安永 可再生能源发展指数报告英文版

合集下载

中国大陆被EI检索的期刊

中国大陆被EI检索的期刊

半导体学报(英文版)爆炸与冲击北京航空航天大学学报北京科技大学学报北京理工大学学报北京理工大学学报(英文版)北京邮电大学学报兵工学报材料工程材料科学技术(英文版)材料热处理学报材料研究学报采矿与安全工程学报测绘学报船舶力学催化学报(网络版,英文版)大地构造与成矿学等离子体科学和技术(英文版)地球科学地球物理学报地球学报地学前缘地震地质地震工程与工程振动(英文版)电波科学学报电工技术学报电机与控制学报电力系统自动化电力自动化设备电网技术电子科技大学学报电子学报电子学报(英文版)电子与信息学报东北大学学报(自然科学版)东华大学学报(英文版)东南大学学报(英文版)东南大学学报(自然科学版)发光学报仿生工程学报(英文版)非线性科学与数值模拟通讯(英文版)分子催化粉末冶金材料科学与工程复合材料学报复杂系统与复杂性科学高等学校化学学报高电压技术高分子材料科学与工程高技术通讯(英文版)高校化学工程学报工程力学工程热物理学报功能材料固体火箭技术固体力学学报(英文版)光电子激光光电子快报(英文版)光谱学与光谱分析光学精密工程光学学报光子传感器(英文版,电子科技大学)光子学报硅酸盐学报国防科技大学学报国际农业工程学报(英文版)国际农业与生物工程学报(英文版)国际自动化与计算杂志(英文版)哈尔滨工程大学学报哈尔滨工业大学学报哈尔滨工业大学学报(英文版)含能材料焊接学报航空动力学报航空学报核动力工程红外与毫米波学报红外与激光工程湖南大学学报(自然科学版)华南理工大学学报(自然科学版)华中科技大学学报(自然科学版)化工学报环境科学学报(英文版)环境科学研究机器人机械工程学报吉林大学学报(工学版)计算机辅助设计与图形学学报计算机集成制造系统计算机科学技术学报(英文版)计算机科学前沿(英文版)计算机学报计算机研究与发展建筑材料学报建筑结构学报交通运输工程学报交通运输系统工程与信息金属学报金属学报(英文版)颗粒学报(英文版)控制理论与应用控制理论与应用(英文版)控制与决策矿物冶金与材料学报(英文版)矿业科学技术(英文版)力学进展力学学报力学学报(英文版)林产化学与工业煤炭学报摩擦学学报南京航空航天大学学报(英文版)内燃机工程内燃机学报农业工程学报农业机械学报汽车工程强激光与粒子束桥梁建设清华大学学报(英文版)清华大学学报(自然科学版)燃料化学学报热科学学报(英文版)人工晶体学报软件学报上海交通大学学报上海交通大学学报(英文版)声学学报石油地球物理勘探石油勘探与开发石油物探石油学报石油学报(石油加工)石油与天然气地质水动力学研究与进展(B辑,英文版)水科学进展水科学与水工程(英文版)水利学报四川大学学报(工程科学版)太阳能学报天津大学学报天津大学学报(英文版)天然气地球科学天然气工业天然气化学(英文版)铁道工程学报铁道学报通信学报同济大学学报(自然科学版)土木工程学报推进技术无机材料学报武汉大学学报(信息科学版)武汉理工大学学报(材料科学版,英文版)物理学报西安电子科技大学学报西安交通大学学报西北工业大学学报西南交通大学学报稀土稀土学报(英文版)稀有金属稀有金属(英文版)稀有金属材料与工程系统工程理论与实践系统工程与电子技术系统工程与电子技术(英文版)系统科学与复杂性学报(英文版)系统科学与系统工程学报(英文版)现代食品科技现代隧道技术新型炭材料烟草科技岩石力学与工程学报岩土工程学报岩土力学仪器仪表学报应用基础与工程科学学报应用数学和力学(英文版)宇航学报原子能科学技术长安大学学报(自然科学版)浙江大学学报(A辑应用物理和工程,英文版)浙江大学学报(C 计算机与电子工程,英文版)浙江大学学报(工学版)真空科学与技术学报振动测试与诊断振动工程学报振动与冲击质谱学报智能计算与控制论国际期刊(英文版)中国地球化学学报(英文版)中国电机工程学报中国公路学报中国惯性技术学报中国光学快报(英文版)中国海洋工程(英文版)中国焊接(英文版)中国航空学报(英文版)中国化学工程学报(英文版)中国环境科学中国机械工程学报(英文版)中国机械工程学刊中国激光中国科学(地球科学,英文版)中国科学(化学,英文版)中国科学(技术科学,英文版)中国科学(物理、力学与天文学,英文版)中国科学(信息科学,英文版)中国矿业大学学报中国粮油学报中国石油大学学报(自然科学版)中国食品学报中国铁道科学中国土木水利工程学刊中国物理(B,英文版)中国烟草学报中国邮电高校学报(英文版)中国有色金属学报中国有色金属学会学报(英文版)中国造船中南大学学报(矿冶科技,英文版)中南大学学报(自然科学版)自动化学报Journal of Semiconductors1674-4926 Baozha yu Chongji1001-1455 Beijing Hangkong Hangtian Daxue Xuebao1001-5965 Beijing Keji Daxue Xuebao1001-053X Beijing Ligong Daxue Xuebao1001-0645 Journal of Beijing Institute of Technology1004-0579 Beijing Youdian Daxue Xuebao1007-5321 Binggong Xuebao1000-1093 Cailiao Gongcheng/Ts'ai Liao Kung Ch'eng1001-4381 Journal of Materials Science & Technology1005-0302 Cailiao Rechuli Xuebao1009-6264 Cailiao Yanjiu Xuebao1005-3093 Caikuang yu Anquan Gongcheng Xuebao1673-3363 Cehui Xuebao1001-1595 Chuanbo Lixue1007-7294 Chinese Journal of Catalysis E1872-2067 Dadi Gouzao yu Chengkuangxue1001-1552 Plasma Science & Technology (Bristol, United Kingdom)1009-0630 Diqiu Kexue1000-2383 Diqiu Wuli Xuebao0001-5733 Diqiu Xuebao1006-3021 Dixue Qianyuan1005-2321 Dizhen Dizhi0253-4967 Earthquake Engineering and Engineering Vibration1671-3664 Dianbo Kexue Xuebao1005-0388 Diangong Jishu Xuebao1000-6753 Dianji yu Kongzhi Xuebao1007-449X Dianli Xitong Zidonghua1000-1026 Dianli Zidonghua Shebei1006-6047 Dianwang Jishu1000-3673 Dianzi Keji Daxue Xuebao1001-0548 Dianzi Xuebao0372-2112 Chinese Journal of Electronics1022-4653 Dianzi yu Xinxi Xuebao1009-5896 Dongbei Daxue Xuebao ,Ziran Kexueban1005-3026 Journal of Donghua University1672-5220 Journal of Southeast University1003-7985 Dongnan Daxue Xuebao ,Ziran Kexueban1001-0505 Faguang Xuebao1000-7032 Journal of Bionic Engineering1672-6529 Communications in Nonlinear Science and Numerical Simu1007-5704 Fenzi Cuihua1001-3555 Fenmo Yejin Cailiao Kexue yu Gongcheng1673-0224 Fuhe Cailiao Xuebao1000-3851 Fuza Xiting yu Fuzaxing Kexue1672-3813 Gaodeng Xuexiao Huaxue Xuebao0251-0790 Gaodianya Jishu1003-6520 Gaofenzi Cailiao Kexue yu Gongcheng1000-7555 High Technology Letters1006-6748 Gaoxiao Huaxue Gongcheng Xuebao1003-9015Gongcheng Lixue1000-4750 Gongcheng Rewuli Xuebao0253-231X Gongneng Cailiao1001-9731Guti Huojian Jishu1006-2793Acta Mechanica Solida Sinica0894-9166 Guangdianzi Jiguang1005-0086 Optoelectronics Letters1673-1905 Guangpuxue yu Guangpu Fenxi1000-0593 Guangxue Jingmi Gongcheng1004-924X Guangxue Xuebao0253-2239Photonic Sensors1674-9251Guangzi Xuebao1004-4213 Guisuanyan Xuebao0454-5648Guofang Keji Daxue Xuebao1001-2486 International Agricultural Engineering Journal0858-2114 International Journal of Agricultural and Biological Enginee1934-6344 International Journal of Automation and Computing1476-81861006-7043Harbin Gongcheng Daxue Xuebao或Ha-erh-pin Kung Cheng Ta Hsueh Hsueh Pao Harbin Gongye Daxue Xuebao0367-6234Journal of Harbin Institute of Technology (English Edition)1005-9113Hanneng Cailiao1006-9941Hanjie Xuebao0253-360X Hangkong Dongli Xuebao1000-8055Hangkong Xuebao1000-6893Hedongli Gongcheng0258-0926Hongwai yu Haomibo Xuebao1001-9014Hongwai yu Jiguang Gongcheng1007-2276Hunan Daxue Xuebao,Ziran Kexueban1674-2974Huanan Ligong Daxue Xuebao,Ziran Kexueban1000-565X Huazhong Keji Daxue Xuebao,Ziran Kexueban1671-4512Huagong Xuebao0438-1157Journal of Environmental Sciences (Beijing, China)1001-0742Huanjing Kexue Yanjiu1001-6929Jiqiren1002-0446Jixie Gongcheng Xuebao0577-6686Jilin Daxue Xuebao,Gongxueban1671-5497Jisuanji Fuzhu Sheji yu Tuxingxue Xuebao1003-9775Jisuanji Jicheng Zhizao Xitong1006-5911Journal of Computer Science and Technology1000-90002095-2228Frontiers of Computer Science(旧名Frontiers of Computer Science in China)Jisuanji Xuebao0254-4164Jisuanji Yanjiu yu Fazhan1000-1239Jianzhu Cailiao Xuebao1007-9629Jianzhu Jiegou Xuebao1000-6869Jiaotong Yunshu Gongcheng Xuebao1671-1637Jiaotong Yunshu Xitong Gongcheng yu Xinxi1009-6744Jinshu Xuebao0412-1961Acta Metallurgica Sinica(English Letters)1006-7191 Particuology1674-2001Kongzhi Lilun yu Yingyong1000-8152Journal of Control Theory and Applications1672-6340 Kongzhi yu Juece1001-0920 International Journal of Minerals, Metallurgy and Materials1674-4799 International Journal of Mining Science and Technology2095-2686Lixue Jinzhan1000-0992Lixue Xuebao0459-1879Acta Mechanica Sinica0567-7718 Linchan Huaxue yu Gongye0253-2417 Meitan Xuebao0253-9993 Mocaxue Xuebao1004-0595 Transactions Nanjing University Aeronautics and Astronau1005-1120 Neiranji Gongcheng1000-0925 Neiranji Xuebao1000-0909 Nongye Gongcheng Xuebao1002-6819 Nongye Jixie Xuebao1000-1298 Qiche Gongcheng1000-680X Qiangjiguang yu Lizishu1001-4322 Qiaoliang Jianshe1003-4722 Tsinghua Science and Technology1007-0214 Qinghua Daxue Xuebao,Ziran Kexueban1000-0054 Ranliao Huaxue Xuebao0253-2409 Journal of Thermal Science1003-2169 Rengong Jingti Xuebao1000-985X Ruanjian Xuebao1000-9825 Shanghai Jiaotong Daxue Xuebao1006-2467 Journal of Shanghai Jiaotong University(Special Issue)1007-1172 Shengxue Xuebao0371-0025 Shiyou Diqiu Wuli Kantan1000-7210 Shiyou Kantan yu Kaifa1000-0747 Shiyou Wutan1000-1441 Shiyou Xuebao0253-2697 Shiyou Xuebao,Shiyou Jiagong1001-8719 Shiyou yu Tianranqi Dizhi0253-9985 Journal of Hydrodynamics,Series B1001-6058 Shuikexue Jinzhan1001-6791 Water Science and Engineering1674-2370Shuili Xuebao0559-9350 Sichuan Daxue Xuebao,Gongcheng Kexueban1009-3087 Taiyangneng Xuebao0254-0096 Tianjin Daxue Xuebao0493-2137 Transactions of Tianjin University1006-4982 Tianranqi Diqiu Kexue1672-1926 Tianranqi Gongye1000-09761003-9953 Journal of Natural Gas Chemistry 是旧名,新名 Journal of Energy Chemistry Tiedao Gongcheng Xuebao1006-2106 Tiedao Xuebao1001-8360 Tongxin Xuebao1000-436X Tongji Daxue Xuebao,Ziran Kexueban0253-374X Tumu Gongcheng Xuebao1000-131XTuijin Jishu1001-4055Wuji Cailiao Xuebao1000-324X Wuhan Daxue Xuebao,Xinxi Kexueban1671-8860 Journal of Wuhan University of Technology,Materials Scien1000-2413 Wuli Xuebao1000-3290 Xi'an Dianzi Keji Daxue Xuebao1001-2400 Xi'an Jiaotong Daxue Xuebao0253-987X Xibei Gongye Daxue Xuebao1000-2758 Xinan Jiaotong Daxue Xuebao0258-2724 Xitu1004-0277 Journal of Rare Earths1002-0721 Xiyou Jinshu0258-7076 Rare Metals(Beijing,China)1001-0521 Xiyou Jinshu Cailiao yu Gongcheng1002-185X Xitong Gongcheng Lilun yu Shijian1000-6788 Xitong Gongcheng yu Dianzi Jishu1001-506X Journal of Systems Engineering and Electronics1004-4132 Journal of Systems Science & Complexity1009-6124 Journal of Systems Science and Systems Engineering1004-3756 Xiandai Shipin Keji1673-9078 Xiandai Shuidao Jishu1009-6582 Xinxing Tan Cailiao1007-8827 Yancao Keji1002-0861 Yanshi Lixue yu Gongcheng Xuebao1000-6915 Yantu Gongcheng Xuebao1000-4548 Yantu Lixue1000-7598 Yiqi Yibiao Xuebao0254-3087 Yingyong Jichu yu Gongcheng Kexue Xuebao1005-0930 Applied Mathematics and Mechanics(English Edition)0253-4827 Yuhang Xuebao1000-1328 Yuanzineng Kexue Jishu1000-6931 Chang'an Daxue Xuebao,Ziran Kexueban1671-8879 Journal of Zhejiang University-SCIENCE A Applied Physics 1673-565X1869-1951 Journal of Zhejiang University-SCIENCE C Computers & Ele Zhejiang Daxue Xuebao,Gongxueban1008-973X Zhenkong Kexue yu Jishu Xuebao1672-7126 Zhendong Ceshi yu Zhenduan1004-6801 Zhendong Gongcheng Xuebao1004-4523 Zhendong yu Chongji1000-3835 Zhipu Xuebao1004-29971756-378X International Journal of Intelligent Computing and Cyberne Chinese Journal of Geochemistry1000-9426 Zhongguo Dianji Gongcheng Xuebao0258-8013 Zhongguo Gonglu Xuebao1001-7372 Zhongguo Guanxing Jishu Xuebao1005-6734 Chinese Optics Letters1671-7694 China Ocean Engineering0890-5487 China Welding1004-5341 Chinese Journal of Aeronautics1000-9361 Chinese Journal of Chemical Engineering1004-9541 Zhongguo Huanjing Kexue1000-6923Chinese Journal of Mechanical Engineering1000-9345Zhongguo Jixie Gongcheng Xuekan0257-9731Zhongguo Jiguang0258-7025Science China(Earth Sciences)1674-7313Science China(Chemistry)1674-7291Science China(Technological Sciences)1674-7321Science China(Physics,Mechanics and Astronomy)1674-7348Science China(Information Sciences)1674-733XZhongguo Kuangye Daxue Xuebao1000-1964Zhongguo Liangyou Xuebao1003-0174Zhongguo Shiyou Daxue Xuebao,Ziran Kexueban1673-5005Zhongguo Shipin Xuebao1009-7848Zhongguo Tiedao Kexue1001-4632Zhongguo Tumu Shuili Gongcheng Xuekan1015-5856Chinese Physics B1674-1056Zhongguo Yancao Xuebao 1004-5708The Journal of China University of Posts Telecommum1005-8885Zhongguo Youse Jinshu Xuebao1004-0609Transactions of Nonferrous Metals Society of China 1003-6326Zhongguo Zaochuan1000-48822095-2899Journal of Central South University(Science & Technology of Mining and Metallurgy)Zhongnan Daxue Xuebao,Ziran Kexueban1672-7207Zidonghua Xuebao0254-4156外加工京继续收录川继续收录京继续收录京继续收录京继续收录京继续收录京继续收录京继续收录京继续收录辽继续收录京继续收录辽继续收录苏继续收录京继续收录苏外加工辽新增加*粤外加工皖继续收录鄂新增加*京新增加*京新增加*京新增加*京外加工黑继续收录豫继续收录京继续收录黑继续收录苏继续收录苏继续收录京继续收录川继续收录京继续收录京继续收录京继续收录辽继续收录沪继续收录苏继续收录苏继续收录吉外加工吉外加工京新增加*甘继续收录湘继续收录京新增加*鲁新增加*吉继续收录鄂继续收录川继续收录京继续收录京继续收录京继续收录渝继续收录陕外加工鄂继续收录津外加工津继续收录京继续收录吉继续收录沪外加工川继续收录陕继续收录京继续收录湘外加工京外加工京新外加工*京继续收录黑继续收录黑继续收录黑新增加*川继续收录黑继续收录京继续收录京继续收录川继续收录沪继续收录津继续收录湘继续收录粤继续收录鄂继续收录京外加工京新增加*京继续收录辽继续收录京继续收录吉继续收录京继续收录京外加工京继续收录京继续收录京继续收录京继续收录沪继续收录京继续收录陕新增加*京继续收录辽继续收录辽外加工京外加工粤继续收录辽继续收录京继续收录苏新增加*京继续收录京外加工京新增加*苏继续收录京继续收录甘继续收录苏继续收录沪继续收录津继续收录京继续收录京继续收录京继续收录川新增加*鄂继续收录京继续收录京继续收录晋继续收录京继续收录京继续收录京继续收录沪继续收录沪继续收录京继续收录冀继续收录京新增加*苏继续收录京继续收录京新增加*鄂继续收录沪继续收录苏继续收录苏继续收录京继续收录川继续收录京继续收录津继续收录津新增加*甘新增加*川外加工辽新增加*京继续收录京继续收录京继续收录沪继续收录京继续收录鄂继续收录鄂新增加*京继续收录陕继续收录陕继续收录陕继续收录川新增加*蒙继续收录京新增加*京继续收录京继续收录陕继续收录京继续收录京继续收录京外加工京外加工京新增加*粤新增加*川继续收录晋新增加*豫继续收录鄂继续收录苏继续收录鄂继续收录京继续收录京外加工沪继续收录京继续收录京新增加*陕继续收录浙外加工浙继续收录浙继续收录京继续收录苏继续收录苏继续收录沪新增加*京外加工吉外加工贵继续收录京继续收录陕继续收录津继续收录沪外加工苏继续收录黑外加工京继续收录京外加工台继续收录沪外加工京新增加*京外加工京新外加工*京新外加工*京继续收录苏新增加*京继续收录鲁新增加*京继续收录京外加工台外加工京新增加*京继续收录京继续收录湘继续收录湘新增加*京继续收录湘继续收录湘继续收录京。

