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Machine learning assisted screening of binary alloys for metal-based anode materials

Journal of Energy Chemistry 2025年第5期104卷 62-68页

作者： Xingyue Shi Linming Zhou Yuhui Huang Chaohui Wu Yongjun Wu Juan Li Zijian Hong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and Engineering Zhejiang University Nanhu Brain-Computer Interface Institute Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Research Institute of Zhejiang University-Taizhou Inner Mongolia Electric Power Economic and Technological Research Institute Institute of Fundamental and Transdisciplinary Research Zhejiang University College of Materials Science and Engineering Zhejiang University of Technology School of Engineering Hangzhou City University

Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***, experimental investigations of metal alloys can be time-consuming and expensive, often requiring extensive experimental design and effort. In this study, we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN) to screen alloy anode materials for seven battery systems, including lithium(Li), sodium(Na), potassium(K), zinc(Zn), magnesium(Mg), calcium(Ca), and aluminum(Al). We utilized data with tens of thousands of alloy materials from the Materials Project(MP) and Automatic FLOW for Materials Discovery(AFLOW) databases. Without any experimental voltage input, we identified over 30 alloy systems that have been experimentally validated with good precision. Additionally, we predicted over 100 alloy anodes with low potential and high specific capacity. We hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.

关键词： Alloy anodes CGCNN Machine learning Metal-based batteries

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Unlocking of Schottky Barrier Near the Junction of MoS2 Heterostructure Under Electrochemical Potential

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Energy & Environmental Materials 2025年第1期8卷 327-333页

作者： Kubra Aydin Mansu Kim Hyunho Seok Chulwoo Bae Jinhyoung Lee Muyoung Kim Jonghwan Park Joseph T.Hupp Dongmok Whang Hyeong-U Kim Taesung Kim SKKU Advanced Institute of Nanotechnology (SAINT) Sungkyunkwan University Department of Nano Science and Technology Sungkyunkwan University Department of Chemistry Northwestern University School of Advanced Material Science and Engineering Sungkyunkwan University School of Mechanical Engineering Sungkyunkwan University Center for Quantum Nanoscience Institute for Basic Science (IBS) Semiconductor Manufacturing Research Center Korea Institute of Machinery and Materials (KIMM) Nano-Mechatronics University of Science & Technology (UST) Department of Nano Engineering Sungkyunkwan University

The exploration of heterostructures composed of two-dimensional(2D)transition metal dichalcogenide(TMDc) materials has garnered significant research attention due to the distinctive properties of each individual compo... 详细信息

关键词： hydrogen evolution reaction (HER) molybdenum disulfide (MoS2) plasma-enhanced chemical vapor deposition (PECVD) Schottky barrier van der Waals (vdW) heterostructure

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Guidelines for dopant selection in Cu catalysts to promote the electrochemical CO2 reduction reaction for ethylene production

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Journal of Energy Chemistry 2025年第4期103卷 674-681页

作者： Dogyeong Kim Seohyeon Ka Man Ho Han Woong Kim Seung-Ho Yu Jae-Young Choi Keun Hwa Chae Hyung-Suk Oh Woong Hee Lee Clean Energy Research Center Korea Institute of Science and Technology (KIST) Department of Chemical and Biological Engineering Korea University Department of Materials Science and Engineering Korea University School of Advanced Materials Science and Engineering Sungkyunkwan University KIST-SKKU Carbon-Neutral Research Center Sungkyunkwan University Analysis &Data Center Korea Institute of Science and Technology Division of Energy and Environmental Technology KIST School Korea University of Science and Technology (UST)

To develop an efficient electrochemical CO2reduction reaction（CO2RR） for the production of C2chemicals,improvements in the Cu catalyst are *** is widely used for catalyst enhancement;however,only a few elements have been *** study proposes guidelines for the selection of Cu catalyst dopants to promote ethylene *** was hypothesized that the dopant chemical state highly influences the CO2RR catalytic *** the case of dopants possessing a standard reduction potential within the CO2RR potential region（e.g.,Mn and Ni）,low Faradaic efficiency（FE） toward ethylene production was obtained owing to the presence of a metallic dopant（10.7% for Ni dopant）.In contrast,a low standard reduction potential led to a stable high oxidation state for the dopant,yielding abundant Cuδ+species with modified electronic structures and enhancing the CO2RR catalytic activity for ethylene production（42.1% for Hf dopant）.We expected that a dopant with a low standard reduction potential is difficult to reduce,which leads to a stable Cu-O-X bond and induces a stable Cuδ+*** study provides insights into how to select dopant for various catalyst to enhance CO2RR catalytic activity.

