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Molybdenum disulfide (MoS2) is a representative material of transition metal dichalcogenides with a wide range of potential practical applications. However, the low responsivity and relatively short lifespan of MoS2 photodetectors restrict their further development. To enhance the performance of MoS2 photodetectors, we propose a strategy involving defect introduction and surface passivation. Sulphur vacancy defects are introduced in MoS2 through remote hydrogen plasma treatment and surface passivation of MoS2 is achieved by alternating growth of Al2O3/HfO2 passivation layers. The obtained MoS2 photodetector features a high responsivity of 567 A W−1, a specific detectivity of 1.12 × 1010 jones, and a response time of 10.51/15.78 s at a bias of −5 V. Moreover, the obtained MoS2 photodetector maintains excellent stability. It is demonstrated that, after 2 months of placement in an atmospheric environment, the responsivity and the specific detectivity are maintained at approximately 95% of the fresh device, while the response time slightly increases. Finally, the underlying physical mechanism based on the energy band diagram has been proposed to interpret the obtained experimental results. The simple and efficient method proposed in this study for improving the performance of the MoS2 photodetector can open up new avenues for research in MoS2-based photoelectric devices. [ABSTRACT FROM AUTHOR]

In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model's ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability. [ABSTRACT FROM AUTHOR]

Understanding the ignition mechanism of spontaneous combustion is critical for preventing it. In this work, the effects of different test conditions including oxygen concentration, heating rate, oxidation carrier gas flow rate, and sample amount on the ignition temperature were studied with a thermal gravimetric analyzer. Further, the effects of coal properties on the ignition temperature were also investigated using 15 different low-rank coals. A heterogenous ignition model was proposed that small amount of active species is the key material leading to ignition. The heterogenous ignition mechanism well explained the complex effects of test conditions and coal properties on the ignition temperature of low-rank coal. Finally, an empirical formula for predicting the ignition temperature was derived for the rapid assessment of the spontaneous combustion potential.