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Study of background from accidental coincidence signals in the PandaX-Ⅱexperiment

Chinese Physics C 2022年第10期46卷 11-24页

作者： Abdusalam Abdukerim Wei Chen Xun Chen Yunhua Chen Chen Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Di Huang Yan Huang Yanlin Huang Zhou Huang Xiangdong Ji Yonglin Ju Shuaijie Li Huaxuan Liu Jianglai Liu Wenbo Ma Yugang Ma Yajun Mao Yue Meng Kaixiang Ni Jinhua Ning Xuyang Ning Xiangxiang Ren Changsong Shang Lin Si Guofang Shen Andi Tan Anqing Wang Hongwei Wang Meng Wang Qiuhong Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Mengmeng Wu Shiyong Wu Weihao Wu Jingkai Xia Mengjiao Xiao Pengwei Xie Binbin Yan Jijun Yang Yong Yang Chunxu Yu Jumin Yuan Ying Yuan Xinning Zeng Dan Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou School of Physics and Astronomy Shanghai Jiao Tong UniversityMOE Key Laboratory for Particle Astrophysics and CosmologyShanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213China Yalong River Hydropower Development Company Ltd.288 Shuanglin RoadChengdu 610051China School of Physics Sun Yat-Sen UniversityGuangzhou 510275China Tsung-Dao Lee Institute Shanghai 200240China School of Physics Nankai UniversityTianjin 300071China Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China School of Physics Peking UniversityBeijing 100871China School of Physics Beihang UniversityBeijing 102206China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing102206China School of Physics and Microelectronics Zhengzhou UniversityZhengzhouHenan 450001China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics University of MarylandCollege ParkMaryland 20742USA School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physics and Key Laboratory of Particle Physics and Particle Irradiation(MOE) Shandong UniversityJinan 250100China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Center for High Energy Physics Peking UniversityBeijing 100871China

Ⅰ.INTRODUCTION The direct detection of dark matter particles,especially the weakly interacting massive particles(WIMPs),is being actively carried out by a couple of experiments worldwide[1].In recent years,the PandaX-Ⅱ experiment located in the China Jinping Underground Laboratory(CJPL)[1-3],which uses the technology of dual phase liquid xenon time projection chambers(TPCs),has pushed the limits of the cross section between WIMPs and nucleons to a new level for most of the possible WIMP masses;other experiments of the same type are also being performed[4–10].

关键词： dark matter xenon detector background accidental coincidence machine learning

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Constraining self-interacting dark matter with the full dataset of PandaX-II

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Science China(Physics,Mechanics & Astronomy) 2021年第11期64卷 2-7页

作者： Jijun Yang Abdusalam Abdukerim Wei Chen Xun Chen Yunhua Chen Chen Cheng Xiangyi Cui Yingjie Fan Deqing Fang Changbo Fu Mengting Fu Lisheng Geng Karl Giboni Linhui Gu Xuyuan Guo Ke Han Changda He Shengming He Di Huang Yan Huang Ran Huo Yanlin Huang Zhou Huang Xiangdong Ji Yonglin Ju Shuaijie Li Qing Lin Huaxuan Liu Jianglai Liu Xiaoying Lu Wenbo Ma Yugang Ma Yajun Mao Yue Meng Nasir Shaheed Kaixiang Ni Jinhua Ning Xuyang Ning Xiangxiang Ren Changsong Shang Guofang Shen Lin Si Andi Tan Anqing Wang Hongwei Wang Meng Wang QiuHong Wang Siguang Wang Wei Wang Xiuli Wang Zhou Wang Mengmeng Wu Shiyong Wu Weihao Wu Jingkai Xia Mengjiao Xiao Xiang Xiao Pengwei Xie Binbin Yan Yong Yang Chunxu Yu Hai-Bo Yu Jumin Yuan Ying Yuan Xinning Zeng Dan Zhang Tao Zhang Li Zhao Qibin Zheng Jifang Zhou Ning Zhou Xiaopeng Zhou School of Physics and Astronomy Shanghai Jiao Tong UniversityMOE Key Laboratory for Particle Astrophysics and CosmologyShanghai Key Laboratory for Particle Physics and CosmologyShanghai 200240China Shanghai Jiao Tong University Sichuan Research Institude Chengdu 610213China Yalong River Hydropower Development Company Ltd.Chengdu 610051China School of Physics Sun Yat-sen UniversityGaungzhou 510275China Tsung-Dao Lee Institute Shanghai 200240China School of Physics Nankai UniversityTianjin 300071China Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Institute of Modern PhysicsFudan UniversityShanghai 200433China School of Physics Peking UniversityBeijing 100871China School of Physics Beihang UniversityBeijing 100191China International Research Center for Nuclei and Particles in the Cosmos&Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang UniversityBeijing 100191China Shandong Institute of Advanced Technology Jinan 250103China School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics University of MarylandCollege ParkMaryland 20742USA School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Modern Physics University of Science and Technology of ChinaHefei 230026China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong UniversityJinan 250100China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary ScienceShandong UniversityQingdao 266237China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China Department of Physics and Astronomy University of CaliforniaRiversideCalifornia 92507USA Center of High Energy Physics Peking UniversityBeijing 100871China