关键词： CO 2 reduction reaction Catalytic selectivity Dopant Cu species Ethylene production

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Methanolysis/Hydrolysis of Polyethylene Terephthalate Over Cu-Mg-Al Oxides Catalysts Promoted by Trace Water

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石油学报(石油加工) 2025年第3期41卷 707-719页

作者： NIU Tiantian LIANG Changhai CHEN Xiao Laboratory of Advanced Materials and Catalytic Engineering School of Chemical EngineeringDalian University of TechnologyDalian 116024China

With the aim to effectively depolymerize polyethylene terephthalate(PET)under mild reaction conditions,PET methanolysis and dimethyl terephthalate(DMT)hydrolysis are integrated in a catalyst ***,methanolysis of PET to DMT is achieved over Cu-Mg-Al oxide ***,terephthalic acid(TPA)is prepared by DMT *** is found that hydrolysis of DMT to TPA can be promoted by introducing trace amount of water in this catalyst ***-MgO-4.5Al_2O_(3)catalyst demonstrates the excellent catalytic performance for the depolymerization of PET with high conversion rate and TPA yield(100%and 99.5%,respectively)after reaction at 160℃for 6 h,which provides a new idea for the depolymerization of PET.

关键词： polyethylene terephthalate terephthalic acid methanolysis hydrolysis Cu-Mg-Al oxide

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RSAPower: Random Style Augmentation Driven Structure Perception Network for Generalized Retinal OCT Fluid Segmentation

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2353-2367页

作者： Lu, Chenggang Guo, Zhitao Zhang, Dan Mou, Lei Yuan, Jinli Ma, Shaodong Chen, Da Zhao, Yitian Xia, Kewen Zhang, Jiong Hebei University of Technology School of Artificial Intelligence School of Electronic and Information Engineering Tianjin300401 China Chinese Academy of Sciences Laboratory of Advanced Theranostic Materials and Technology Ningbo Institute of Materials Technology and Engineering Ningbo China Innovation and Research Institute of Hebei University of Technology in Shijiazhuang Shijiazhuang China Ningbo University of Technology School of Cyber Science and Engineering Ningbo China Ningbo Cixi Institute of Biomedical Engineering China Qilu University of Technology Jinan China

Optical Coherence Tomography (OCT) imaging is extensively utilized for non-invasive observation of pathological conditions, such as retinal fluid-associated diseases. Accurate fluid segmentation in OCT images is therefore critical for quantifying disease severity and aiding clinical decision-making. However, achieving precise segmentation remains challenging due to pathological variations in shape and size, uncertain boundaries, and low contrast of fluid. Most importantly, variability in OCT image styles across different vendors and centers significantly affects fluid segmentation, leading to poor generalization to unseen domains. To address this, we propose a novel method, RSAPower, to enhance the generalization ability of fluid perception networks via style augmentation for retinal fluid segmentation. Specifically, RSAPower comprises a plug-and-play random style transform augmentation (RSTAug) module and a novel fluid perception network (FLPNet) for end-to-end training. The RSTAug module generates new random-style data from the source domain, preserving realistic pathological and structural features. The FLPNet benefits from a novel hybrid structure attention (HSA) module to perceive fluid's spatial features and long-range dependence. Furthermore, FLPNet adapts to the diverse augmented data through a saliency-guided multi-scale attention (SGMA) block, boosting its segmentation performance. We validate RSAPower against various state-of-the-art methods using two publicly available datasets, Retouch and Kermany. Experimental results demonstrate the proposed method's superior generalization ability and effectiveness in fluid segmentation. © 1982-2012 IEEE.