人工智能英文参考文献(最新120个)

人工智能英文参考文献(最新120个)

人工智能是一门新兴的具有挑战力的学科。

自人工智能诞生以来,发展迅速,产生了许多分支。

诸如强化学习、模拟环境、智能硬件、机器学习等。

但是,在当前人工智能技术迅猛发展,为人们的生活带来许多便利。

下面是搜索整理的人工智能英文参考文献的分享,供大家借鉴参考。

人工智能英文参考文献一:[1]Lars Egevad,Peter Str?m,Kimmo Kartasalo,Henrik Olsson,Hemamali Samaratunga,Brett Delahunt,Martin Eklund. The utility of artificial intelligence in the assessment of prostate pathology[J]. Histopathology,2020,76(6).[2]Rudy van Belkom. The Impact of Artificial Intelligence on the Activities ofa Futurist[J]. World Futures Review,2020,12(2).[3]Reza Hafezi. How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments[J]. World Futures Review,2020,12(2).[4]Alejandro Díaz-Domínguez. How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence[J]. World Futures Review,2020,12(2).[5]Russell T. Warne,Jared Z. Burton. Beliefs About Human Intelligence in a Sample of Teachers and Nonteachers[J]. Journal for the Education of the Gifted,2020,43(2).[6]Russell Belk,Mariam Humayun,Ahir Gopaldas. Artificial Life[J]. Journal of Macromarketing,2020,40(2).[7]Walter Kehl,Mike Jackson,Alessandro Fergnani. Natural Language Processing and Futures Studies[J]. World Futures Review,2020,12(2).[8]Anne Boysen. Mine the Gap: Augmenting Foresight Methodologies with Data Analytics[J]. World Futures Review,2020,12(2).[9]Marco Bevolo,Filiberto Amati. The Potential Role of AI in Anticipating Futures from a Design Process Perspective: From the Reflexive Description of “Design” to a Discussion of Influences by the Inclusion of AI in the Futures Research Process[J]. World Futures Review,2020,12(2).[10]Lan Xu,Paul Tu,Qian Tang,Dan Seli?teanu. Contract Design for Cloud Logistics (CL) Based on Blockchain Technology (BT)[J]. Complexity,2020,2020.[11]L. Grant,X. Xue,Z. Vajihi,A. Azuelos,S. Rosenthal,D. Hopkins,R. Aroutiunian,B. Unger,A. Guttman,M. Afilalo. LO32: Artificial intelligence to predict disposition to improve flow in the emergency department[J]. CJEM,2020,22(S1).[12]A. Kirubarajan,A. Taher,S. Khan,S. Masood. P071: Artificial intelligence in emergency medicine: A scoping review[J]. CJEM,2020,22(S1).[13]L. Grant,P. Joo,B. Eng,A. Carrington,M. Nemnom,V. Thiruganasambandamoorthy. LO22: Risk-stratification of emergency department syncope by artificial intelligence using machine learning: human, statistics or machine[J]. CJEM,2020,22(S1).[14]Riva Giuseppe,Riva Eleonora. OS for Ind Robots: Manufacturing Robots Get Smarter Thanks to Artificial Intelligence.[J]. Cyberpsychology, behavior and social networking,2020,23(5).[15]Markus M. Obmann,Aurelio Cosentino,Joshy Cyriac,Verena Hofmann,Bram Stieltjes,Daniel T. Boll,Benjamin M. Yeh,Matthias R. Benz. Quantitative enhancement thresholds and machine learning algorithms for the evaluation of renal lesions using single-phase split-filter dual-energy CT[J]. Abdominal Radiology,2020,45(1).[16]Haytham H. Elmousalami,Mahmoud Elaskary. Drilling stuck pipe classification and mitigation in the Gulf of Suez oil fields using artificial intelligence[J]. Journal of Petroleum Exploration and Production Technology,2020,10(10).[17]Rüdiger Schulz-Wendtland,Karin Bock. Bildgebung in der Mammadiagnostik –Ein Ausblick <trans-title xml:lang="en">Imaging in breast diagnostics—an outlook [J]. Der Gyn?kologe,2020,53(6).</trans-title>[18]Nowakowski Piotr,Szwarc Krzysztof,Boryczka Urszula. Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection[J]. Science of the Total Environment,2020,730.[19]Wang Huaizhi,Liu Yangyang,Zhou Bin,Li Canbing,Cao Guangzhong,Voropai Nikolai,Barakhtenko Evgeny. Taxonomy research of artificial intelligence for deterministic solar power forecasting[J]. Energy Conversion and Management,2020,214.[20]Kagemoto Hiroshi. Forecasting a water-surface wave train with artificial intelligence- A case study[J]. Ocean Engineering,2020,207.[21]Tomonori Aoki,Atsuo Yamada,Kazuharu Aoyama,Hiroaki Saito,Gota Fujisawa,Nariaki Odawara,Ryo Kondo,Akiyoshi Tsuboi,Rei Ishibashi,Ayako Nakada,Ryota Niikura,Mitsuhiro Fujishiro,Shiro Oka,Soichiro Ishihara,Tomoki Matsuda,Masato Nakahori,Shinji Tanaka,Kazuhiko Koike,Tomohiro Tada. Clinical usefulness of a deep learning‐based system as the first screening on small‐bowel capsule endoscopy reading[J]. Digestive Endoscopy,2020,32(4).[22]Masashi Fujii,Hajime Isomoto. Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices[J]. Digestive Endoscopy,2020,32(4).[23]Roberto Verganti,Luca Vendraminelli,Marco Iansiti. Innovation and Design in the Age of Artificial Intelligence[J]. Journal of Product Innovation Management,2020,37(3).[24]Yuval Elbaz,David Furman,Maytal Caspary Toroker. Modeling Diffusion in Functional Materials: From Density Functional Theory to Artificial Intelligence[J]. Advanced Functional Materials,2020,30(18).[25]Dinesh Visva Gunasekeran,Tien Yin Wong. Artificial Intelligence in Ophthalmology in 2020: A Technology on the Cusp for Translation and Implementation[J]. Asia-Pacific Journal of Ophthalmology,2020,9(2).[26]Fu-Neng Jiang,Li-Jun Dai,Yong-Ding Wu,Sheng-Bang Yang,Yu-Xiang Liang,Xin Zhang,Cui-Yun Zou,Ren-Qiang He,Xiao-Ming Xu,Wei-De Zhong. The study of multiple diagnosis models of human prostate cancer based on Taylor database by artificial neural networks[J]. Journal of the Chinese Medical Association,2020,83(5).[27]Matheus Calil Faleiros,Marcello Henrique Nogueira-Barbosa,Vitor Faeda Dalto,JoséRaniery Ferreira Júnior,Ariane Priscilla Magalh?es Tenório,Rodrigo Luppino-Assad,Paulo Louzada-Junior,Rangaraj Mandayam Rangayyan,Paulo Mazzoncini de Azevedo-Marques. Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging[J]. Advances in Rheumatology,2020,60(1078).[28]Balamurugan Balakreshnan,Grant Richards,Gaurav Nanda,Huachao Mao,Ragu Athinarayanan,Joseph Zaccaria. PPE Compliance Detection using Artificial Intelligence in Learning Factories[J]. Procedia Manufacturing,2020,45.[29]M. Stévenin,V. Avisse,N. Ducarme,A. de Broca. Qui est responsable si un robot autonome vient à entra?ner un dommage ?[J]. Ethique et Santé,2020.[30]Fatemeh Barzegari Banadkooki,Mohammad Ehteram,Fatemeh Panahi,Saad Sh. Sammen,Faridah Binti Othman,Ahmed EL-Shafie. Estimation of Total Dissolved Solids (TDS) using New Hybrid Machine Learning Models[J]. Journal of Hydrology,2020.[31]Adam J. Schwartz,Henry D. Clarke,Mark J. Spangehl,Joshua S. Bingham,DavidA. Etzioni,Matthew R. Neville. Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons?[J]. The Journal of Arthroplasty,2020.[32]Ivana Nizetic Kosovic,Toni Mastelic,Damir Ivankovic. Using Artificial Intelligence on environmental data from Internet of Things for estimating solar radiation: Comprehensive analysis[J]. Journal of Cleaner Production,2020.[33]Lauren Fried,Andrea Tan,Shirin Bajaj,Tracey N. Liebman,David Polsky,Jennifer A. Stein. Technological advances for the detection of melanoma: Part I. Advances in diagnostic techniques[J]. Journal of the American Academy of Dermatology,2020.[34]Mohammed Amoon,Torki Altameem,Ayman Altameem. Internet of things Sensor Assisted Security and Quality Analysis for Health Care Data Sets Using Artificial Intelligent Based Heuristic Health Management System[J]. Measurement,2020.[35]E. Lotan,C. Tschider,D.K. Sodickson,A. Caplan,M. Bruno,B. Zhang,Yvonne W. Lui. Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future[J]. Journal of the American College of Radiology,2020.[36]Fabien Lareyre,Cédric Adam,Marion Carrier,Juliette Raffort. Artificial Intelligence in Vascular Surgery: moving from Big Data to Smart Data[J]. Annals of Vascular Surgery,2020.[37]Ilesanmi Daniyan,Khumbulani Mpofu,Moses Oyesola,Boitumelo Ramatsetse,Adefemi Adeodu. Artificial intelligence for predictive maintenance in the railcar learning factories[J]. Procedia Manufacturing,2020,45.[38]Janet L. McCauley,Anthony E. Swartz. Reframing Telehealth[J]. Obstetrics and Gynecology Clinics of North America,2020.[39]Jean-Emmanuel Bibault,Lei Xing. Screening for chronic obstructive pulmonary disease with artificial intelligence[J]. The Lancet Digital Health,2020,2(5).[40]Andrea Laghi. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence[J]. The Lancet Digital Health,2020,2(5).人工智能英文参考文献二:[41]K. Orhan,I. S. Bayrakdar,M. Ezhov,A. Kravtsov,T. ?zyürek. Evaluation of artificial intelligence for detecting periapical pathosis on cone‐beam computed tomography scans[J]. International Endodontic Journal,2020,53(5).[42]Avila A M,Mezi? I. Data-driven analysis and forecasting of highway traffic dynamics.[J]. Nature communications,2020,11(1).[43]Neri Emanuele,Miele Vittorio,Coppola Francesca,Grassi Roberto. Use of CT andartificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.[J]. La Radiologia medica,2020.[44]Tau Noam,Stundzia Audrius,Yasufuku Kazuhiro,Hussey Douglas,Metser Ur. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.[J]. AJR. American journal of roentgenology,2020.[45]Coppola Francesca,Faggioni Lorenzo,Regge Daniele,Giovagnoni Andrea,Golfieri Rita,Bibbolino Corrado,Miele Vittorio,Neri Emanuele,Grassi Roberto. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.[J]. La Radiologia medica,2020.[46]?. ? ? ? ? [J]. ,2020,25(4).[47]Savage Rock H,van Assen Marly,Martin Simon S,Sahbaee Pooyan,Griffith Lewis P,Giovagnoli Dante,Sperl Jonathan I,Hopfgartner Christian,K?rgel Rainer,Schoepf U Joseph. Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[48]Brzezicki Maksymilian A,Bridger Nicholas E,Kobeti? Matthew D,Ostrowski Maciej,Grabowski Waldemar,Gill Simran S,Neumann Sandra. Artificial intelligence outperforms human students in conducting neurosurgical audits.[J]. Clinical neurology and neurosurgery,2020,192.[49]Lockhart Mark E,Smith Andrew D. Fatty Liver Disease: Artificial Intelligence Takes on the Challenge.[J]. Radiology,2020,295(2).[50]Wood Edward H,Korot Edward,Storey Philip P,Muscat Stephanie,Williams George A,Drenser Kimberly A. The retina revolution: signaling pathway therapies, genetic therapies, mitochondrial therapies, artificial intelligence.[J]. Current opinion in ophthalmology,2020,31(3).[51]Ho Dean,Quake Stephen R,McCabe Edward R B,Chng Wee Joo,Chow Edward K,Ding Xianting,Gelb Bruce D,Ginsburg Geoffrey S,Hassenstab Jason,Ho Chih-Ming,Mobley William C,Nolan Garry P,Rosen Steven T,Tan Patrick,Yen Yun,Zarrinpar Ali. Enabling Technologies for Personalized and Precision Medicine.[J]. Trends in biotechnology,2020,38(5).[52]Fischer Andreas M,Varga-Szemes Akos,van Assen Marly,Griffith L Parkwood,Sahbaee Pooyan,Sperl Jonathan I,Nance John W,Schoepf U Joseph. Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.[J]. AJR. American journal ofroentgenology,2020,214(5).[53]Moore William,Ko Jane,Gozansky Elliott. Artificial Intelligence Pertaining to Cardiothoracic Imaging and Patient Care: Beyond Image Interpretation.[J]. Journal of thoracic imaging,2020,35(3).[54]Hwang Eui Jin,Park Chang Min. Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.[J]. Korean journal of radiology,2020,21(5).[55]Mateen Bilal A,David Anna L,Denaxas Spiros. Electronic Health Records to Predict Gestational Diabetes Risk.[J]. Trends in pharmacological sciences,2020,41(5).[56]Yao Xiang,Mao Ling,Lv Shunli,Ren Zhenghong,Li Wentao,Ren Ke. CT radiomics features as a diagnostic tool for classifying basal ganglia infarction onset time.[J]. Journal of the neurological sciences,2020,412.[57]van Assen Marly,Banerjee Imon,De Cecco Carlo N. Beyond the Artificial Intelligence Hype: What Lies Behind the Algorithms and What We Can Achieve.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[58]Guzik Tomasz J,Fuster Valentin. Leaders in Cardiovascular Research: Valentin Fuster.[J]. Cardiovascular research,2020,116(6).[59]Fischer Andreas M,Eid Marwen,De Cecco Carlo N,Gulsun Mehmet A,van Assen Marly,Nance John W,Sahbaee Pooyan,De Santis Domenico,Bauer Maximilian J,Jacobs Brian E,Varga-Szemes Akos,Kabakus Ismail M,Sharma Puneet,Jackson Logan J,Schoepf U Joseph. Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography.[J]. Journal of thoracic imaging,2020,35 Suppl 1.[60]Ghosh Adarsh,Kandasamy Devasenathipathy. Interpretable Artificial Intelligence: Why and When.[J]. AJR. American journal of roentgenology,2020,214(5).[61]M.Rosario González-Rodríguez,M.Carmen Díaz-Fernández,Carmen Pacheco Gómez. Facial-expression recognition: An emergent approach to the measurement of tourist satisfaction through emotions[J]. Telematics and Informatics,2020,51.[62]Ru-Xi Ding,Iván Palomares,Xueqing Wang,Guo-Rui Yang,Bingsheng Liu,Yucheng Dong,Enrique Herrera-Viedma,Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective[J]. Information Fusion,2020,59.[63]Abdulrhman H. Al-Jebrni,Brendan Chwyl,Xiao Yu Wang,Alexander Wong,Bechara J. Saab. AI-enabled remote and objective quantification of stress at scale[J]. Biomedical Signal Processing and Control,2020,59.[64]Gillian Thomas,Elizabeth Eisenhauer,Robert G. Bristow,Cai Grau,Coen Hurkmans,Piet Ost,Matthias Guckenberger,Eric Deutsch,Denis Lacombe,Damien C. Weber. The European Organisation for Research and Treatment of Cancer, State of Science in radiation oncology and priorities for clinical trials meeting report[J]. European Journal of Cancer,2020,131.[65]Muhammad Asif. Are QM models aligned with Industry 4.0? A perspective on current practices[J]. Journal of Cleaner Production,2020,258.[66]Siva Teja Kakileti,Himanshu J. Madhu,Geetha Manjunath,Leonard Wee,Andre Dekker,Sudhakar Sampangi. Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics[J]. Artificial Intelligence In Medicine,2020,105.[67]. Evaluation of Payer Budget Impact Associated with the Use of Artificial Intelligence in Vitro Diagnostic, Kidneyintelx, to Modify DKD Progression:[J]. American Journal of Kidney Diseases,2020,75(5).[68]Rohit Nishant,Mike Kennedy,Jacqueline Corbett. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda[J]. International Journal of Information Management,2020,53.[69]Hoang Nguyen,Xuan-Nam Bui. Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach[J]. Applied Soft Computing Journal,2020,92.[70]Benjamin S. Hopkins,Aditya Mazmudar,Conor Driscoll,Mark Svet,Jack Goergen,Max Kelsten,Nathan A. Shlobin,Kartik Kesavabhotla,Zachary A Smith,Nader S Dahdaleh. Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions[J]. Clinical Neurology and Neurosurgery,2020,192.[71]Mei Yang,Runze Zhou,Xiangjun Qiu,Xiangfei Feng,Jian Sun,Qunshan Wang,Qiufen Lu,Pengpai Zhang,Bo Liu,Wei Li,Mu Chen,Yan Zhao,Binfeng Mo,Xin Zhou,Xi Zhang,Yingxue Hua,Jin Guo,Fangfang Bi,Yajun Cao,Feng Ling,Shengming Shi,Yi-Gang Li. Artificial intelligence-assisted analysis on the association between exposure to ambient fine particulate matter and incidence of arrhythmias in outpatients of Shanghai community hospitals[J]. Environment International,2020,139.[72]Fatemehalsadat Madaeni,Rachid Lhissou,Karem Chokmani,Sebastien Raymond,Yves Gauthier. Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review[J]. Cold Regions Science and Technology,2020,174.[73]Steve Chukwuebuka Arum,David Grace,Paul Daniel Mitchell. A review of wireless communication using high-altitude platforms for extended coverage and capacity[J]. Computer Communications,2020,157.[74]Yong-Hong Kuo,Nicholas B. Chan,Janny M.Y. Leung,Helen Meng,Anthony Man-Cho So,Kelvin K.F. Tsoi,Colin A. Graham. An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department[J]. International Journal of Medical Informatics,2020,139.[75]Matteo Terzi,Gian Antonio Susto,Pratik Chaudhari. Directional adversarial training for cost sensitive deep learning classification applications[J]. Engineering Applications of Artificial Intelligence,2020,91.[76]Arman Kilic. Artificial Intelligence and Machine Learning in Cardiovascular Health Care[J]. The Annals of Thoracic Surgery,2020,109(5).[77]Hossein Azarmdel,Ahmad Jahanbakhshi,Seyed Saeid Mohtasebi,Alfredo Rosado Mu?oz. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM)[J]. Postharvest Biology and Technology,2020,166.[78]Wafaa Wardah,Abdollah Dehzangi,Ghazaleh Taherzadeh,Mahmood A. Rashid,M.G.M. Khan,Tatsuhiko Tsunoda,Alok Sharma. Predicting protein-peptide binding sites with a deep convolutional neural network[J]. Journal of Theoretical Biology,2020,496.[79]Francisco F.X. Vasconcelos,Róger M. Sarmento,Pedro P. Rebou?as Filho,Victor Hugo C. de Albuquerque. Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis[J]. Engineering Applications of Artificial Intelligence,2020,91.[80]Masaaki Konishi. Bioethanol production estimated from volatile compositions in hydrolysates of lignocellulosic biomass by deep learning[J]. Journal of Bioscience and Bioengineering,2020,129(6).人工智能英文参考文献三:[81]J. Kwon,K. Kim. Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography[J]. Journal of Heart and Lung Transplantation,2020,39(4).[82]C. Maathuis,W. Pieters,J. van den Berg. Decision support model for effects estimation and proportionality assessment for targeting in cyber operations[J]. Defence Technology,2020.[83]Samer Ellahham. Artificial Intelligence in Diabetes Care[J]. The American Journal of Medicine,2020.[84]Yi-Ting Hsieh,Lee-Ming Chuang,Yi-Der Jiang,Tien-Jyun Chang,Chung-May Yang,Chang-Hao Yang,Li-Wei Chan,Tzu-Yun Kao,Ta-Ching Chen,Hsuan-Chieh Lin,Chin-Han Tsai,Mingke Chen. Application of deep learning image assessment software VeriSee? for diabetic retinopathy screening[J]. Journal of the Formosan Medical Association,2020.[85]Emre ARTUN,Burak KULGA. Selection of candidate wells for re-fracturing in tight gas sand reservoirs using fuzzy inference[J]. Petroleum Exploration and Development Online,2020,47(2).[86]Alberto Arenal,Cristina Armu?a,Claudio Feijoo,Sergio Ramos,Zimu Xu,Ana Moreno. Innovation ecosystems theory revisited: The case of artificial intelligence in China[J]. Telecommunications Policy,2020.[87]T. Som,M. Dwivedi,C. Dubey,A. Sharma. Parametric Studies on Artificial Intelligence Techniques for Battery SOC Management and Optimization of Renewable Power[J]. Procedia Computer Science,2020,167.[88]Bushra Kidwai,Nadesh RK. Design and Development of Diagnostic Chabot for supporting Primary Health Care Systems[J]. Procedia Computer Science,2020,167.[89]Asl? Bozda?,Ye?im Dokuz,?znur Begüm G?k?ek. Spatial prediction of PM 10 concentration using machine learning algorithms in Ankara, Turkey[J]. Environmental Pollution,2020.[90]K.P. Smith,J.E. Kirby. Image analysis and artificial intelligence in infectious disease diagnostics[J]. Clinical Microbiology and Infection,2020.[91]Alklih Mohamad YOUSEF,Ghahfarokhi Payam KAVOUSI,Marwan ALNUAIMI,Yara ALATRACH. Predictive data analytics application for enhanced oil recovery in a mature field in the Middle East[J]. Petroleum Exploration and Development Online,2020,47(2).[92]Omer F. Ahmad,Danail Stoyanov,Laurence B. Lovat. Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[93]Sanne A. Hoogenboom,Ulas Bagci,Michael B. Wallace. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[94]Douglas K. Rex. Can we do resect and discard with artificial intelligence-assisted colon polyp “optical biopsy?”[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[95]Neal Shahidi,Michael J. Bourke. Can artificial intelligence accurately diagnose endoscopically curable gastrointestinal cancers?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[96]Michael Byrne. Artificial intelligence in gastroenterology[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[97]Piet C. de Groen. Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[98]Robin Zachariah,Andrew Ninh,William Karnes. Artificial intelligence for colon polyp detection: Why should we embrace this?[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[99]Alexandra T. Greenhill,Bethany R. Edmunds. A primer of artificial intelligence in medicine[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[100]Tomohiro Tada,Toshiaki Hirasawa,Toshiyuki Yoshio. The role for artificial intelligence in evaluation of upper GI cancer[J]. Techniques and Innovations in Gastrointestinal Endoscopy,2020,22(2).[101]Yahui Jiang,Meng Yang,Shuhao Wang,Xiangchun Li,Yan Sun. Emerging role of deep learning‐based artificial intelligence in tumor pathology[J]. Cancer Communications,2020,40(4).[102]Kristopher D. Knott,Andreas Seraphim,Joao B. Augusto,Hui Xue,Liza Chacko,Nay Aung,Steffen E. Petersen,Jackie A. Cooper,Charlotte Manisty,Anish N. Bhuva,Tushar Kotecha,Christos V. Bourantas,Rhodri H. Davies,Louise A.E. Brown,Sven Plein,Marianna Fontana,Peter Kellman,James C. Moon. The Prognostic Significance of Quantitative Myocardial Perfusion: An Artificial Intelligence–Based Approach Using Perfusion Mapping[J]. Circulation,2020,141(16).[103]Muhammad Asad,Ahmed Moustafa,Takayuki Ito. FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning[J]. Applied Sciences,2020,10(8).[104]Wu Wenzhi,Zhang Yan,Wang Pu,Zhang Li,Wang Guixiang,Lei Guanghui,Xiao Qiang,Cao Xiaochen,Bian Yueran,Xie Simiao,Huang Fei,Luo Na,Zhang Jingyuan,Luo Mingyan. Psychological stress of medical staffs during outbreak of COVID-19 and adjustment strategy.[J]. Journal of medical virology,2020.[105]. Eyenuk Fulfills Contract for Artificial Intelligence Grading of Retinal Images[J]. Telecomworldwire,2020.[106]Kim Tae Woo,Duhachek Adam. Artificial Intelligence and Persuasion: A Construal-Level Account.[J]. Psychological science,2020,31(4).[107]McCall Becky. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread.[J]. The Lancet. Digital health,2020,2(4).[108]Alca?iz Mariano,Chicchi Giglioli Irene A,Sirera Marian,Minissi Eleonora,Abad Luis. [Autism spectrum disorder biomarkers based on biosignals, virtual reality and artificial intelligence].[J]. Medicina,2020,80 Suppl 2.[109]Cong Lei,Feng Wanbing,Yao Zhigang,Zhou Xiaoming,Xiao Wei. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer.[J]. Journal of Cancer,2020,11(12).[110]Wang Fengdan,Gu Xiao,Chen Shi,Liu Yongliang,Shen Qing,Pan Hui,Shi Lei,Jin Zhengyu. Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development.[J]. PeerJ,2020,8.[111]Hu Wenmo,Yang Huayu,Xu Haifeng,Mao Yilei. Radiomics based on artificial intelligence in liver diseases: where we are?[J]. Gastroenterology report,2020,8(2).[112]Batayneh Wafa,Abdulhay Enas,Alothman Mohammad. Prediction of the performance of artificial neural networks in mapping sEMG to finger joint angles via signal pre-investigation techniques.[J]. Heliyon,2020,6(4).[113]Aydin Emrah,Türkmen ?nan Utku,Namli G?zde,?ztürk ?i?dem,Esen Ay?e B,Eray Y Nur,Ero?lu Egemen,Akova Fatih. A novel and simple machine learning algorithm for preoperative diagnosis of acute appendicitis in children.[J]. Pediatric surgery international,2020.[114]Ellahham Samer. Artificial Intelligence in Diabetes Care.[J]. The Americanjournal of medicine,2020.[115]David J. Winkel,Thomas J. Weikert,Hanns-Christian Breit,Guillaume Chabin,Eli Gibson,Tobias J. Heye,Dorin Comaniciu,Daniel T. Boll. Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation[J]. European Journal of Radiology,2020,126.[116]Binjie Fu,Guoshu Wang,Mingyue Wu,Wangjia Li,Yineng Zheng,Zhigang Chu,Fajin Lv. Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study[J]. European Journal of Radiology,2020,126.[117]Georgios N. Kouziokas. A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting[J]. Engineering Applications of Artificial Intelligence,2020,92.[118]Qingsong Ruan,Zilin Wang,Yaping Zhou,Dayong Lv. A new investor sentiment indicator ( ISI ) based on artificial intelligence: A powerful return predictor in China[J]. Economic Modelling,2020,88.[119]Mohamed Abdel-Basset,Weiping Ding,Laila Abdel-Fatah. The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model[J]. Information Fusion,2020,61.[120]Federico Caobelli. Artificial intelligence in medical imaging: Game over for radiologists?[J]. European Journal of Radiology,2020,126.以上就是关于人工智能参考文献的分享,希望对你有所帮助。

