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Stacking faults, as defects of disordered crystallographic planes, are one of the most important slipping mechanisms in the commonly seen lattice, face-centered cubic (FCC). Such defects can initiate twinning which strengthens mechanical properties, e.g. twinning-induced plasticity (TWIP), of high entropy alloys (HEAs) at cryogenic temperatures. In this work, by using density functional theory (DFT), the twinning initiated from stacking faults is discussed with regard to two different solute elements, Al and Mo, in the FeNiCoCr HEAs. Our results show that adding aluminum (Al) has noticeable enhancement of twinnability while molybdenum (Mo) only induces more stacking faults in the FeNiCoCr-based HEAs.
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http://dx.doi.org/10.1038/s41598-019-47223-3 | DOI Listing |
Small
September 2025
Department of Materials Science, Key Laboratory of Automobile Materials, MOE and State Key Laboratory of High Pressure and Superhard Materials, International Center of Future Science, Jilin University, Changchun, 130012, China.
Molybdenum disulfide (MoS) exhibits excellent lubrication capacity rooted in its layered structure, but it suffers significant structural and functional deterioration due to oxidation in ambient environments, limiting its applications. Concerted efforts are focused on enhancing the antioxidation ability of MoS, but challenges remain. This work conceptualizes and demonstrates a contrarian design of MoS-based film via metal incorporation and oxidation based on consideration of key fundamental principles of thermodynamics, chemistry, and physical mechanics.
View Article and Find Full Text PDFInorg Chem
September 2025
Chemistry Department and Center for Material Science and Nanotechnology, University of Oslo, Oslo NO-0315, Norway.
The Jahn-Teller effect significantly affects the CrF octahedra in Cr(II) fluoroperovskites. Here, we report the synthesis, crystal structures, and magnetic properties of RbCrF and CsCrF, thereby completing a comprehensive investigation of the CrF fluoroperovskites. Powder samples are prepared using a wet-chemical method, which allows stabilization of Cr(II).
View Article and Find Full Text PDFSmall
September 2025
Institute of New Energy Materials, School of Materials Science and Engineering, Tianjin University, Tianjin, 300072, China.
Copper (Cu) catalysts with abundant defects are pivotal for converting CO into valuable multi-carbon products. However, the practical application of Cu catalysts is challenged by the thermodynamic instability of the defects, often leading to surface reconstruction during catalytic processes. Here, it is found that particle size and COO-containing intermediates are key factors driving reconstruction, as the defect stability is size-dependent and can be amplified by leveraging the highly reactive intermediates as the initial reactant.
View Article and Find Full Text PDFSensors (Basel)
August 2025
State Key Laboratory of Coal Mine Disaster Prevention and Control, CCTEG, Chongqing Research Institute, Chongqing 400039, China.
Considering the problems of having insufficient fault identification from single information sources in actual industrial environments, and different information sensitivity in multi-information source data, and different sensitivity of artificial feature extraction, which can lead to difficulties of effective fusion of equipment information, insufficient state representation ability, low fault identification accuracy, and poor robustness, a multi-information fusion fault identification network model based on deep ensemble learning is proposed. The network is composed of multiple sub-feature extraction units and feature fusion units. Firstly, the fault feature mapping information of each information source is extracted and stored in different sub-models, and then, the features of each sub-model are fused by the feature fusion unit.
View Article and Find Full Text PDFMaterials (Basel)
August 2025
National Engineering Research Center of Highway Maintenance Technology, Changsha University of Science & Technology, Changsha 410114, China.
Fault diagnosis for embedded sensors in asphalt pavement faces significant challenges, including the scarcity of real-world fault data and the difficulty in identifying compound faults, which severely compromises the reliability of monitoring data. To address these issues, this study proposes an intelligent diagnostic framework that integrates a Discrete Wavelet Transform (DWT) with a staged, attention-based Long Short-Term Memory (LSTM) network. First, various fault modes were systematically defined, including short-term (i.
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