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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Application of USB for Communication Verification Device in Theodolite

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IOP Conference Series: Materials Science and engineering 2020年第1期746卷

作者： Xinghong Li Lingling Zhang Yongfeng Lei Department of Automation Engineering The Engineering & Technical College of Chengdu University of Technology Leshan Sichuan China

It is primary that difficult to verify the data for a long time in the data communication system of present theodolite. So take advantage of CY7C68013 to Designing the data communication verification device and composing the program for the test of the data communication verification. It is profitable to realize the data communication of theodolite and the realtime or later verificate data, and making the debugging more convenient. Meantime realize the high speed transmission.

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Chapter 5 Dark Matter and New Physics Beyond the Standard Model with LHAASO

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Chinese Physics C 2022年第3期46卷 126-138页

作者： Xiao-Jun Bi Andrea Addazi Konstantin Belotsky Vitaly Beylin Marco Cirelli Arman Esmaili Nicolao Fornengo Qing-Yu Gan Michael Kachekriess Maxim Khlopov Vladimir Korchagin Alexander Korochkin Vladimir Kuksa Antonino Marciano Andrei Neronov Paolo Panci Roman Pasechnick Alexander Sakharov Filippo Sala Giuseppe Di Sciascio Dimiri Semikoz Pasquale Dario Serpico Nikolay Volchanskiy Peng-Fei Yin Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China Center for Theoretical Physics College of Physics Science and TechnologySichuan UniversityChengdu 610065China Laboratori Nazionali di Frascati INFN Frasacati(Rome)ItalyEU National Research Nuclear University MEPHI(Moscow State Engineering Physics Institute) 31 Kashirskoe chausseeMoscow 115409Russia Institute of Physics Southern Federal University Stachki 194Rostov on Don 344090Russia LPTHE CNRS and Sorbonne Universit′e4 Place JussieuF-75252ParisFrance Departamento de Fisica Pontificia Universidade Catolica do Rio de JaneiroRio de Janeiro 22452-970Brazil Dipartimento di Fisica Universita di TorinoVia P.Giuria 110125 TorinoItaly Istituto Nazionale di Fisica Nucleare Sezione di TorinoVia P.Giuria 110125TorinoItaly Institutt for fysikk NTNU7491 TrondheimNorway Universite de Paris CNRSAstroparticule et CosmologieF-75013 ParisFrance Department of Physics and Center for Field Theory and Particle Physics Fudan UniversityShanghai 200433China Astronomy Department University of GeneveCh.d’Ecogia 161290VersolxSwitzerland Dipartimento di Fisica Universita di Pisa and INFNSezione di Pisa Largo Pontecorvo 356127 PisaItaly Department of Astronomy and Theoretical Physics Lund University22100 LundSwedenEU Experimental Physics Department CERNGeneva 23 CH 1211Switzerland Department of Physics New York UniversityNew York NY United States of America INFN sezione Roma Tor Vergata I-00133 RomeItaly LAPTh Univ.Grenoble AlpesUSMBCNRSF-74000 AnnecyFrance Bogoliubov Laboratory of Theoretical Pjysics JINRDubnaRussia

In order to reveal the nature of dark matter,it is crucial to detect its non-gravitational interactions with the standard model *** traditional dark matter searches focused on the so-called weakly interacting massive ***,this paradigm is strongly constrained by the null results of current experiments with high *** there is a renewed interest of searches for heavy dark matter particles above TeV *** Large High Altitude Air Shower Observatory(LHAASO)with large effective area and strong background rejection power is very suitable to investigate the gamma-ray signals induced by dark matter annihilation or decay above TeV *** this document,we review the theoretical motivations and background of heavy dark *** review the prospects of searching for the gamma-ray signals resulted from dark matter in the dwarf spheroidal satellites and Galactic halo for LHAASO,and present the projected *** also review the prospects of searching for the axion-like particles,which are a kind of well motivated light pseudo-scalars,through the LHAASO measurement of the very high energy gamma-ray spectra of astrophysical sources.

关键词： dark matter gamma-ray axion-like particle

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Preparation and Properties of Rice Hull Ash as the catalysis supporter in the fluidized-bed reactor

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IOP Conference Series: Earth and Environmental Science 2021年第1期766卷

作者： Zhaohui Teng Shan Huang Heng Xu Na Li Qulan Zhou College of energy Chengdu University of Tecnoogy Chengdu 610059 China National Key Laboratory of Nuclear Reactor System Design Technology Nuclear Power Institute of China Chengdu 610200 China shenzhen ILNG energy technology CO. LTD Shenzhen 518061 China State Key Laboratory of Multiphase Flow in Power Engineering Xi'an Jiaotong University Xi'an 710049 China

This research focuses on the preparation and properties of rice hull ash(RHA). We prepared RHA from combustion of rice husk in the fluidized-bed reactor. The temperature field of the furnace was steady and bellow 850°C to keep SiO2 amorphous. XRD, BET and SEM were applied in the characterization tests to investigate the effect of flow rate(u), the higher primary-secondary wind ratio(i) and relative height of bed material. The optimal combustion condition for preparing RHA was discussed. RHA obtained from rice hull contains abundant amorphous SiO2 (>90%) and the characterization results of RHA show its potential to be a good catalyst supporter.