专业英语科技论文写作

专业英语科技论文写作

Structure arrangement and writing Structure

Title Authors and Address Abstract Key words Where do I start?


Introduction Materials and methods Results Discussion & Conclusion
NDD----New drug discovery 新药发现 NDC----New drug candidate 候选药物 LC---Leading compound 先导化合物 HTS---High-throughput screening 高通量筛选 NCE---new chemical entities 新颖化学实体 Me-too 模仿类药物 IND---Investigational new drug 申请作为临床研究新药 NDA---New drug application 申请作为注册新药 CRF----Case report form 病例报告表 ICF----Informed consent form 知情同意书 IB-----Investigator’s Brochure 研究者手册 CRO---Contract research organization 合同研究组织 QC-----Quality control QA----- Quality assurance TCM----Traditional Chinese Medicine OTC----Over The Counter 非处方药

刊物的宗旨和范围; 各栏目论文的长度、章节的顺序安排, 等;

采取何种体例格式? 如: 页边距、纸张大小、参 考文献的体例、图表的准备、等; 履行何种形式的同行评议?

Addressing the Impact of Temperature动力电池梯次利用

Addressing the Impact of Temperature动力电池梯次利用
• Controlling or Reducing the Impact of Temperature
o Material Selection o Cell and Module Design o Balance of System Design
• Summary
2
NREL Energy Storage Projects
NATIONAL RENEWABLE ENERGY LABORATORY
5
Large Format Li-Ion Batteries for xEVs
• Lithium-ion battery technology is expected to be the energy storage choice for (xEVs) in the coming years
NREL
Energy Storage Project
Component Testing and Characterization (Including Safety)
Multi‐physics Battery Modeling (including Safety)
Battery Life Prediction and Trade‐off
Golden, Colorado 30TH INTERNATIONAL BATTERY SEMINAR
Ft. Lauderdale, Florida March 11-14, 2013
NREL/PR-5400-58145
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

sustainable horizons 索引

sustainable horizons 索引

sustainable horizons 索引《Sustainable Horizons》是一本关注可持续发展领域的国际学术期刊。

根据您提供的参考信息,以下是期刊中的一些文章和研讨会信息:1. 文章:- Wu J., Wang D., Li L., Zeng Z.(2022). Hydrological Feedback from Projected Earth Greening in the 21st Century. Sustainable Horizons. 1, 100007.- Paoletti E., Sicard P., Hoshika Y., Fares S., Badea O., Pitar D., Popa I., Anav A., Moura B.B., De Marco A.(2022). Towards Long-term Sustainability of Stomatal Ozone Flux Monitoring at Forest Sites. Sustainable Horizons 2, 100018.-Gerges F., Nassif H., Herrington T. & Boufadel M. C.(2022). A GIS-based approach for estimating community transportation exposure and capacity in the context of disaster resilience. Sustainable Horizons, 3, 100030.- Liu X., Liu S., Qiu W., et al.(2022). Cardiotoxicity of PFOA, PFOS, and PFOSA in Early Life Stage Zebrafish: Molecular Changes to Behavioral-level Response. Sustainable Horizons 3, 100027.2. 研讨会:-Sustainable Horizons:城市水处理系列专题研讨会(2023年10月23日,11:00-13:30)。

翻译原文

翻译原文

第五届“学府杯”翻译竞赛原文(英译汉)Smart Energy Solutions: Increase RenewablesA variety of energy sources can be used to generate electricity, including coal, natural gas, nuclear power, and renewable resources like wind, water, plants, and the sun. Our energy choices have direct implications for our health, our environment, and our climate —and right now we are dangerously dependent on coal and other fossil fuels for most of our electricity needs.Coal is abundant and more equally distributed throughout the world than oil and gas. Global recoverable reserves are the largest of all fossil fuels, and most countries have at least some. Moreover, existing and prospective big energy consumers like the US, China and India are self-sufficient in coal and will be for the foreseeable future. Coal has been exploited on a large scale for two centuries, so both the product and the available resources are well known; no substantial new deposits are expected to be discovered. Extrapolating the demand forecast forward, the world will consume 20% of its current reserves by 2030 and 40% by 2050. Hence, if current trends are maintained, coal would still last several hundred years. However, coal is the worst offender, a dirty energy source that produces less than half our electricity but more than 80 percent of all power plant carbon emissions, along with significant and harmful levels of pollutants that degrade our environment and adversely impact our health.There’s a better, cleaner way to meet our energy needs. Renewable energy resources like wind and solar power generate electricity with little or no pollution and global warming emissions. To move the world toward a cleaner, healthier energy future, we need smart policy decisions focused on two primary goals: Increase renewable energy and decrease the use of coal.Support for renewable energy is growing worldwide and at the beginning of 2014, the governments of 144 countries have set renewable energy targets, almost a ten-fold increase since 2005 when only 15 countries had policies in place. Emerging and developing economies are leading the way, with 95 of them having renewable energy policy plans, according to the Renewable 2014 Global Status Report.The world generated 22.1 percent of its electricity from renewable sources in 2013, even though governments in the EU and US reduced their support. Renewable energy provides 6.5 million jobs worldwide, the report said.China, the US, Brazil, Canada, and Germany are the top renewable power countries in 2013, with Spain, Italy, and India making huge strides, according to the report. Denmark leads per capita power generation, and Uruguay, Mauritius, and Costa Rica were among the top investment locations.The most significant growth is in power generation, as global capacity jumped 8percent to more than 1,560 gigawatts (GW) in the last year. Hydropower rose by 4 percent to 1,000 GW, and overall renewables grew 17 percent to more than 560 GW.Over 140 countries have adopted new renewable energy targets, including the United States, which has pledged to increase renewable energy capacity 50 percent by 2020. President Obama has promised to slash America’s dependence on coal, and to switch power plants towards cleaner energy.However, it will be difficult for America to switch off coal consumption, as many states depend on it for their economic livelihood. Many lobbying groups are protesting the new clean energy act, saying it unfairly deprives local industries. Even though coal is a proven dirty energy source consumption has reached a 44-year high. Alternative and clean energy is no longer just a hip trend among environmentalists, but a real energy solution that provides power to homes and offices. The market has matured and become an attractive option for energy security. From the currently available technologies, solar photovoltaics, followed by wind power, concentrated solar power and geothermal, have the highest potentials in the power sector. The use of ocean energy might be significantly higher, but with the current state of development, the technical and economical potential remains unclear.Germany is pushing its economy towards renewables, and produces half of its electricity from solar power, The Local reports. In Denmark wind power meets 33 percent of electricity demand, in Spain 20.9 percent, and in Italy solar power meets 7.8 percent of electricity needs. Many cities across the globe have a goal to transition to 100% renewable energy.Over the last 5 years, solar power has on average expanded 55 percent per year, according to the report. Clean energy is becoming cheaper and more popular with consumers, and investors are snapping up the opportunity. Elon Musk, the entrepreneur who invested early in PayPal and the Tesla electric car, has put money into solar power, and Google is weighing a move into the renewables sector.Current levels of renewables development represent only a tiny fraction of what could be developed. Many regions of the world are rich in renewable resources. Spain and Portugal sit on a bed of geothermal energy, but currently use none of it. Scientists from the Renewable Energy Journal estimate Spain has the potential to produce up to 700 GW, which is five times the current electricity power generation of Spain.第五届“学府杯”翻译竞赛原文(汉译英)“圈子”没那么重要“刚才明明还在说笑,怎么我一走过来就闭嘴了,难道是在说我什么吗?”“看着同事们谈笑风生,我也想融入进去,可就是插不上嘴……”刚从学校踏入社会的职场年轻人,面对陌生的职场环境,往往会出现一段时期的“社交空窗”,总感觉自己被排除在圈子之外,融入不了主流群体之中,这也是年轻人最容易感到苦闷的事情。

