长江师范学院

Nanocomposites have demonstrated excellent performance as photocatalysts in various photocatalytic reactions. Herein, we developed a nanocomposite comprising modified graphitic carbon nitride (MCN) and gold nanoparticles (Au NPs) for the hydrogen evolution reaction (HER). The MCN was synthesized via co-polymerization of melamine and malonamide. The Au/MCN nanocomposite exhibited hydrogen evolution rates of 86.7 µmol·h− 1·g− 1 under visible light and 1281.2 µmol·h− 1·g− 1 under simulated sunlight, respectively. The photocatalytic HER rate of Au/MCN under simulated sunlight was enhanced by a factor of 99.3 compared to pristine g-C3N4 nanosheet (CN). Controlled hydrogen evolution experiments confirmed the contribution of the surface plasmon resonance (SPR) effect. By combining HER results with conventional characterization and electrochemical tests, we elucidated the photocatalytic mechanism, revealing that the Schottky junction and SPR effect synergistically enhance the photocatalytic performance. The SPR effect was further verified by photocatalytic experiments and ultraviolet-visible diffuse reflectance spectroscopy.

Aromatic hydrocarbons such as benzene, toluene, and ethylbenzene are extensively used as solvents in coatings, resin, and artificial leather industries. Azeotropic mixtures involving these compounds are commonly encountered in chemical manufacturing, where accurate azeotropic temperature and composition are essential for designing and optimizing separation processes such as extractive and pressure-swing distillation. In this study, two quantitative structure–property relationship (QSPR) models were developed to predict the azeotropic temperature and composition of binary mixtures containing aromatic hydrocarbons using only molecular structural information. The models show excellent agreement with experimental data (R2 = 0.9454 and 0.9448, R_adj^2 = 0.9400 and 0.9413). Internal validation via leave-one-out cross-validation yields R_cv^2 = 0.9308 and 0.9364, while external validation using an independent test set yields Q_ext^2 = 0.8939 and 0.9364, indicating strong robustness and superior predictive performance compared to previously reported models. Molecular geometries were optimized using HyperChem 8.0, employing MM + and PM3 methods. Molecular descriptors were calculated using the Online Chemical Modeling Environment (OCHEM). Binary mixture descriptors were derived from pure-component descriptors via Kay’s mixing rule. The genetic function approximation (GFA) algorithm was used to select the most relevant descriptors, and predictive models were constructed using multiple linear regression (MLR). Model robustness and predictive capacity were evaluated using leave-one-out cross-validation and an external test set, with applicability domains assessed via Williams plots. All computational procedures and modeling analyses were performed using OCHEM, SPSS, and HyperChem 8.0.

Online reviews play a crucial role in addressing patients’ concerns and guiding them toward online consultations in telemedicine. This study employs game-theoretic models to analyze how online reviews and public benefit influence the online channel strategies of medical institutions. We constructed three different channel structures: medical institutions providing offline services only (the offline model), establishing their own online channels (the self-built model), and cooperating with third-party platforms to provide online services (the cooperation model). Our analysis identifies the conditions under which medical institutions can successfully introduce online channels. Notably, even when online reviews reflect negative information, institutions can still increase profits by choosing the self-built channel, provided the degree of negativity is limited. Furthermore, the self-built model offers greater benefits to patients compared to the cooperation model. We also explore the interaction between online reviews and public benefit, providing strategic recommendations for institutions with high or low levels of public benefit. Finally, we demonstrate that opening online reviews in the online channel may not be profitable for public benefit medical institutions unless the proportion of positive reviews exceeds a certain threshold. These findings offer practical insights for medical institutions aiming to optimize their online channel strategies in the era of telemedicine.

Vegetation physiological processes are critical regulators of terrestrial carbon-water cycles and local microclimate dynamics, with photosynthetically active radiation (PAR, 400-700 nm) serving as a primary driving force. However, most vegetation-climate process models simplify the fraction of PAR in global solar radiation as a constant 50%, potentially introducing diurnal simulation biases that propagate into cumulative annual errors in vegetation carbon-water coupling estimates. To address this limitation, we first evaluated the performance of three empirical models for simulating the dynamic PAR fraction and integrated the most accurate model into the Vegetation Microclimate Process (VMcP) model, and further used typical meteorological year (TMY) data of Beijing, Shanghai and Shenzhen as input to compare the differences in vegetation carbon-water processes before and after the improvement. The results show that the diurnal variation range of PAR fraction in global solar radiation is between 39% and 58%. The existing models that neglect the dynamic changes in PAR may overestimate vegetation transpiration cooling and photosynthetic carbon sequestration by 2.3% and 3.5%, respectively. Meanwhile, Shenzhen (64.3 W/m2; 1.59 g/m2 & centerdot;d), characterized by favorable light and thermal conditions, is more prone to large errors compared with Shanghai (47.6 W/m2; 1.21 g/m2 & centerdot;d) and Beijing (39.5 W/m2; 0.93 g/m2 & centerdot;d). This study provides a novel tool for the accurate assessment of vegetation-mediated microclimate improvement, and offers a new perspective for nature-based climate solutions.

Two-dimensional (2D) metal-oxides are promising for methane electrooxidation due to their excellent active edge sites and large surface areas. Yet, these materials suffer from limited charge transfer and restricted edge regions, which are further exacerbated by layer-layer stacking/aggregation and/or selfcurling during use. This work develops unique hierarchical catalysts by assembling 2D nanoholey NiCo2O4 with maximized active oxygen vacancies (NiCo2O4-NF-Vo) present on both edges and surfaces. This is achieved by combining the introduction of porosity in vertically-standing 2D planes, followed by H2 cold plasma treatment for efficient, selective, and stable methane electrooxidation to acetaldehyde. The Faradaic efficiency reached 74% at 1.5 V vs. RHE, maintaining stability for 120 h while suppressing the oxygen evolution reaction through oxygen vacancy incorporation. In situ surface analysis and density functional theory calculations revealed that the upward shift of the d-band centers of Ni and Co active sites with oxygen vacancies could enhance key intermediates (*CH2OH and *CH2) adsorption on exposed cobalt sites enabled by vertically-standing ultrathin porous sheets. Improved adsorption facilitates ratelimiting-step kinetics and C-C bonding, increasing productivity levels of acetaldehyde. Oxygen vacancyincorporated, fully-exposed 2D-holey NiCo2O4-NF-Vo demonstrates exceptional catalytic performance and stability for selective methane oxidation. This innovative approach provides a strategic design framework for vertically-mountable and selectively-activatable ultrathin 2D metal oxides in catalytic applications. (c) 2026 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights are reserved, including those for text and data mining, AI training, and similar technologies.