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Electrocatalytic N2 reduction reaction (NRR) serves as a promising approach for converting N2 to NH3 in a sustainable way to replace the energy-intensive Haber-Bosch process. MoS2-based electrocatalysts hold great potentials in catalyzing N2 reduction due to their similarity with active MoFe-co in biological nitrogenase. In this work, we reported a sulfur vacancy-rich MoS2 as an excellent electrocatalyst for NRR, where the sulfur vacancies (SVs) were easily controlled by regulating the amount of P dopants. MoS2 with abundant SVs (P-M-1) achieved a large NH3 yield rate of 60.27 µg h−1 mg−1cat. and high Faradaic efficiency of 12.22% towards NRR. Further mechanistic study revealed that P dopants not only created SVs as the active centers but also modulated the electronic structure for the enhanced adsorption and activation of N2 molecules, thus immensely promoting the catalytic performance of NRR.

Drug-disease associations provide important information for drug discovery and drug repositioning. Drug-disease associations can induce different effects, and the therapeutic effect attracts wide spread interest. Therefore, developing drug-disease association prediction methods is an important task, and differentiating therapeutic associations from other associations is also very important. In this paper, we formulate the known drug-disease associations as a bipartite network, and then present a novel representation for drugs and diseases based on the bipartite network and linear neighborhood similarity. Thus, we propose the network topological similarity-based inference method (NTSIM) to predict unobserved drug-disease associations. Further, we extend the work to the association classification, and propose the network topological similarity-based classification method (NTSIM-C) to differentiate therapeutic associations from others. Compared with existing drug-disease association prediction methods, NTSIM can produce superior performances in predicting drug-disease associations, and NTSIM-C can accurately classify drug-disease associations. Further, we analyze the capability of proposed methods by using several case studies. The studies show the usefulness of NTSIM and NTSIM-C in the real applications. In conclusion, NTSIM and NTSIM-C are promising for predicting drug-disease associations and their therapeutic functions.

The livestock product consumption per capita in China has almost doubled in the past three decades. The planting of green fodder has increased in the agropastoral ecotone of China to meet the increasing demand for livestock feed, and the green fodder expansion can have subsequent consequences on the environment. However, information on the area and distribution of green fodder is very limited. Here, we developed a pixel- and phenology-based algorithm to map green fodder and track its dynamics in the northeastern Tibetan Plateau, a typical alpine pasture region in China, using all the available Landsat images and Google Earth Engine (GEE). We developed a simple approach for the rapid identification of green fodder fields by using a new green fodder index that considers the unique phenology of green fodder, which has higher greenness and water content in the late growing season than other vegetation. A total of 858 Landsat images were used to generate green fodder maps in northeastern Tibetan Plateau (including Zeku, Guinan, and Tongde Counties) in three periods (circa 2010, 2015, and 2019). The overall accuracies of our green fodder maps were 93–97% and the Matthews correlation coefficients were 0.76–0.83. We found a rapid expansion of green fodder from 16.3 km2 in 2010 to 136.1 km2 in 2019. Newly cultivated green fodder occurred in both existing croplands and natural grasslands. Our study demonstrated the potential of the phenology-based approach, all the available Landsat images, and GEE for tracing the historical dynamics of green fodder at 30-m resolution in alpine regions. Our findings advance our understanding of changes in forage area, production, the supply–demand gap, and the ecological and climatic consequences of green fodder expansion.

Excessive ammonium (NH4+) and phosphate (PO43−) in aquatic environments can cause serious eutrophication. Agricultural waste is an efficient adsorbent for nutrients removal and has good potential for comprehensive utilisation of resources. In this study, two Chinese medicinal herbal residues, Rhizoma Typhonium Gigantei (ZB) and Radix Glycyrrhizae Preparata (ZG), were implemented to simultaneously remove NH4+ and PO43− from water. Parameters such as adsorbent dosage, solution pH, contact time and initial concentration that influenced the adsorption process onto adsorbents, along with removal mechanism and practical performance were investigated. The microstructure of ZB was irregular with numerous spherical particles, while ZG displayed an uneven flaky structure with few rough grooves. The nutrients adsorption was significantly influenced by pH. The maximum adsorption capacity by ZB was 91.84 N mg g−1 and 71.81 P mg g−1. In addition, the maximum adsorption capacity recorded by ZG was 100.86 N mg g−1 and 89.51 P mg g−1 (pH, 8.0; dosage, 0.2 g L−1; initial concentration, 60 mg L−1; and contact time, 180 min, for both the herbal residues). The adsorption of NH4+ and PO43− was well described by the pseudo-second-order and intra-particle diffusion models, respectively. The Langmuir isotherm model is the best fit model for the adsorption equilibrium data, suggesting monolayer adsorption of NH4+ and PO43− by the two adsorbents. In addition, NH4+ was predominantly adsorbed by electrostatic attraction, ion exchange and complexation, whereas PO43− was predominantly adsorbed by intra-particle diffusion and ligand exchange. Practical application analysis demonstrated that ZG could remove and recover more NH4+ and PO43− from swine wastewater than ZB. Therefore, ZG exhibited a more significant potential to simultaneously remove and recover NH4+ and PO43− from wastewater.

The NH3 is generally synthesized by the traditional Haber–Bosch method, which is energy-consuming and releases a lot of CO2. Electrocatalytic fixation of N2 is highlighted as a carbon-free route to generate NH3 by the nitrogen reduction reaction (NRR), but strongly demands highly efficient and durable electrocatalysts. Herein, we propose the multiphasic 1T@2H-MoSe2 as a highly active NRR catalyst for the first time, which exhibits excellent activity for the NRR with a NH3 yield of 19.91 ± 0.89 μg h−1 mgcat.−1 and a FE of 2.82% ± 0.1% at −0.6 V in 0.1 M Na2SO4. Moreover, a good durability is also demonstrated.