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AMiner is a novel online academic search and mining system, and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors, papers, conferences, journals and organizations. The system is subsequently able to extract researchers’ profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation. Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search. In addition, AMiner offers a set of researcher-centered functions, including social influence analysis, relationship mining, collaboration recommendation, similarity analysis, and community evolution. The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.

This paper first analyzes and researches the current practice status of digital transformation of Chinese agricultural production from production, credit, and management aspects. Then, it analyzes the measurement of agricultural production efficiency and the factors influencing agricultural production efficiency in the context of digital transformation by using DEA measurement tools. The impact of digitalization level enhancement on rural economic structure is also analyzed and explained from the perspective of technological progress bias through spatial econometric models. The results show that the level of digitalization promotes the development of agricultural modernization in terms of the change in the ranking of scores between 2014 and 2023. The correlation coefficients of the level of digital economy development, financial support for agriculture, and urbanization rate are 0.483, 1.163, and 0.764, which significantly promote the efficiency of agricultural production in China. There is a significant spatial dependence relationship between economic structure and digital transformation (P

In this article, silicotungstic acid (STA)-loaded metal–organic framework (MOF)-derived composites (C-STA@ZrO2) were successfully synthesized by simple strategies. X-ray diffraction, Fourier transform infrared, scanning electron microscopy, energy-dispersive X-ray, N2 physisorption, UV-vis diffuse reflection spectroscopy, and X-ray photoelectron spectroscopy techniques were used to characterize the as-obtained composites. Intriguingly, C-STA@ZrO2 exhibits excellent photocatalytic performance, and rhodamine B (RhB) (40 mg·L−1) in water can be degraded to 93.9% after 120 min of irradiation. Moreover, various catalysts, catalyst dosage, and dye concentrations on RhB degradation were evaluated. Besides, the reusability of C-STA@ZrO2 was also investigated. This work may provide a new and significant guideline for exploring excellent performance of MOF-derived hybrid material for wastewater purification.

In this article, Bi2O3@Zn-MOF hybrid nanomaterials were synthesized by supporting Zn-based metal–organic framework (Zn-MOF) through the hydrothermal method. X-ray diffractometer, Fourier transform infrared, scanning electron microscopy, energy-dispersive X-ray, N2 physisorption, X-ray photoelectron spectroscopy, and UV-Vis were used to characterize the physical and chemical properties of Bi2O3@Zn-MOF nanomaterials. The photocatalytic activity of the as-prepared hybrid has been studied over the degradation of rhodamine B (RhB). A catalytic activity of 97.2% was achieved using Bi2O3@Zn-MOF nanocomposite with the loading of 0.18 g Bi2O3, after 90 min of exposure to visible light irradiation, and the high photocatalytic performance was mainly associated with the nanorod structures, larger pore size, and broaden visible light absorption region due to the synergistic effect of the constituting materials. Furthermore, the Bi2O3@Zn-MOF nanocomposite can be reused three times and the degradation rate of RhB was maintained at 77.9%. Thus, the Bi2O3@Zn-MOF nanocomposite can act as a potential photocatalyst for the photodegradation of organic dyes in environmental applications.

In this article, Bi-based metal–organic framework-supported silicotungstic acid (STA) was synthesized by a simple hydrothermal method and used as a precursor for the preparation of the Bi-based MOF-derived catalyst (STA@C-Bi-BDC). Using a combination of FTIR, XRD, SEM-EDS, N2 adsorption–desorption, TG, UV-Vis DRS, and XPS techniques, the successful immobilization of STA groups on Bi-MOF-derived C-Bi-BDC was assessed. Furthermore, the photocatalytic performance of the as-prepared catalysts was investigated in the degradation of the RhB dye process under visible light. Within 120 min of visible light exposure, the high degradation rate of RhB (92.7%) by STA@C-Bi-BDC system was achieved, which was a lot larger than the STA (39.4%), C-Bi-BDC (59.2%), and STA@Bi-BDC (74.0%) system, and cyclic experiments exhibit that the STA@C-Bi-BDC is a relatively stable photocatalyst. More importantly, the catalyst shows high applicability for the degradation of other dyes. This study reveals a comprehensive strategy for the design of efficient Bi-based MOF-derived photocatalyst for organic dye-based wastewater treatment.