关键词： Optical coherence tomography

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High entropy nanomaterials for zero-emission energy systems: advanced structural design, catalytic performance and functional mechanisms

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Journal of Energy Chemistry 2025年 107卷 512-532页

作者： Zhai, Zibo Wang, Yan-Jie Liu, Dan Wang, Biao Fang, Baizeng New Energy and Advanced Functional Materials Group Research Institute of Interdisciplinary Science & School of Materials Science and Engineering Dongguan University of Technology Guangdong Dongguan523808 China Innovation Catalysis Team School of Chemical Engineering and Energy Technology Dongguan University of Technology Dongguan523808 China

High entropy materials (HEMs) are the promising electrocatalysts for anion exchange membrane electrolyser (AEMs) and proton exchange membrane fuel cells (PEMFCs) due to the intriguing cocktail effect, wide design space, tailorable electronic structure, and entropy stabilization effect. The precise fabrication of HEMs with functional nanostructures provides a crucial avenue to optimize the adsorption strength and catalytic activity for electrocatalysis. This review comprehensively summarizes the development of HEMs, focusing on the principles and strategies of structural design, and the catalytic mechanism towards hydrogen evolution reaction, oxygen evolution reaction and oxygen reduction reaction for the development of high-performance electrocatalysts. The complexity inherent in the interactions between different elements, the changes in the d-band center and the Gibbs free energies during the catalytic progress, as well as the coordination environment of the active sites associated with the unique crystal structure to improve the catalytic performance are discussed. We also provide a perspective on the challenges and future development direction of HEMs in electrocatalysis. This review will contribute to the design and development of HEMs-based catalysts for the next generation of electrochemical applications. © 2025 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences

关键词： Electrolytic reduction

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MSTFormer: multi-granularity spatial-temporal transformers for 3D human pose estimation

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Journal of King Saud University - Computer and Information Sciences 2025年第3期37卷 1-19页

作者： Lin, Hao Xu, Sheng Su, Chengyue School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangdong 510006 China School of Advanced Manufacturing Guangdong University of Technology Guangdong 515200 China

The 2D-to-3D lifting approach based on multi-granularity methods effectively captures spatial-temporal features at various scales. Existing multi-granularity methods primarily focus on extracting joint features through graph-based approaches, which abstract information at different levels. However, these methods often overlook the structured information inherent in skeleton sequences, such as global connectivity, continuous motion trajectories, and temporal context relationships. To address these limitations, we propose a novel method, Multi-granularity Spatial-Temporal Transformers (MSTFormer), for spatial-temporal feature extraction and fusion that leverages the structured information in skeleton sequences. First, the Multi-granularity Spatial Transformer Module constructs hierarchical feature representations of joints, bones, and limbs using Spatial-Temporal Pooling. The Multi-level Spatial Transformer Encoder then extracts the spatial structured information of the skeleton. Next, the Multi-granularity Temporal Transformer Module utilizes the Attention-Enhanced Temporal Transformer Encoder to model the temporal context relationships of the multi-granularity spatial features. Finally, the Multi-granularity Feature Fusion Module integrates these spatial-temporal features, generating accurate 3D pose representations. We introduce a new Multi-granularity Loss Function to align and balance the multi-granularity representations across different levels. Experimental results demonstrate that MSTFormer outperforms state-of-the-art methods on the Human3.6M and HumanEva-I datasets, achieving superior performance with fewer parameters. © The Author(s) 2025.

关键词： 3D human pose estimation Multi-granularity method Skeleton structured information

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An algorithm for indoor positioning based on LightGBM + ExtraTrees chaotic weighted ensemble: evaluation and comparison

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Journal of Supercomputing 2025年第7期81卷 1-31页

作者： Zhang, Lieping Chen, Yao Gao, Xiaolin Zheng, Xinpeng Zhang, Cui Guangxi Key Laboratory of Special Engineering Equipment and Control Guilin University of Aerospace Technology Guilin China Key Laboratory of Advanced Manufacturing and Automation Technology Guilin University of Technology Guilin China School of Information Engineering Nanning College of Technology Guilin China

To address the issues of insufficient positioning accuracy and poor stability in existing indoor positioning algorithms, this paper proposes an indoor fingerprint positioning algorithm based on LightGBM and ExtraTrees chaotic weighted ensemble. First, the received signal strength indicator (RSSI) signal strengths of wireless fidelity (WiFi) at different locations are collected using a mobile device to construct a fingerprint database. Kalman filtering is then applied to preprocess the fingerprint data, removing outliers and noise to improve the data quality. The preprocessed dataset is subsequently divided into training and testing sets. Light gradient boosting machine (LightGBM) and extremely randomized trees (ExtraTrees) are used for modeling, and the chaos particle swarm optimization (CPSO) algorithm is employed to optimize the key parameters of both LightGBM and ExtraTrees. The optimal parameter configuration is determined based on comprehensive evaluation metrics. Finally, the optimal weight ratio of LightGBM and ExtraTrees models is determined through the CPSO algorithm for weighted fusion. Experimental results demonstrate that the proposed algorithm achieves an average positioning error of 1.1 m, which represents a reduction of 7.5–26.7% in average positioning error compared to LightGBM, ExtraTrees, and the LightGBM + ExtraTrees algorithms. After introducing random noise, the proposed algorithm exhibits the smallest variation in average positioning error, validating its significant advantages in positioning accuracy and anti-interference ability. However, the algorithm in this study is primarily based on static WiFi fingerprint data and has not yet been validated in dynamic environments. Future research should further explore its applicability and robustness in more complex environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Particle swarm optimization (PSO)