2.天津大学管理与经济学部核心期刊目录-V类期刊(C级)

天津大学管理与经济学部核心期刊目录
有关说明:
1.此核心期刊目录只对导师选聘与考核以及在学部上会的博士生有效,对硕士生发表学术论文的要求仍然按照学校的相关文件执行;
2.所有被EI检索的期刊论文,等同于B类论文,但论文内容需与管理或经济相关;
3.所有大学学报社科版须源于985院校且被CSSCI核心库检索;
A
CSSCI
4
管理世界
国务院发展研究中心
A
CSSCI
5
光明日报理论版
光明日报社
A
6
机械工程学报(含英文版)
0577-6686
A
CSCD
7
计算机集成制造系统
1006-5911
A
CSCD
8
计算机学报
0254-4164
A
CSCD
9
控制理论与应用
1000-8152
A
CSCD
10
控制与决策
1001-0920
A
B
74
中国法学
中国法学会
B
CSSCI
75
中国工业经济
中国社会科学院工业经济研究所
B
CSSCI
76
中国管理科学
中国优选法统筹法与经济数学研究会等
B
CSSCI
77
中国会计评论(集刊)
《中国会计评论》理事会
B
(2012-2013)CSSCI集刊
78
中国会计与财务研究(集刊)
香港理工大学,清华大学
B
79
中国机械工程
哈尔滨工业大学学报
0367-6234
CSCD
169
海洋环境科学
1007-6336

