High entropy metal chalcogenides are an emergent class of materials that have shown exceptional promise in applications such as energy storage, catalysis, and thermoelectric energy conversion. However, the stability of these materials to factors other than temperature are as yet unknown. Here we set out to assess the stability of the high entropy metal sulfide (MnFeCuAgZnCd)S with high pressure (up to 9 GPa), compared to an enthalpically stabilised AgCuS, and a quasi-stable (MnFeZnCd)S. Compression and pressure-annealing of (MnFeCuAgZnCd)S showed diffusion-controlled time and pressure dependent exsolution of jalpaite (AgCuS) from the bulk. Bulk materials characterisation found minor phase impurities and possible elemental localisations in (MnFeCuAgZnCd)S prior to pressure-annealing. To gain deeper understanding of the material pre- and post-pressure annealing at the nanoscale an advanced technique was used which combined machine learning, unsupervised clustering analysis of STEM-EDX mapping with scanning precession electron diffraction (SPED), which revealed a chemically distinct post-pressure annealed jalpaite exsolved from (MnFeCuAgZnCd)S.
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http://dx.doi.org/10.1038/s42004-025-01463-9 | DOI Listing |
Med Gas Res
June 2025
Beijing Key Laboratory of Micro-Nano Energy and Sensor, Center for High-Entropy Energy and Systems, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, China (Zhao Q, Zhong S, Li L).
Sci Rep
March 2025
School of Civil Engineering and Architecture, Suqian University, Suqian, China.
To improve the computational efficiency of global sensitivity analysis (GSA) for complex structures, this study proposed a new importance analysis method (IE) based on the low deviation sequences and orthogonal polynomials to study the influence of parameters' uncertainty on three structural seismic demands. A comparative investigation utilizing nonlinear time history analysis for these seismic demands was conducted using OpenSEES. The variance-based importance analysis method and the Tornado graphic sensitivity analysis method were employed to validate the accuracy of the proposed approach.
View Article and Find Full Text PDFNat Commun
March 2025
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China.
The recycling of spent lithium-ion batteries can effectively mitigate the environmental and resource challenges arising from the escalating generation of battery waste and the soaring demand for battery metals. The existing mixing-then-separating recycling process is confronted with high entropy-increasing procedures, including crushing and leaching, which result in irreversible entropy production due to the decrease in material orderliness or heavy chemical consumption, thereby hindering its thermodynamic efficiency and economic viability of the entire recycling process. Herein, we propose a galvanic leaching strategy that leverages the self-assembly of LiNiCoMnO particles with their inherent aluminium foil current collectors in spent lithium-ion batteries, creating a primary cell system capable of recovering battery metals without pre-crushing or additional reductants.
View Article and Find Full Text PDFInt J Biol Macromol
March 2025
Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control, School of Light Industry and Food Engineering, Guangxi University, Nanning 530004, China; School of Environment and Natural Resources, Zhejiang University of Science and Technology, 310023, China; Key Laboratory of Recycling and Eco-Treatment of Waste Biomass of Zhejiang Province, Hangzhou, 310023, PR China. Electronic address:
In this work, through sequential demethylation, amination and esterification, a novel multi-chemically modified lignin-based adsorbent (NMCL) was developed to remove lead ions (Pb) from wastewater. These modifications significantly enhanced lignin's reactivity and introduced diverse active sites, thereby improving its adsorption performances. The adsorption studies revealed that NMCL's adsorption followed the Langmuir isotherm model and Pseudo-second-order kinetics, confirming a monolayer chemical adsorption process.
View Article and Find Full Text PDFNeural Netw
February 2025
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
Minimum error entropy with fiducial points (MEEF) has gained significant attention due to its excellent performance in mitigating the adverse effects of non-Gaussian noise in the fields of machine learning and signal processing. However, the original MEEF algorithm suffers from high computational complexity due to the double summation of error samples. The quantized MEEF (QMEEF), proposed by Zheng et al.
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