reaction systems also differ: the Ni2Al-LDO catalyst is only capable of activating isopropanol, with the reaction proceeding via the MPV mechanism; in contrast, Ni2Al-LDO-700 can activate both H₂ and isopropanol, where H₂ serves as the primary hydrogen source. This work elucidates a clear structure-activity relationship centered on the metal oxidation state and provides a practical reduction-engineering approach for designing adaptable catalysts in biomass upgrading.

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.

Low-temperature Chemical Looping Air Separation (CLAS) is a promising technology for producing oxygen-enriched gas streams. It utilizes the redox properties of solid oxygen carriers to selectively capture and release oxygen from the air. Oxygen vacancy formation energy (E_ovf) can be used to evaluate the ease of oxygen release by the oxides. In this study, the applicable range of E_ovf for CLAS oxygen carriers was determined to be <2.3 eV using the thermodynamic calculations. A graph neural network (GNN) was trained and used to predict the E_ovf. By predicting E_ovf with the GNN model, a screening from the 3649 compositions of Sr_xA_1−xFe_yB_1−yO₃ perovskites was conducted to discover the CLAS oxygen carriers. The prediction results indicate that the doping of Ba and Ca ions at the A site and Co ions at the B-site can effectively reduce the E_ovf of the perovskite structure. The feasibility of screening the CLAS oxygen carriers using the E_ovf method was validated through the rediscovery some known materials. These results support the effectiveness of GNNs in accelerating the discovery of CLAS oxygen carriers.

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.

Olefin hydroformylation, as a key process for synthesizing high-value-added aldehydes, has extended its application scope from short-chain olefins to long-chain olefins (C₆₊). Traditional homogeneous catalytic systems face challenges such as difficulty in separating and recovering precious metal rhodium (Rh), making heterogeneous catalytic systems for olefin hydroformylation a research hotspot. By designing catalyst supports, these issues can be effectively addressed. This review summarizes recent research progress in supports for heterogeneous Rh-based catalysts, with a focus on the influence of the structural regulation strategy of the inorganic oxide-supported catalysts, porous carbon-supported catalysts, organic porous polymer-based catalysts, zeolite-supported catalysts, and composite-supported catalysts on the dispersion of active sites, regioselectivity and the cycle stability, and the review aims to provide valuable insights for the rational design of high-performance heterogeneous hydroformylation catalysts.

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.

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.