Low-cost X-ray detectors with high performance, durability, and flexibility, are required for a wide range of applications in several fields, such as medical (diagnostic radiology, imaging, etc.), nondestructive testing (radioscopic inspections, radiography testing, etc.), security and defence (baggage/body scanning systems, paper mail, etc.), nuclear and radiation industries (nuclear power plants, research reactors, users of nuclear gauges, etc.), and research and development. [1] X-ray detection using semiconductors, based on the direct generation of electrical signals by X-rays (i.e., direct scheme), offers better spatial resolution and a simpler route than indirect schemes, in which X-rays are converted into photons by scintillating phosphors before detection by photodiode arrays. [2] Currently, the conventional materials used for direct conversion of X-rays include stabilized amorphous Se (α-Se), PbI 2 , HgI 2 , CdTe, and CdZnTe. [3] Metal halide perovskites represent a family of the most promising materials for fascinating photovoltaic and photodetector applications due to their unique optoelectronic properties and much needed simple and low-cost fabrication process. The high atomic number (Z) of their constituents and significantly higher carrier mobility also make perovskite semiconductors suitable for the detection of ionizing radiation. By taking advantage of that, the direct detection of soft-X-ray-induced photocurrent is demonstrated in both rigid and flexible detectors based on all-inorganic halide perovskite quantum dots (QDs) synthesized via a solution process. Utilizing a synchrotron soft-X-ray beamline, high sensitivities of up to 1450 µC Gy air −1 cm −2 are achieved under an X-ray dose rate of 0.0172 mGy air s −1 with only 0.1 V bias voltage, which is about 70-fold more sensitive than conventional α-Se devices. Furthermore, the perovskite film is printed homogeneously on various substrates by the inexpensive inkjet printing method to demonstrate large-scale fabrication of arrays of multichannel detectors. These results suggest that the perovskite QDs are ideal candidates for the detection of soft X-rays and for large-area flat or flexible panels with tremendous application potential in multidimensional and different architectures imaging technologies.

CsPbI nanocrystals suffer from a facile cubic perovskite to orthorhombic phase transformation, which deteriorates their appealing optoelectronic properties. Here, we report a new colloidal synthesis that replaces the conventionally used oleic acid with an alkyl phosphinic acid to grow high-quality, phase-stable cubic perovskite CsPbI nanocrystals.

Metal halide perovskite semiconductor nanocrystals have emerged as a lucrative class of materials for many optoelectronic applications. By leveraging the synthetic toolboxes developed from decades of research into more traditional semiconductor nanocrystals, remarkable progress has been made across these materials in terms of their structural, compositional, and optoelectronic control. Here, we review this progress in terms of their underlying formation stages, synthetic approaches, and postsynthetic treatment steps. This assessment highlights the rapidly maturing nature of the perovskite nanocrystal field, particularly with regard to their lead-based derivatives. It further demonstrates that significant challenges remain around precisely controlling their nucleation and growth processes. In going forward, a deeper understanding of the role of precursors and ligands will significantly bolster the versatility in the size, shape, composition, and functional properties of these exciting materials.

Human mobility prediction is of great importance in a wide range of modern applications in different fields such as personalized recommendation systems, the fifth-generation (5G) mobile communication systems, and so on. Generally, the prediction goal varies from different application scenarios. For the applications of 5G network including resource allocation and mobility management, it is essential to predict the positions of mobile users in the near future from dozens of seconds to a few minutes so as to make preparation in advance, which is actually a trajectory prediction problem. In this paper, with the particular focus on multi-user multi-step trajectory prediction, we first design a basic deep learning-based prediction framework, where the long short-term memory (LSTM) network is directly applied as the most critical component to learn user-specific mobility pattern from the user's historical trajectories and predict his/her movement trends in the future. Motivated by the related findings after testifying and analyzing this basic framework on a model-based dataset, we extend it to a region-oriented prediction scheme and propose a multi-user multi-step trajectory prediction framework by further incorporating the sequence-to-sequence (Seq2Seq) learning. The experimental results on a realistic dataset demonstrate that the proposed framework has significant improvements on generalization ability and reduces error-accumulation effect for multi-step prediction.