电导池

Effects of sulfonated polyether-etherketone (SPEEK)and composite membranes on the proton exchange membrane fuel cell (PEMFC)performanceErce S x engu ¨l a ,Hu ¨lya Erdener a ,R.Gu ¨ltekin Akay a ,Hayrettin Yu ¨cel a ,Nurcan Bac¸b ,_Inc _I Erog ˘lu a ,*a Chemical Engineering Department,Middle East Technical University,06531Ankara,TurkeybChemical Engineering Department,Yeditepe University,34755Istanbul,Turkeya r t i c l e i n f oArticle history:Received 8March 2008Received in revised form 20August 2008Accepted 22August 2008Available online 5November 2008Keywords:PEM fuel cells SPEEKComposite membrane Zeolite betaMembrane electrode assembly (MEA)a b s t r a c tSulfonated polyether-etherketone (SPEEK)has a potential for proton exchange fuel cell applications.However,its conductivity and thermohydrolytic stability should be improved.In this study the proton conductivity was improved by addition of an aluminosilicate,zeolite beta.Moreover,thermohydrolytic stability was improved by blending poly-ether-sulfone (PES).Sulfonated polymers were characterized by posite membranes prepared were characterized by Electrochemical Impedance Spectroscopy (EIS)for their proton conductivity.Degree of sulfonation (DS)values calculated from H-NMR results,and both proton conductivity and thermohydrolytic stability was found to strongly depend on DS.Therefore,DS values were controlled time in the range of 55–75%by controlling the reaction time.Zeolite beta fillers at different SiO 2/Al 2O 3ratios (20,30,40,50)were synthesized and characterized by XRD,EDX,TGA,and SEM.The proton conductivity of plain SPEEK membrane (DS ¼68%)was 0.06S/cm at 60 C and the conductivity of the composite membrane containing of zeolite beta filled SPEEK was found to increase to 0.13S/cm.Among the zeolite Beta/SPEEK composite membranes the best conductivity results were achieved with zeolite beta having a SiO 2/Al 2O 3ratio of 50at 10wt%loading.Single fuel cell tests performed at different operating temperatures indicated that SPES/SPEEK membrane is more stable hydrodynamically and also performed better than pristine SPEEK membranes which swell excessively.Membrane electrode assemblies (MEAs)were prepared by gas diffusion layer (GDL)spraying method.The highest performance of 400mA/cm 2was obtained for SPEEK membrane (DS 56%)at 0.6V for a H 2–O 2/PEMFC working at 1atm and 70 C.At the same conditions Nafion Ò112gave 660mA/cm 2.It was observed that the operating temperature can be increased up to 90 C with polymer blends containing poly-ether-sulfone (PES).ª2008International Association for Hydrogen Energy.Published by Elsevier Ltd.All rightsreserved.1.IntroductionThe increased interest in the potential use of proton exchange membrane fuel cells (PEMFCs)is due to the factthat they can offer high efficiencies with almost zero emis-sion of pollutant gases.Moreover,the quick start-up times and high flexibility to load changes are other advantages.The PEMFC,which uses hydrogen and oxygen (or air)as reactant*Corresponding author .Tel.:þ903122102609;fax:þ903122102600.E-mail address:ieroglu@.tr (_Inc _I Erog˘lu).A v a i l a b l e a t w w w.s c i e n c e d i r e c t.c o mj o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /h e0360-3199/$–see front matter ª2008International Association for Hydrogen Energy.Published by Elsevier Ltd.All rights reserved.doi:10.1016/j.ijhydene.2008.08.066i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 34(2009)4645–4652gases,is particularly attractive due to high power outputs delivered at low operating temperatures(50–80 C)and pres-sures(1–3atm).The electrochemical reaction occurs in the membrane electrode assembly(MEA),which is considered to be the heart of PEMFC[1].When hydrogen gas is fed to the anode side of the cell,it separates into its protons and elec-trons.The protons are conducted through the membrane electrolyte whereas the free electrons produced at the anode move through an external circuit to the cathode.At the cathode side,oxygen gas combines with the electrons and protons.Thefinal products of such a cell are electric power, water,and heat.They are ideally suited for transportation and other appli-cations.PEM fuel cell stacks operating on hydrogen can be 40–50%electrically efficient and80%system efficient if the heat recovery is included.The research and development of PEM fuel cell stacks based on different materials,structures and fabricating methods are going on[2–4].05pThe key component of PEMFC is the membrane which enables proton transfer between anode and cathode.Current applications prefer NafionÒ(DuPont)which belongs to the perflourosulfonic acid(PFSA)family[5].However,there are two significant drawbacks associated with the use of Nafion membrane.First,the cost of NafionÒmembrane is still too high for commercial applications.Second,it is not possible to operate at high temperatures with NafionÒ.High temperature operation is useful for enhanced reaction kinetics and reduced catalyst poisoning by fuel impurities.Therefore,efforts are concentrated on developing alternate membranes that are capable of operating at higher temperatures.Phosphoric acid doped polybenzimidazole is one of the most successful elec-trolyte membranes[6].Other,the most popular candidates are polyaromatic hydrocarbon polymers,especially PEEK,due to its high thermal and mechanical stability,low price and improvable proton conductivity via post-sulfonation. Although,it is improvable,the conductivity of SPEEK membrane is still lower than that of NafionÒ.Its proton conductivity depends on the degree of sulfonation(DS). However,the mechanical properties tend to deteriorate as the DS increases.Highly sulfonated polymers will swell signifi-cantly at high temperature and humidity[7].2.Experimental2.1.Zeolite synthesis and characterizationZeolite beta crystals were synthesized hydrothermally according to the batch composition2.2Na2O:1.0Al2O3:x SiO2: 4.6(TEA)2O:440H2O at various SiO2/Al2O3ratios[8].In hydrothermal synthesis,an alkaline precursor solution was prepared by dissolving sodium aluminate(52.9wt%Al2O3, 45.3wt%Na2O,Riedel de Hae¨n)in deionized water prior to addition of the structure directing agent,tetraethyl ammo-nium hydroxide(TEAOH)solution(20or35wt%in water, Aldrich).The silica precursor solution,mainly composed of colloidal silica(SiO2),(Ludox40wt%suspension in water, Sigma–Aldrich),was added to the alumina precursor solution and gelation was observed.This gel was poured into Teflon-lined steel autoclaves were kept at constant temperature (150 C)under autogenously pressure for a reaction period of 5–15days.The autoclaves were then taken out of the oven, cooled,filtered,and the zeolite product was dried at80 C. Zeolite beta was calcined at550 C,and then converted into more proton conductive Hþform after acid treatment with 95–98wt%H2SO4(Merck).Synthesized zeolite beta samples were characterized by X-Ray Diffraction(XRD)to confirm beta structure,Thermogravimetric Analysis(TGA)for its thermal stability,Energy Dispersive X-Ray Analysis(EDX)to compare theoretical Si/Al ratio with that in synthesized form,and Scanning Electron Microscopy(SEM)for crystal morphology and average particle size.2.2.Polymer sulfonation2.2.1.Sulfonation of PEEK polymerPEEK polymer was obtained as pellets(Polyoxy-1,4-pheney-leneoxy-1,4-pheneyelene carbonyl-1,4-phenylene,Aldrich, Mw¼20,800).PEEK pellets were ground to reduce the disso-lution time of the polymer and dried at100 C in vacuum oven prior to post-sulfonation.In the post-sulfonation reaction,the polymer was dissolved in H2SO4to give a dark,viscous solu-tion then the degree of sulfonation(DS)was controlled by changing the reaction times at a constant temperature(50 C) [9].Reaction was stopped by pouring the polymer solution in icy-water and white polymer strings were obtained.The decanted polymer strings were washed with deionized water and dried in vacuum oven.2.2.2.Sulfonation of PES polymerPES polymer cannot be easily sulfonated as PEEK in H2SO4. Therefore chlorosulfonic acid(CSA)was used in the sulfona-tion reaction.The polymer wasfirst dissolved in H2SO4 (usually1/10w/v)then a predetermined amount of CSA was added drop wise into the solution.Reactions were carried out at around5 C by using ice-cold water around reaction vessel to prevent cross linking and decomposition of the polymer chains which may occur above20 C.At the end of the pre-determined reaction time solution was poured into cold ice-water and the precipitate wasfiltered and washed until excess acid is removed and dried at90 C.2.2.3.Determination of DS by H-NMRThe H-NMR spectra were obtained by using Bruker Biospin NMR spectrometer with a resonance frequency of300MHz. Samples were prepared by dissolving10–20mg polymer in DMSO-d6.The degree of sulfonation,DS,was determined by integration of distinct aromatic signals determined quantita-tively by using H-NMR spectroscopy.In H-NMR the presence of sulfonic acid group’s results in a0.25ppm down-field shift of the hydrogen H E compared to H C,H D in the hydroquinone ring[10].The nomenclature of the aromatic protons for the SPEEK repeat unit is given in Scheme1below.The presence of sulfonic acid groups in the structure causes a distinct signal for protons at E position.Estimates for the H E content which is equal to the sulfonic acid group content can be done according to the intensity of this signal[10].The H-NMR signal for sulfonic acid group is difficult since the proton is labile.The ratio of peak area of distinct H E signalsðA HEÞand integrated areas of the signalsi n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y34(2009)4645–4652 4646corresponding to all the other aromatic hydrogen’s ðA H AA 0BB 0CD Þare expressed as:n 12À2n¼A H EPA H AA 0BB 0CD ð0 n 1Þ(1)DS ¼n Â100%(2)2.3.Membrane castingThe SPEEK polymer was dissolved in n-n,dimethyl-acet-amide (DMAc,Merck)and stirred overnight with magneticstirrer.Then,zeolite H þ-beta was added to the solution at certain quantities.The solution was mixed under ultrasonic mixing overnight and then drop-casted onto petri dishes.The membranes were dried in vacuum oven at 60–120 C for 24h.For blend membranes,proportional amounts of sulfonated PEEK and PES polymers were dissolved in DMAc to give a 10wt%polymer solution.The solution was stirred by magnetic stirrer overnight prior to mixing in ultrasonic water bath to obtain a homogenous solution.After mixing,the homogenous solution was cast onto Petri dishes and dried from 60 C to 120 C in 24h.2.4.Proton conductivity analysisThe proton conductivity of the membranes was measured by AC Electrochemical Impedance (EIS)technique over a frequency range of 1–300kHz with an oscillating voltage using GAMRY PCL40Potentiostat system.All measurements were performed in longitudinal direction,under water vapor atmosphere at 100%relative humidity with a 4probe EIS as a function of temperature.The specimens were prepared as 1Â5cm membrane strips and sandwiched into a Teflon Òconductivity cell with Pt electrodes (Fig.1).The specimen and the electrodes were fixed by nuts and bolts.The conductivity,s ,of samples in longitu-dinal direction was calculated in Siemens per cm from the impedance data by using Eq.(3);s ¼L RWd(3)where;L is the distance between the electrodes,W is the width of the membrane,d is the thickness of the membraneand R is the low intersect of the high-frequency semi-circle on a complex impedance plane with the Re(Z )axis.Proton conductivity measurements were performed in a closed jar with water at the bottom in a temperature controlled bath with mechanical stirrer.The temperature and relative humidity (RH)of the vapor inside the jar were measured with a thermocouple and RH meter.Conductivities were measured several times at each temperature until they were constant.2.5.MEA preparationMEAs were prepared from the membranes cast,which resul-ted in good proton conductivities during electrochemical impedance spectroscopy analyses.Gas diffusion layer (GDL)Spraying technique was applied for the preparation of MEAs [10].In the first step,catalyst ink,which is comprised of 20wt%Pt on Vulcan XC-72catalyst (E-Tek),5wt%Nafion Òsolution (Ion Power Inc),distilled water,and 2-propanol,were prepared and mixed in ultrasonic bath for 2h.In order to clean and increase the proton conductivity of the membranes,they were conditioned by boiling in 0.5M H 2SO 4solution and distilled water at 80 C.In order to coat the GDLs with catalyst layer,the anode and cathode side GDLs were fixed on a paper frame.The catalyst ink was sprayed until the desired catalyst loading (0.4mgPt/cm 2for both anode and cathode sides)was achieved.The catalyst loading was controlled by just weighing the GDLs at different times.After the GDLs were loaded with catalyst,they were kept in oven at 80 C for 1h in order to completely remove the liquid components of catalyst ink.Then,they were weighed again.To complete the MEA,the GDLs were hot pressed to the membrane at 130 C [11].2.6.Performance testsPerformances of fabricated MEAs were measured via the PEMFC test station built at METU Fuel CellTechnologyScheme 1–Aromatic protons of PEEK andSPEEK.Fig.1–Proton conductivity cell.i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 34(2009)4645–46524647Laboratory.A single cell PEMFC (Electrochem FC05-01SP-REF)having 5cm 2active area was used in the experiments.The external load was applied by means of an electronic load (Dynaload ÒRBL488),which can be controlled either manually or by the computer.The current and voltage of the cell were monitored and logged throughout the operation of the cell by fuel cell testing software (FCPower Òv.2.1.102Fideris).The fabricated MEA was placed in the test cell and the bolts were tightened with a torque 1.7Nm on each bolt.The cell temper-ature was adjusted and the temperatures of the humidifiers and gas transfer lines were set 10 C above the cell tempera-ture.After the preset temperatures were achieved,hydrogen and oxygen are supplied to the cell at a rate of 0.1slpm.The cell was operated at 0.5V until it came to steady state.After steady state was achieved,starting from the OCV value,the current–voltage data was logged by changing the load.3.Results3.1.Zeolite beta characterizationThe XRD pattern of zeolite beta that was hydrothermally synthesized at SiO 2/Al 2O 3ratio of 20is given in Fig.2a.The characteristic peaks of zeolite beta were observed at 2q w 7.8 and 2q w 22.4 as stated in literature [12].The morphology of the zeolites was explored with SEM and the average particle size distribution was found to be around 1micron as shown in SEM Picture below (Fig.2b).Another important