Self-interacting dark matter(SIDM)is a leading candidate proposed to solve discrepancies between predictions of the prevailing cold dark matter theory and observations of *** SIDM models predict the existence of a light force carrier that mediates strong dark matter *** the mediator couples to the standard model particles,it could produce characteristic signals in dark matter direct detection *** report searches for signals of SIDM models with a light mediator using the full dataset of the PandaX-II experiment,basing on a total exposure of 132 *** significant excess over background is found,and our likelihood analysis leads to a strong upper limit on the dark matter-nucleon coupling *** further combine the PandaX-II constraints and those from observations of the light element abundances in the early universe,and show that direct detection and cosmological probes can provide complementary constraints on dark matter models with a light mediator.

关键词： dark matter direct detection self-interacting dark matter

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Synthesis of churros-like V2Se9 nanostructures for ultrasensitive electrochemical detection of oxalic acid in urine with portable point-of-care diagnostic devices

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Biosensors and Bioelectronics 2025年 285卷 117589页

作者： Shi, Peizheng Ye, Chen Sun, Zhuang Sun, Kaiqiang Zhao, Ningbin Pourfakhr, Mahan Cai, Tao Nishimura, Kazuhito Jiang, Nan Tsai, Hsu-Sheng Wu, Lidong Wu, Wenqi Yu, Jiancheng Fu, Li Xu, Lixin Chen, Jun Lin, Cheng-Te College of Materials Science and Engineering Zhejiang University of Technology Hangzhou310014 China Qianwan Institute of CNITECH Ningbo315336 China State Key Laboratory of Advanced Marine Materials Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Department of Bioengineering University of California Los AngelesCA90095 United States School of Physics Harbin Institute of Technology Harbin150001 China Key Laboratory of Control of Quality and Safety for Aquatic Products Chinese Academy of Fishery Sciences Beijing100141 China Department of Urology The Second Affiliated Hospital of Guangzhou Medical University Guangdong 510260 China Guangdong Key Laboratory of Urology Guangzhou Medical University Guangdong 511495 China Institute of Mass Spectrometry Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application Ningbo University Ningbo315211 China Zhenhai Institute of Mass Spectrometry Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China College of Materials and Environmental Engineering Hangzhou Dianzi University Hangzhou310018 China