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In situ construction of Cu(Ⅰ)-Cu(Ⅱ) pairs for efficient electrocatalytic nitrate reduction reaction to ammonia

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Journal of Energy Chemistry 2025年第1期100卷 106-113页

作者： Muyun Zheng Yuchi Wan Leping Yang Shen Ao Wangyang Fu Zhengjun Zhang Zheng-Hong Huang Tao Ling Feiyu Kang Ruitao Lv State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Institute of New Energy Materials and Engineering School of Materials Science and EngineeringFuzhou UniversityFuzhou 350108FujianChina Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Key Laboratory for Advanced Ceramics and Machining Technology of Ministry of Education Tianjin Key Laboratory of Composite and Functional MaterialsSchool of Materials Science and EngineeringTianjin UniversityTianjin 300072China

Electrocatalytic nitrate reduction reaction (NO_(3)-RR) to ammonia under ambient conditions is expected to be a green process for ammonia synthesis and alleviate water pollution *** report a CuO nanoparticles incorporated on nitrogen-doped porous carbon (CuO@NC) catalyst for NO_(3)-*** of Cu(Ⅱ) is reduced to Cu(Ⅰ) during the NO_(3)-RR process to construct Cu(Ⅰ)-Cu(Ⅱ) pairs,confirmed by in situ X-ray photoelectron spectroscopy (XPS) and Raman *** functional theory (DFT) calculations indicated that the formation of Cu(Ⅰ) could provide a reaction path with smaller energy barrier for NO_(3)-RR,while Cu(Ⅱ) effectively suppressed the competition of hydrogen evolution reaction (HER).As a result,CuO@NC catalyst achieved a Faradaic efficiency of 84.2% at -0.49 V versus reversible hydrogen electrode (RHE),and a NH_(3)yield rate of 17.2 mg h^(-1)mg^(-1)*** -0.79 V ***,higher than the HaberBosch process (<3.4 g h^(-1)g^(-1)cat.).This work may open a new avenue for effective NO_(3)-RR by modulating oxidation states.

关键词： Ammonia synthesis Cu oxidation state Electrochemistry Nitrate reduction In situ XPS

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Real-time traffic enhancement scheduling for train communication networks based on TSN

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Journal of Traffic and Transportation engineering(English Edition) 2025年第1期12卷 34-51页

作者： Deqiang He Zeqian Chen Daliang Sun Zhenzhen Jin Yanjun Chen Rui Ma Chen Liang Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing TechnologySchool of Mechanical EngineeringGuangxi University

With the increasing data volume of train on-board system, real-time performance has become the most critical factor to ensure the safety of train operation. Considering that standard Ethernet cannot meet the real-time requirement of existing train communication network(TCN), the time-sensitive network(TSN) technology for TCN is introduced. To solve the time-delay problem, an adaptive switch queue selection mechanism for traffic scheduling is proposed. Firstly, the topology model of TCN based on TSN and the traffic model are described. Then, the K shortest path routing algorithm based on load balancing provides the optimal routing for the scheduling process. Finally, the adaptive switch queue selection mechanism is introduced to solve the aggregation flow conflict problem effectively, queue resources are properly allocated, and the gate control list(GCL) of each frame in the queue is obtained. Experimental results show that compared with the traditional constraint model, the schedulability of the model with an adaptive switch queue selection mechanism increases by 33.0%, and the maximum end-to-end delay and network jitter decrease by 19.1% and 18.6% on average respectively. It can provide theoretical support and application reference for the real-time performance optimization of TCN based on TSN.

关键词： Train communication network Time-sensitive network Adaptive switch queue selection mechanism Gate control list Real-time

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