Efficient photocatalytic nitrogen fixation via oxygen vacancies in Zr-MOFs at ambient conditions.

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 (H2NDC) and 1,4-phthalic acid (H2BDC) as ligands. Since the structure of H2NDC includes one extra benzene ring than H2BDC, 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 H2BDC, the benzene ring added in H2NDC 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.

Fe/Zr-MOFs constructed by a sunlight-responsive ligand for efficient photocatalytic nitrogen fixation under ambient condition.

Photocatalytic nitrogen fixation opens new opportunities for sustainable and healthier futures, and developing effective and inexpensive photocatalysts is the key. We use the ligand 3,3',5,5'-azomellitic acid (H4abtc) to connect with Fe clusters and Zr clusters to form stable metal-organic frameworks (MOFs) Fe-abtc and Zr-abtc, both of which are responsive to visible lights for nitrogen fixation. It is worth noting that the presence of NN in the ligand makes it respond to visible lights. The tetracarboxyl group is connected to the metal cluster to form a stable structure. The field-only surface integral method verified that the ligands were successfully applied into the synthesized MOF particles, which expanded the photoresponse range and enhanced the photonic interactions of the synthesized photocatalysts compared with pure MOF particles. The best photocatalytic nitrogen fixation performance of Fe-abtc and Zr-abtc is 49.8 µmol·g(cat.)-1·h-1 and 35.7 µmol·g(cat.)-1·h-1, respectively, the apparent quantum efficiency (AQY) of the sample Fe-abtc is 0.56 %, and the reliability of the source of N element is proved by the isotope 15N2. This work provides a new idea for the design of cheap and effective MOFs for photocatalytic nitrogen fixation.

A hybrid data-driven framework for diagnosing contributing factors for soil heavy metal contaminations using machine learning and spatial clustering analysis.

The efficacy of source apportionment is often limited by a lack of information on natural and anthropogenic contributing factors influencing soil heavy metal (HM) contaminations. To overcome this limitation and develop the data mining methods, a novel hybrid data-driven framework was proposed to diagnose the contributing factors in an industrialized region in Guangdong Province, China, mainly using a combination of naive Bayes (NB), random forest (RF), and bivariate local Moran's I (BLMI) on the basis of the multi-source big data. The medium industry types of enterprises from the freely available Baidu point of interest data were successfully classified, and then the 250 contaminating enterprises as a contributing factor were identified by the optimized NB classifier. The quantitative contributions of the nine contributing factors for the As, Cd, and Hg concentrations were determined by the optimized RF. The twelve spatial clustering maps between the three HM concentrations and the four key contributing factors were generated by BLMI, explicitly revealing their mutual interactions and internal effects and also intuitively showing the "high-high" areas and their distributions. This framework can obtain rich information on contributing factors such as medium industry types, contribution rates, spatial clusters, and spatial distributions.