characteristicof zeolite beta is its high thermal stability.Thermogravimetric Analyses of zeolite beta crystals showed that the first weight loss was around 465 C as given in Fig.2c and it demonstrates the removal of the structure directing agent (SDA)from the zeolite structure.Thus,zeolite crystals were calcined at higher temperatures to remove SDA completely.The thermal decomposition temperature of zeolite beta particles was around 850 C,this means that the zeolite beta particles are stable up to this temperature.Hence,they are suitable for fuel cell applications.As a result of the EDX analysis it was found that the Si/Al ratio in the structure of the as synthesized zeolite Na-Beta is close to the value of Si/Al ratio in the batch solution (theo-retical)(Table 1).3.2.Sulfonated polymer characterizationsDegree of sulfonation (DS)values of the sulfonated polymers was determined by using H-NMR data as described intheFig.2–(a)XRD pattern of as synthesized zeolite beta (SiO 2/Al 2O 3[20)(b)SEM micrograph of as synthesized zeolite beta (c)TGA of as synthesized zeolite beta.i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 34(2009)4645–46524648experimental section.The signal around7.6ppm chemical shifts corresponds to the aromatic proton H E and its area relative to the other aromatic protons shows the extent of DS (data are not given).The degree of sulfonation is directly related to the reaction time,temperature and the amount of the sulfonation agent used.At higher temperatures the reaction kinetics is enhanced thus higher degrees of sulfonation are achieved. PEEK sulfonation proceeds very slow at room temperature and takes several days to reach a DS above50%.However at around50 C this time decreases to several hours as shown in Fig.3which is consistent with the literature[13].DS of PES was determined similarly as reported in the literature[14].Since sulfonation of PES is more difficult than that of PEEK because of the electrophilic sulfone linkage,DS was around20%.Therefore,conductivity of SPES samples was lower than SPEEK.Since swelling and thermohydrolytic stability strongly depends on DS,SPES membranes showed better stability and low swelling.These properties can becombined by blending these compatible polymers.3.3.Proton conductivity of composite membranesThe objective of introducing zeolite particles into the polymer matrix was to enhance the proton transfer through the membrane by retaining water within the membrane and to create water mediated pathways while contributing their own proton conductivity.The hydrophilic zeolite particles improved the water retention property of the SPEEK membranes.Above60 C,the composite membranes absor-bed too much water and swelling problem was observed above this temperature(Fig.4).Thus,the proton conductivity analyses of composite membranes were limited up to this temperature.The proton conductivities of plain and composite membranes were measured at room temperature before and after treatment with1M HCl.Acid treatment was performed after the casting process,and all the membranes were kept in 1M HCl for2h for complete protonation.Acid treated membranes always result in higher conductivities naturally since all the available ion exchange sites are saturated with protons(–SO3H).All membranes were washed and hydrated with deionized water prior to measurement.As shown in Fig.5,the membranes with higher DS were resulted in better proton conductivities.Proton transfer enhances by increasing the number of acid sites enhances the proton transfer.Moreover,the effect of acid treatment on proton conductivity was explored in Fig.5and improved proton conductivities were observed after the acid treatment of the membranes.Thus,the membranes were treated with 1M HCl and washed with distilled water prior to proton conductivity measurements.Another important observation that could be made in Fig.5is the effect of zeolite particles. The composite membranes containing zeolite Beta have shown improved proton conductivities,for instance,0.11S/ cm was achieved for the composite membranes with74%DS after acid treatment.This is a promising result,since it is comparable with the conductivity of Nafion112membrane (0.1S/cm).Fig.3–Degree of sulfonation with respect to time ofsulfonationreaction.Fig.4–Water uptake capacities of plain and compositeSPEEKmembranes.Fig.5–Proton conductivity of plain and compositemembranes(with10wt%zeolite loading)at roomtemperature and fully hydrated state.i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y34(2009)4645–46524649In order to overcome the swelling problems observed in the pure and composite SPEEK membranes,SPEEK polymer was blended with a more hydrophobic polymer,namely sulfonated poly-ether-sulfone (SPES).The PES polymer was post-sulfonated and blended with SPEEK polymer at pre-determined proportions before membrane casting.However,owing to the poor proton transfer mechanism of SPES poly-mer,lower conductivities were obtained for blend membranes compared to the pure and composite SPEEK membranes.The proton conductivity measurements of pure SPEEK,SPES and blend membranes are given in Fig.6.So a trade-off between mechanical strength and conductivity exists for these blends.3.4.Performance testsFirst of all,the effect of using different catalyst ink solutions on the membrane performance is explored.The MEAs could be either prepared by using Nafion Òsolution or the original SPEEK solution [15].The comparison of two MEAs prepared by both Nafion Òand SPEEK solutions are given in Fig.7.It is apparent that the utilization of Nafion Òsolution in the catalyst ink resulted inhigher performance.Thus,Nafion Òsolution is utilized in the preparation of all MEAs.Second,the effect of operating temperatures on the performances of MEAs prepared by using SPEEK membranes (DS 56%)was examined and the results are given in Fig.8.It was observed that SPEEK based MEAs were not stable at high temperatures and they have punctured above 90 C.The best operating temperature of SPEEK based MEAs was found to be 70 C as demonstrated in Fig.9.The thermal stability of the membranes could be improved by blending with SPES poly-mer.It was noticed that,after the incorporation of 10wt%SPES into SPEEK membrane,the cell operating temperature could be increased up to 90 C without any damage to the membrane.As shown in Fig.9,the highest power output could be obtained at 80 C for SPES–SPEEK blend membranes.In order to understand the effect of sulfonation level on membrane performance,MEAs were prepared by using two membranes with different DS and the test results are displayed in Fig.10.It was not surprising to observe higher performance results for the MEA prepared by using the membrane at higher DS,since the proton transfer facilitates more easily with increased sulfonic acid groupcontents.Fig.6–Proton conductivities of plain and blendmembranes.Fig.7–Comparison of Nafion Òsolution and SPEEK solution for SPEEK based MEAs (cell temperature 708C).Fig.8–Effect of operating temperature on the performance of SPEEK (DS 56%)basedMEAs.Fig.9–Effect of sulfonation level on the performance of SPEEK based MEAs (cell temperature 708C).i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 34(2009)4645–46524650Another important parameter affecting the MEA’s perfor-mance is membrane treatment.Since the proton transfer mechanism of both SPEEK and SPES membranes depend on the acidic character of the membranes,the acid treatment influences the membrane performance.The performance curves of both untreated and acid treated SPEEK based MEAs are given in Fig.11.The acid treated membrane showed almost threefold higher power density compared to the untreated membrane.The fuel cell performance of SPEEK membrane was compared with the performance of Nafion Òmembrane as given in Fig.12.The current density of plain SPEEK membrane (DS 56%)was 400mA/cm 2at 0.6V,whereas that of Nafion Ò112membrane was 660mA/cm 2under the same conditions.Although SPEEK membrane possesses lower fuel cell perfor-mance in comparison to the Nafion membrane,the result is promising when the relatively low cost of SPEEK membrane is considered.Moreover,the composite membrane SPEEK-Laponite exhibited better performance than the pure SPEEK membrane [9].Composite membranes prepared with inor-ganic additives such as silica,zeolite 4A and zeolite beta increase the proton conductivity and fuel cell performances of both Nafion Òand SPES-40polymer membrane [16].It should be emphasized that the same technique of MEA fabrication,cell assembling and operating conditions were used in the present work.The significant difference of the obtained performances can be caused by various factors.One of them is the difference in the thickness of the membranes [17].Proton transfer mechanisms are also quite different in Nafion Òand SPEEK membranes.Degree of hydration is the factor that influences the proton conductivity of a membrane.The hydration is dependent on the phase separation between the hydrophobic polymer backbone and hydrophilic side chains [18].Nafion Òand SPEEK polymers both exhibit phase separated domains consisting of an extremely hydrophobic backbone which gives morphological stability and extremely hydrophilic side chains [18].Higher performances could be obtained for the membranes with higher DS values and for composite membranes.4.ConclusionThe development of alternative membranes at relatively low cost for fuel cell applications requires target properties such as suitable thermal and chemical stability,mechanical strength,comparable proton conductivity and fuel cell performance with the commercial PEM fuel cell membranes.In this study,zeolite beta composite membranes and blend membranes were developed.The proton conductivity of SPEEK was improved by addition of an aluminosilicate,zeolite beta.Also thermohydrolytic stability was improved by blending poly-ether-sulfone (PES).The proton conductivity of plain SPEEK membrane (DS ¼68%)was 0.06S/cm at 60 C and the conductivity of the composite membrane consisting of zeolite beta fillers into SPEEK was further increased to 0.13S/cm.Among the zeolite beta/SPEEK composite membranes the best conductivity results were achieved with zeolite beta having a SiO 2/Al 2O 3ratio of 50at 10wt%loading.Single fuel cell tests performed at different operating temperatures indicated that SPES/SPEEK membrane ismoreFig.11–Effect of acid treatment on the performance of SPEEK (DS 56%)based MEAs (cell temperature 708C).Fig.12–The comparison of performances of Nafion Òand SPEEKmembranes.Fig.10–Effect of operating temperature on the performance of blend membranes.i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 34(2009)4645–46524651stable hydrodynamically and also performed better than pristine SPEEK membranes which swell excessively. Membrane electrode assemblies(MEAs)were prepared by gas diffusion layer(GDL)spraying method.The highest perfor-mance,which is400mA/cm2,was obtained for SPEEK membrane(DS56%)at0.6V for a H2–O2/PEMFC working at 1atm and70 C.At the same conditions NafionÒ112gave 660mA/cm2.It was observed that the operating temperature can be increased up to90 C with polymer blends containing poly-ether-sulfone(PES).AcknowledgementsThis study was supported by Turkish Scientific and Research Counsel with Project104M364and Turkish State Planning Organization Grant BAP-08-11-DPT2005K120600.r e f e r e n c e s[1]Barbir F.PEM fuel cells theory and practice.ElsevierAcademic Press;2005.[2]Corbo P,Migliardini F,Veneri O.Performance investigation of2.4kW PEM fuel cell stack in vehicles.International Journalof Hydrogen Energy2007;32:4340–9.[3]Hu M,Sui S,Zhu X,Yu Q,Cao G,Hong X,et al.A10kW classPEM fuel cell stack based on the catalyst-coated membrane (CCM)method.International Journal of Hydrogen Energy2006;31:1010–8.[4]Yan X,Hou M,Sun L,Liang D,Shen Q,Xu H,et al.ACimpedance characteristics of a2kW PEM fuel cell stackunder different operating conditions and load changes.International Journal of Hydrogen Energy2007;32:4358–64.[5]Bıyıkog˘lu A.Review of proton exchange membrane fuel cellmodels.International Journal of Hydrogen Energy2005;30: 1181–212.[6]Li Q,He R,Jensen JO,Bjerrum NJ.PBI-based polymermembranes for high temperature fuel cells–preparation,characterization and fuel cell demonstration.Fuel Cells2004;4(3):147–59.[7]Xing DM,Li BY,Liu FQ,Fu YZ,Zhang HM.Characterization ofsulfonated poly(ether ether ketone)/polytetrafluoroethylene composite membrane for fuel cell applications.Fuel Cells2005;5(3):406–11.[8]Akata B,Yilmaz B,Jirapnogphan SS,Warzywoda J,Sacco Jr A.Characterization of zeolite beta grown in microgravity.Microporous and Mezoporous Materials2004;71:1–9.[9]Chang JH,Park JH,Park G-G,Kim C-S,Park O-O.Proton-conducting composite membranes derived from sulfonated hydrocarbon and inorganic materials.Journal of PowerSources2003;124:18–25.[10]Zaidi SMJ,Michailenko SD,Robertson GP,Guiver MD,Kaliaguine S.Proton conducting composite membranes from polyether ether ketone and heteropolyacids for fuel cellapplications.Journal of Membrane Science2000;173:17–34.[11]Bayrakc¸eken A,Erkan S,Tu¨rker L,Erog˘lu_I.Effects ofmembrane electrode assembly components on protonexchange membrane fuel cell performance.InternationalJournal of Hydrogen Energy2008;33(1):165–70.[12]Holmberg BA,Hwang S-J,Davis ME,Yan Y.Synthesis andproton conductivity of sulfonic acid functionalized zeolitebeta nanocrystals.Microporous and Mesoporous Materials 2005;80:347–56.[13]Huang RYM,Shao P,Burns CM,Feng X.Sulfonation ofpolyetherether–ketone(PEEK):kinetic study andcharacterization.Journal of Applied Polymer Science2001;82: 2651–60.[14]Guan R,Zou H,Lu D,Gong C,Liu Y.Polyethersulfonesulfonated by chlorosulfonic acid and its membranecharacteristics.European Polymer Journal2005;41:1554–60.[15]S x engu¨l E,Erkan S,Erog˘lu_I,Bac¸N.Effect of gas diffusion layercharacteristics and addition of pore forming agents on theperformance of polymer electrolyte membrane fuel cells.Chemical Engineering Communications,2008;196(1–2):161–70.[16]Bac N,Nadirler S,Ma C,Mukerjee S.Inorganic–organiccomposite membranes for fuel cell applications.In:Proceedings international hydrogen energy congress andexhibition IHEC2005Istanbul,Turkey;2005.[17]Grigoriev SA,Lyutikova EK,Martemianov S,Fateev VN.Onthe possibility of replacement of Pt by Pd in a hydrogenelectrode of PEM fuel cells.International Journal of Hydrogen Energy2007;32:4438–42.[18]Hogarth M,Glipa X.High temperature membranes for solidpolymer fuel cells.Johnson Matthey Technology Center;2001 [Crown Copyright].i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y34(2009)4645–4652 4652。