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Se substitution and micro-nano-scale porosity enhancing thermoelectric Cu2Te

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Chinese Physics B 2018年第4期27卷 85-89页

作者： Xiaoman Shi Guoyu Wang Ruifeng Wang Xiaoyuan Zhou Jingtao Xu Jun Tang Ran Ang Key Laboratory of Radiation Physics and Technology Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu 610064 China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China University of Chinese Academy of Sciences Beijing 100190 China College of Physics Chongqing University Chongqing 401331 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 China Institute of New Energy and Low-Carbon Technology Sichuan University Chengdu 610065 China

Binary Cu-based chalcogenide thermoelectric materials have attracted a great deal of attention due to their outstanding physical properties and fascinating phase ***,the relatively low figure of merit z T restricts their practical applications in power generation.A general approach to enhancing z T value is to produce nanostructured grains,while one disadvantage of such a method is the expansion of grain size in heating-up ***,we report a prominent improvement of z T in Cu2Te（0.2）Se（0.8）,which is several times larger than that of the *** significant enhancement in thermoelectric performance is attributed to the formation of abundant porosity via cold *** pores with nano-to micrometer size can manipulate phonon transport simultaneously,resulting in an apparent suppression of thermal ***,the Se substitution triggers a rapid promotion of power factor,which compensates for the reduction of electrical properties due to carriers scattering by *** strategy of porosity engineering by phonon scattering can also be highly applicable in enhancing the performances of other thermoelectric systems.

关键词： thermoelectrics Cu2Te porosity thermal conductivity

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A tera-electronvolt afterglow from a narrow jet in an extremely bright gamma-ray burst 221009A

arXiv

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arXiv 2023年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, H.L. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Long, W.J. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Nan, Y.C. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, C. Wang, C.B. Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center Sichuan Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institute for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Zhuhai 519000 China Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Department of Engineering Physics Tsinghua University Beijing1

Some gamma-ray bursts (GRBs) have an afterglow in the tera-electronvolt (TeV) band, but the early onset of this afterglow has not been observed. We report observations with the Large High Altitude Air Shower Observatory of the bright GRB 221009A, which serendipitously occurred within the instrument field of view. More than 64,000 photons (above 0.2 TeV) were detected within the first 3000 seconds. The TeV photon flux began several minutes after the GRB trigger, then rose to a flux peak about 10 seconds later. This was followed by a decay phase, which became more rapid at ∼ 650 s after the peak. The emission can be explained with a relativistic jet model with half-opening angle ∼ 0.8◦, consistent with the core of a structured jet. This interpretation could explain the high isotropic energy of this GRB. Copyright © 2023, The Authors. All rights reserved.

关键词： Photons

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Influence of idler light on the shot-to-shot energy stability of third harmonic generation

Influence of idler light on the shot-to-shot energy stabilit...

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2018 Conference on Optoelectronic Devices and Integration, OEDI 2018, part of International Photonics and Optoelectronics Meeting, POEM 2018

作者： Liu, Jun Ouyang, Xiaoping Wei, Kun Peng, Bodong Tan, Xinjian Xu, Qing Sun, Bin College of Mechatronics Engineering and Automation National University of Defense Technology Changsha410073 China Northwest Institute of Nuclear Technology Xi'an710024 China

ISBN: (纸本)9781943580514

The energy stability of 266nm laser is a key factor for an electron accelerator. We report on the shot to shot energy stability of second and third harmonic generation (THG) by using sub-100-fs idler laser pulses. A maximum energy conversion as high as 25% from the fundamental to the third harmonic generation is achieved by phase and group velocity matching (Type I). The influence of energy and pulse duration of the idler wave has been studied. The THG stability varies from 0.135%~2.94% by changing the idler pulse energy in the experiments. © 2018 Optical Society of America.