The binding of oxalic acid (OA) with metal ions, particularly calcium ions, is a key factor in kidney stone formation, as it produces insoluble calcium oxalate, which crystallizes and accumulates in the kidneys. Consequently, OA in urine is commonly considered a significant biomarker for clinical diagnosis and early screening of kidney stones. This study reports churros-like V2Se9 nanostructures synthesized using a solvothermal method (high pressure and temperature) for sensitive and selective electrochemical detection of OA in urine. The electrodes made of churros-like V2Se9 hybridized with reduced graphene oxide (rGO) for OA recognition, demonstrated a linear range from 20 nM to 4 mM, with an extremely low detection limit of 9.7 nM. To enable fast, user friendly, and accurate point-of-care testing (POCT) for OA in urine samples, we designed and developed a portable workstation integrated with smartphone for data visualization. This is the first study to utilize the V2Se9 system for highly sensitive biosensor applications, highlighting its potential to advance screening techniques for key indicators of kidney stones by incorporating novel transition metal chalcogenide nanomaterials in POCT devices. © 2025 Elsevier B.V.

关键词： Electrochemical biosensors

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Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation

Semi-Supervised and Self-Supervised Collaborative Learning f...

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IEEE International Symposium on Biomedical Imaging

作者： Yousuf Babiker M. Osman Cheng Li Weijian Huang Nazik Elsayed Leslie Ying Hairong Zheng Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Faculty of Mathematical and Computer Sciences University of Gezira Wad Madani Sudan Department of Biomedical Engineering and Department of Electrical Engineering University at Buffalo The State University of New York New York USA Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China

Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human-level performance on various image segmentation tasks. Nevertheless, manually annotating volumetric MR images for DL model training is labor-exhaustive and time-consuming. In this work, we aim to train a semi-supervised and self-supervised collaborative learning framework for prostate 3D MR image segmentation while using extremely sparse annotations, for which the ground truth annotations are provided for just the central slice of each volumetric MR image. Specifically, semi-supervised learning and self-supervised learning methods are used to generate two independent sets of pseudo labels. These pseudo labels are then fused by the Boolean operation to extract a more confident pseudo label set. The images with either manual or network self-generated labels are then employed to train a segmentation model for target volume extraction. Experimental results on a publicly available prostate MR image dataset demonstrate that, while requiring significantly less annotation effort, our framework generates very encouraging segmentation results. The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.

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CHECKING CONTINUOUS STOCHASTIC LOGIC AGAINST QUANTUM CONTINUOUS-TIME MARKOV CHAINS

arXiv

引用

arXiv 2022年

作者： Mei, Jingyi Xu, Ming Guan, Ji Deng, Yuxin Yu, Nengkun Shanghai Key Lab of Trustworthy Computing East China Normal University China Shanghai Key Lab of Trustworthy Computing & MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences China Department of Computer Science Stony Brook University United States

Verifying quantum systems has attracted a lot of interest in the last decades. In this paper, we study the quantitative model-checking of quantum continuous-time Markov chains (quantum CTMCs). The branching-time properties of quantum CTMCs are specified by continuous stochastic logic (CSL), which is famous for verifying real-time systems, including classical CTMCs. The core of checking the CSL formulas lies in tackling multiphase until formulas. We develop an algebraic method using proper projection, matrix exponentiation, and definite integration to symbolically calculate the probability measures of path formulas. Thus the decidability of CSL is established. To be efficient, numerical methods are incorporated to guarantee that the time complexity is polynomial in the encoding size of the input model and linear in the size of the input formula. A running example of Apollonian networks is further provided to demonstrate our method. © 2022, CC BY.

关键词： Stochastic systems

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Nonequilibrium transport and the fluctuation theorem in the thermodynamic behaviors of nonlinear photonic systems

arXiv

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arXiv 2024年

作者： Liu, Yang Lu, Jincheng Xiong, Zhongfei Wu, Fan O. Christodoulides, Demetrios Chen, Yuntian Jiang, Jian-Hua School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University 1 Shizi Street Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China CREOL College of Optics and Photonics University of Central Florida OrlandoFL32816-2700 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Physics University of Science and Technology of China Hefei230026 China

