The crystal structure of a Se-rich antimonpearceite has been solved and refined by means of X-ray diffraction data collected at temperatures above (room temperature) and below (120 K) an ionic conductivity-induced phase transition. Both structure arrangements consist of the stacking of [(Ag,Cu)(6)(Sb,As)(2)(S,Se)(7)](2-) A (A') and [Ag(9)Cu(S,Se)(2)Se(2)](2+) B (B') module layers in which Sb forms isolated SbS(3) pyramids typically occurring in sulfosalts; copper links two S atoms in a linear coordination, and silver occupies sites with coordination ranging from quasi-linear to almost tetrahedral. In the ionic-conducting form, at room temperature, the silver d(10) ions are found in the B (B') module layer along two-dimensional diffusion paths and their electron densities described by means of a combination of a Gram-Charlier development of the atomic displacement factors and a split-atom model. The structure resembles that of pearceite, except for the presence of both specific (Se) and mixed (S, Se) sites. In the low-temperature ;ordered' phase at 120 K the silver d(10) ions of the B (B') module layer are located in well defined sites with mixed S-Se coordination ranging from quasi-linear to almost tetrahedral. The structure is then similar to that of 222-pearceite but with major differences, specifically its cell metric, symmetry and local arrangement in the B (B') module layer.
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http://dx.doi.org/10.1107/S0108768106022798 | DOI Listing |
Animals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of experience into tasks and associate them with internal task abstracts.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
January 2025
Southern University of Science and Technology, Department of Mechanical and Energy Engineering, 1088 Xueyuan Blvd, Nanshan District, 518055, Shenzhen, CHINA.
The escape of organic cations over time from defective perovskite interface leads to non-stoichiometric terminals, significantly affecting the stability of perovskite solar cells (PSCs). How to stabilize the interface composition under environmental stress remains a grand challenge. To address this issue, we utilize thiol-functionalized particles as a "seed" and conduct in situ polymerization of 2,2,3,4,4,4-hexafluorobutyl methacrylate (HFMA) as a "root" at the bottom of the perovskite layer.
View Article and Find Full Text PDFMater Horiz
January 2025
State Key Laboratory of Organic-Inorganic Composites, College of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Solar energy sources have garnered significant attention as a renewable energy option. Despite this, the practical power conversion efficiency (PCE) of widely used silicon-based solar cells remains low due to inefficient light utilization. In this study, carbon dots (APCDs) were prepared a hydrothermal method using ammonium polyphosphate and -phenylenediamine, then incorporated into a silicone-acrylic emulsion (CAS) to create a luminescent down-shifting (LDS) layer for solar cells.
View Article and Find Full Text PDFJ Glaucoma
January 2025
Department of Ophthalmology, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), Universidad Complutense. Madrid, Spain.
Prcis: The discriminant function of glaucoma, obtained by the Laguna ONhE colorimetric program, significantly correlates with the BMO-MRW. Furthermore, the diagnostic capacity was inferior to other structural tests in POAG patients.
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Sci Rep
January 2025
College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China.
To address the problems of complex cloud features in satellite cloud maps, inaccurate typhoon localization, and poor target detection accuracy, this paper proposes a new typhoon localization algorithm, named TGE-YOLO. It is based on the YOLOv8n model with excellent high-low feature fusion capability and innovatively achieves the organic combination of feature fusion, computational efficiency, and localization accuracy. Firstly, the TFAM_Concat module is creatively designed in the neck network, which comprehensively utilizes the detailed information of shallow features and the semantic information of deeper features, enhancing the fusion ability of features at each layer.
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