关键词： Harmonic generation

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

arXiv

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arXiv 2022年

作者： Abdukerim, Abdusalam Chen, Wei Chen, Xun Chen, Yunhua Cheng, Chen Cui, Xiangyi Fan, Yingjie Fang, Deqing Fu, Changbo Fu, Mengting Geng, Lisheng Giboni, Karl Gu, Linhui Guo, Xuyuan Han, Ke He, Changda Huang, Di Huang, Yan Huang, Yanlin Huang, Zhou Ji, Xiangdong Ju, Yonglin Li, Shuaijie Liu, Huaxuan Liu, Jianglai Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Ni, Kaixiang Ning, Jinhua Ning, Xuyang Ren, Xiangxiang Shang, Changsong Si, Lin Shen, Guofang Tan, Andi Wang, Anqing Wang, Hongwei Wang, Meng Wang, Qiuhong Wang, Siguang Wang, Wei Wang, Xiuli Wang, Zhou Wu, Mengmeng Wu, Shiyong Wu, Weihao Xia, Jingkai Xiao, Mengjiao Xie, Pengwei Yan, Binbin Yang, Jijun Yang, Yong Yu, Chunxu Yuan, Jumin Yuan, Ying Zeng, Xinning Zhang, Dan Zhang, Tao Zhao, Li Zheng, Qibin Zhou, Jifang Zhou, Ning Zhou, Xiaopeng School of Physics and Astronomy Shanghai Jiao Tong University Moe Key Laboratory for Particle Astrophysics and Cosmology Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China School of Physics Sun Yat-Sen University Guangzhou510275 China Tsung-Dao Lee Institute Shanghai200240 China School of Physics Nankai University Tianjin300071 China Institute of Modern Physics Fudan University Shanghai200433 China School of Physics Peking University Beijing100871 China School of Physics Beihang University Beijing102206 China Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing102206 China School of Physics and Microelectronics Zhengzhou University Henan Zhengzhou450001 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Physics University of Maryland College ParkMD20742 United States School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Shandong University Jinan250100 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Center for High Energy Physics Peking University Beijing100871 China

The PandaX-II experiment employed a 580kg liquid xenon detector to search for the interactions between dark matter particles and the target xenon atoms. The accidental coincidences of isolated signals result in a dangerous background which mimic the signature of the dark matter. We performed a detailed study on the accidental coincidence background in PandaX-II, including the possible origin of the isolated signals, the background level and corresponding background suppression method. With a boosted-decision-tree algorithm, the accidental coincidence background is reduced by 70% in the dark matter signal region, thus the sensitivity of dark matter search at PandaX-II is improved. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Discriminative Competitive Representation for Image Classification

Discriminative Competitive Representation for Image Classifi...

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第三十八届中国控制会议

作者： Zhongli Ma Xue Qinlin Jiang Tao Jing She Zuoyong Li School of Control Engineering Chengdu University of Information Technology Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University College of Automation Harbin Engineering University

For collaborative representation based image classification methods, it is very important to represent the image well. In this paper, we propose a novel representation method called discriminative competitive representation(DCR) for image classification. It divides the set of all training samples into two competitive subsets to represent a test sample, and then uses the representation result to perform classification. This method can obtain an approximate representation of a test sample and enable representation components to be discriminative and competitive, which is beneficial to correct classification of the test sample. Specifically, the main idea of the DCR method is that it makes test set and training set have low correlation. One notable advantage of the DCR method is that it exploits a mathematically tractable way to obtain discrimination of classes. The image classification experiments show that the DCR method can obtain a higher accuracy than conventional sparse representation methods.

关键词： Discriminative competitive representation image classification sparse representation low correlation

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Band engineering and precipitation enhance thermoelectric performance of SnTe with Zn-doping

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Chinese Physics B 2018年第4期27卷 75-79页

作者： Zhiyu Chen Ruifeng Wang Guoyu Wang Xiaoyuan Zhou Zhengshang Wang Cong Yin Qing Hu Binqiang Zhou Jun Tang Ran Ang Key Laboratory of Radiation Physics and Technology Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu 610064 China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing 400714 China University of Chinese Academy of Sciences Beijing 100190 China College of Physics Chongqing University Chongqing 401331 China School of Materials Science and Engineering Tongji University Shanghai 201804 China Institute of New Energy and Low-Carbon Technology Sichuan University Chengdu 610065 China

We have systematically studied the thermoelectric properties in Zn-doped Sn ***,band convergence and embedded precipitates arising from Zn doping,can trigger a prominent improvement of thermoelectric *** particular,the value of dimensionless figure of merit z T has increased by 100% and up to ～ 0.5 at 775 K for the optimal sample with 2% Zn *** findings demonstrate that carrier concentration and effective mass play crucial roles on the Seebeck coefficient and power *** obvious deviation from the Pisarenko line（Seebeck coefficient versus carrier concentration） due to Zn-doping reveals the convergence of valence *** the doping concentration exceeds the solubility,precipitates occur and lead to a reduction of lattice thermal *** addition,bipolar conduction is suppressed,indicating an enlargement of band *** Zn-doped Sn Te is shown to be a promising candidate for thermoelectric applications.

关键词： thermoelectric materials chalcogenide band engineering precipitation

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