Nonlinear multimode optical systems have attracted substantial attention due to their rich physical properties. Complex interplay between the nonlinear effects and mode couplings makes it difficult to understand the collective dynamics of photons. Recent studies show that such collective phenomena can be effectively described by a Rayleigh–Jeans thermodynamics theory which is a powerful tool for the study of nonlinear multimode photonic systems. These systems, in turn, offer a compelling platform for investigating fundamental issues in statistical physics, attributed to their tunability and the ability to access negative temperature regimes. However, to date, a theory for the nonequilibrium transport and fluctuations is yet to be established. Here, we employ the full counting statistics theory to study the nonequilibrium transport of particle and energy in nonlinear multimode photonic systems in both positive and negative temperature regimes. Furthermore, we discover that in situations involving two reservoirs of opposite temperatures and chemical potentials, an intriguing phenomenon known as the loop current effect can arise, wherein the current in the positive energy sector runs counter to that in the negative energy sector. In addition, we numerically confirm that the fluctuation theorem remains applicable in optical thermodynamics systems across all regimes, from positive temperature to negative ones. Our findings closely align with numerical simulations based on first-principles nonlinear wave equations. Our work seeks to deepen the understanding of irreversible non-equilibrium processes and statistical fluctuations in nonlinear many-body photonic systems which will enhance our grasp of collective phenomena of photons and foster a fruitful intersection between optics and statistical physics. © 2024, CC BY-NC-ND.

关键词： Temperature

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CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

arXiv

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arXiv 2023年

作者： Guo, Di Li, Sijin Liu, Jun Tu, Zhangren Qiu, Tianyu Xu, Jingjing Feng, Liubin Lin, Donghai Hong, Qing Lin, Meijin Lin, Yanqin Qu, Xiaobo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China The Key Laboratory for Chemical Biology of Fujian Province MOE Key Laboratory of Spectrochemical Analysis & Instrumentation College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China The China Mobile Group Xiamen361005 China

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://***/***. © 2023, CC BY.

关键词： Deep learning

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Efficient Federated Learning for AIoT applications Using Knowledge Distillation

arXiv

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arXiv 2021年

作者： Liu, Tian Ling, Zhiwei Xia, Jun Fu, Xin Yu, Shui Chen, Mingsong MoE Engineering Research Center of Software/Hardware Co-design Technology and Application East China Normal University Shanghai200062 China Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States School of Computer Science University of Technology Sydney Australia

As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which makes it widely used by Artificial Intelligence Internet of Things (AIoT) applications. However, the traditional FL suffers from model inaccuracy, since it trains local models only using hard labels of data while useful information of incorrect predictions with small probabilities is ignored. Although various solutions try to tackle the bottleneck of the traditional FL, most of them introduce significant communication overhead, making the deployment of large-scale AIoT devices a great challenge. To address the above problem, this paper presents a novel Distillation-based Federated Learning (DFL) method that enables efficient and accurate FL for AIoT applications. By using Knowledge Distillation (KD), in each round of FL training, our approach uploads both the soft targets and local model gradients to the cloud server for aggregation, where the aggregation results are then dispatched to AIoT devices for the next round of local training. During the DFL local training, in addition to hard labels, the model predictions approximate soft targets, which can improve model accuracy by leveraging the knowledge of soft targets. To further improve our DFL model performance, we design a dynamic adjustment strategy of loss function weights for tuning the ratio of KD and FL, which can maximize the synergy between soft targets and hard labels. Comprehensive experimental results on well-known benchmarks show that our approach can significantly improve the model accuracy of FL without introducing significant communication overhead. Copyright © 2021, The Authors. All rights reserved.

