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Abstract Chalcogenide perovskites, particularly BaZrS₃, hold promise for optoelectronic devices owing to their exceptional light absorption and inherent stability. However, thin films obtained at lower processing temperatures typically result in small grain sizes and inferior transport properties. Here we introduce an approach employing co-sputtering elemental Ba and Zr targets followed by CS₂sulfurization, with a judiciously applied NaF capping layer. NaF acts as a flux agent during sulfurization, leading to marked increase in grain size and improved crystallinity. This process results in near-stoichiometric films with enhanced photoresponse. Terahertz spectroscopy further reveals a carrier mobility more than two orders of magnitude higher than those obtained from field-effect transistor measurements, suggesting that bulk transport is limited by grain boundary scattering. Our results demonstrate flux-assisted sulfurization as an effective strategy to improve the crystallinity of chalcogenide perovskite thin films for optoelectronic applications. Graphical abstract 

Abstract In this study, we demonstrate a novel approach for synthesizing free‐standing and transferable polycrystalline diamond membranes (PCDm) to overcome these constraints, thus enabling a much wider spectrum of applications. Two types of PCDm cantilevers —Top‐Surface‐Up (TSU) and Bottom‐Surface‐Up (BSU) are fabricated, each with two different sets of dimensions: 150 µm (width) × 1200 µm (length) and 300 µm (width) × 2000 µm (length). Their mechanical and electrical properties are systematically investigated. Atomic Force Microscopy (AFM) analysis revealed that TSU‐PCDm has a higher elastic modulus than BSU‐PCDm, attributed to differences in grain size and defect distribution. Despite these differences, all PCDms in our work exhibit consistently high modulus values with minimal mechanical degradation across various cantilever geometries. Bandgap measurements using X‐ray Photoelectron Spectroscopy (XPS) and UV–vis absorption spectroscopy indicated a lower bandgap for TSU‐PCDm due to boron incorporation, while BSU‐PCDm exhibited a higher bandgap due to increased hydrogen content. Electrical characterization showed that the sheet resistance of TSU‐PCDm decreases under strain, whereas BSU‐PCDm maintains stable resistance. These findings unveil the material properties of PCDm and their potential usage for myriad diamond‐based electronic applications. 

The goal of the crime forecasting problem is to predict different types of crimes for each geographical region (like a neighborhood or censor tract) in the near future. Since nearby regions usually have similar socioeconomic characteristics which indicate similar crime patterns, recent state-of-the-art solutions constructed a distance-based region graph and utilized Graph Neural Network (GNN) techniques for crime forecasting, because the GNN techniques could effectively exploit the latent relationships between neighboring region nodes in the graph if the edges reveal high dependency or correlation. However, this distance-based pre-defined graph can not fully capture crime correlation between regions that are far from each other but share similar crime patterns. Hence, to make a more accurate crime prediction, the main challenge is to learn a better graph that reveals the dependencies between regions in crime occurrences and meanwhile captures the temporal patterns from historical crime records. To address these challenges, we propose an end-to-end graph convolutional recurrent network called HAGEN with several novel designs for crime prediction. Specifically, our framework could jointly capture the crime correlation between regions and the temporal crime dynamics by combining an adaptive region graph learning module with the Diffusion Convolution Gated Recurrent Unit (DCGRU). Based on the homophily assumption of GNN (i.e., graph convolution works better where neighboring nodes share the same label), we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution. Empirical experiments and comprehensive analysis on two real-world datasets showcase the effectiveness of HAGEN.