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PbS films grown on quartz substrates by the chemical bath deposition method were annealed in an O2 atmosphere to investigate the role of oxygen in the sensitization process at different annealing temperatures. The average grain size of the PbS films gradually increased as the annealing temperature increased from 400 °C to 700 °C. At an annealing temperature of 650 °C, the photoresponsivity and detectivity reached 1.67 A W−1 and 1.22 × 1010 cm Hz1/2 W−1, respectively. The role of oxides in the sensitization process was analyzed in combination with X-ray diffraction and scanning electron microscopy results, and a three-dimensional network model of the sensitization mechanism of PbS films was proposed. During the annealing process, O functioned as a p-type impurity, forming p+-type PbS layers with high hole concentrations on the surface and between the PbS grains. As annealing proceeds, the p+-type PbS layers at the grain boundaries interconnect to form a three-dimensional network structure of hole transport channels, while the unoxidized p-type PbS layers act as electron transport channels. Under bias, photogenerated electron–hole pairs were efficiently separated by the formed p+-p charge separation junction, thereby reducing electron–hole recombination and facilitating a higher infrared response.

Landslides are among the most common geological hazards that result in considerable human and economic losses globally. Researchers have put great efforts into addressing the landslide prediction problem for decades. Previous methods either focus on analyzing the landslide inventory maps obtained from aerial photography and satellite images or propose machine learning models—trained on historical land deformation data—to predict future displacement and sedimentation. However, existing approaches generally fail to capture complex spatial deformations and their inter-dependencies in different areas. This work presents a novel landslide prediction model based on graph neural networks, which utilizes graph convolutions to aggregate spatial correlations among different monitored locations. Besides, we introduce a novel locally historical transformer network to capture dynamic spatio-temporal relations and predict the surface deformation. We conduct extensive experiments on real-world data and demonstrate that our model significantly outperforms state-of-the-art approaches in terms of prediction accuracy and model interpretations.