关键词： Distillation

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Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

Grand Challenge on Respiratory Sound Classification for SPRS...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Zhang, Qing Zhang, Jing Yuan, Jiajun Huang, Huajie Zhang, Yuhang Chen, Changyan Lin, Jilei Zhang, Baoqin Lv, Gaomei Lin, Shuzhu Wang, Na Liu, Xin Tang, Mingyu Wang, Yahua Liu, Lu Ma, Hui Xie, Dan Wu, Lihua Yang, Haibo Yuan, Shuhua Chen, Mengjun Zhang, Bingxue Zhou, Hongyuan Zhao, Jian Li, Yongfu Yin, Yong Zhao, Liebin Wang, Guoxing Lian, Yong Shanghai Jiao Tong University Department of Micro-Nano Electronics MoE Key Lab of Artificial Intelligence China Shanghai Jiao Tong University School of Medicine Pediatric Ai Clinical Application and Research Center Shanghai Children's Medical Center China Shanghai Jiao Tong University School of Medicine Shanghai Children's Medical Center Department of Respiratory Medicine China Shanghai Children's Medical Center School of Medicine Shanghai China Shanghai University School of Computer Engineering and Science China Sanya Maternity and Child Care Hospital China Taicang Affiliated Hospital of Soochow University The First People's Hospital of Taicang Department of Paediatrics China Linyi City People Hospital China Fengcheng Hospital Department of Paediatrics China Linyi City Maternal and Child Health Hospital China Fifth Affiliated Hospital of Harbin Medical University Department of Pediatrics China Fujian Children's Hospital China The First Affiliated Hospital of Henan University of Traditional Chinese Medicine China Shanghai Tuoxiao Intelligent Technology Co. Ltd China

ISBN: (纸本)9798350300260

Globally, respiratory diseases are the leading cause of death, making it essential to develop an automatic respiratory sounds software to speed up diagnosis and reduce physician workload. A recent line of attempts have been proposed to predict accurately, but they have yet been able to provide a satisfactory generalization performance. In this contest, we invited the community to develop more accurate and generalized respiratory sound algorithms. A starter code is provided to standardize the submissions and lower the barrier. New testing set is prepared to evaluate the generalization performance of the submissions. Top 3 teams will present their work at IEEE BioCAS 2023 conference. © 2023 IEEE.

关键词： and machine-learning models data augmentation deep-learning models feature extractions grand challenge Open-source respiratory sound classification

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Intrinsic mechanical behavior of MgAgSb thermoelectric material:An ab initio study

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Journal of Materiomics 2020年第1期6卷 24-32页

作者： Guodong Li Qi An Umut Aydemir Sergey I.Morozov Bo Duan Pengcheng Zhai Qingjie Zhang William A.Goddard Ⅲ Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics School of ScienceWuhan University of TechnologyWuhan430070China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan430070China Department of Chemical and Materials Engineering University of Nevada RenoRenoNV89557USA Department of Chemistry Koc UniversitySariyerIstanbul34450Turkey Boron and Advanced Materials Research Center Koc UniversitySariyerIstanbul34450Turkey Department of Computer Simulation and Nanotechnology South Ural State UniversityChelyabinsk454080Russia Materials and Process Simulation Center California Institute of TechnologyPasadenaCA91125USA

α-MgAgSb based thermoelectric(TE)device attracts much attention for its commercial application because it shows an extremely high conversion efficiency of ~8.5% under a temperature difference of 225 ***,the mechanical behavior of α-MgAgSb is another serious consideration for its engineering ***,we apply density functional theory(DFT)simulations to examine the intrinsic mechanical properties of all three MgAgSb phases,including elastic properties,shear-stress-shear-strain relationships,deformation and failure mechanism under ideal shear and biaxial shear *** find that the ideal shear strength of α-MgAgSb is 3.25 GPa along the most plausible(100)<010>slip *** strength is higher than that of β-MgAgSb(0.80 GPa)and lower than that of γ-MgAgSb(3.43 GPa).The failure of α-MgAgSb arises from the stretching and breakage of MgeSb bond α-MgAgSb under pure shear load,while it arises from the softening of MgeAg bond and the breakage of AgeSb bond under biaxial shear *** suggests that the deformation mechanism changes significantly under different loading conditions.

关键词： MgAgSb thermoelectric materials Density functional theory Ideal strength Deformation mechanism

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