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[Display omitted] Photocatalytic nitrogen fixation is seen to be a potential technology for nitrogen reduction due to its eco-friendliness, low energy consumption, and environmental protection. In this study, photocatalysts with abundant oxygen vacancies (Zr-naphthalene dicarboxylic acid (Zr-NDC) and Zr-phthalic acid (Zr-BDC)) were designed using 1,4-naphthalene dicarboxylic acid (H 2 NDC) and 1,4-phthalic acid (H 2 BDC) as ligands. Since the structure of H 2 NDC includes one extra benzene ring than H 2 BDC, the charge density differential of the organic ligand is probably altered. The hypothesis is proved by density function theory (DFT) calculation. The abundant oxygen vacancies of the catalyst offer numerous active sites for nitrogen fixation. Concurrently, the process of ligand–metal charge transfer facilitates photo-electron transfer, creating an active center for nitrogen reduction. Additionally, the functionalization of ligand amplifies another pathway for charge transfer, broadening the light absorption range of Metal-organic framework (MOF) and increasing its capacity for nitrogen reduction. In contrast to H 2 BDC, the benzene ring added in H 2 NDC structure acts as an electron energy storage tank with a stronger electron density difference favorable for photogenerated electron-hole separation resulting in higher photocatalytic activity in Zr-NDC. The experimental results show that the nitrogen fixation efficiency of Zr-NDC is 163.7 µmol g−1h−1, which is significantly better than that of Zr-BDC (29.3 µmol g−1h−1). This work utilizes cost-effective and non-toxic ingredients to design highly efficient photocatalysts, thereby significantly contributing to the practical implementation of green chemistry principles. [ABSTRACT FROM AUTHOR]

Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation measures. However, predicting soil loss while meeting the criteria of high precision, efficiency, and generalizability remains a challenge. Therefore, this study first used three machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to develop soil loss models and predict soil loss rates (SLRs). These soil loss models were constructed using field observation data with an average SLR of 1756.48 t/km2 from rainfall events and small watersheds in the hilly-gully region of the Loess Plateau, China. During training, testing and generalizability stages, the average coefficients of determination from the RF, SVM, and ANN models were 0.903, 0.860, and 0.836, respectively. Similarly, the average Nash-Sutcliffe coefficients of efficiency from the RF, SVM and ANN models were 0.893, 0.791 and 0.814, respectively. These results indicated that MLs have superior predictive performance and generalizability, and broad prospects for predicting SLRs. This study also demonstrated that the RF model outperformed better than the SVM and ANN models. Therefore, the RF model was used to simulate the SLR of each small watershed in the Chabagou watershed. Our results showed the four-year (2017–2020) average annual SLR of the small watersheds ranged from 0.73 to 1.63 × 104 t/(km2∙a) in the Chabagou watershed. Additionally, the results also indicated the SLR of small watersheds under the rainstorm event with a 100-year recurrence interval was 4.4–51.3 times that of other rainfall events.Furthermore, this study confirmed that bare land was the predominant source of soil loss in the Chabagou watershed, followed by cropland land and grassland. This study helps to provide the theoretical basis for deploying soil and water conservation measures to realize the sustainable utilization of soil resources in the future. [Display omitted] • Three machine learning algorithms were first used to build soil loss models. • Random forest demonstrated the most favorable performance for soil loss modeling. • Soil loss caused by rainstorms was 4.4–51.3 times that of other rainfall events. • Bare land was the predominant source of soil loss. [ABSTRACT FROM AUTHOR]