A fracture-resistant high-entropy alloy for cryogenic applications

DOI: 10.1126/science.1254581, 1153 (2014);345 Science et al.Bernd Gludovatz A fracture-resistant high-entropy alloy for cryogenic applicationsThis copy is for your personal, non-commercial use only.clicking here.colleagues, clients, or customers by , you can order high-quality copies for your If you wish to distribute this article to othershere.following the guidelines can be obtained by Permission to republish or repurpose articles or portions of articles): September 13, 2014 (this information is current as of The following resources related to this article are available online at/content/345/6201/1153.full.html version of this article at:including high-resolution figures, can be found in the online Updated information and services, /content/suppl/2014/09/03/345.6201.1153.DC1.html can be found at:Supporting Online Material /content/345/6201/1153.full.html#ref-list-1, 2 of which can be accessed free:cites 45 articles This article/cgi/collection/mat_sci Materials Sciencesubject collections:This article appears in the following registered trademark of AAAS.is a Science 2014 by the American Association for the Advancement of Science; all rights reserved. The title Copyright American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the Science o n S e p t e m b e r 13, 2014w w w .s c i e n c e m a g .o r g D o w n l o a d e d f r o mdid not observe the formation of any well-defined structures in the absence of an applied magnetic field (see,e.g.,fig.S8J).24.A.Dong et al .,Nano Lett.11,841–846(2011).25.S.Brooks,A.Gelman,G.Jones,X.-L.Meng,Handbook of Markov Chain Monte Carlo (Chapman &Hall,London,2011).26.Z.Kakol,R.N.Pribble,J.M.Honig,Solid State Commun.69,793–796(1989).27.Ü.Özgür,Y.Alivov,H.Morkoç,J.Mater.Sci.Mater.Electron.20,789–834(2009).28.The formation of helices,and the self-assembly of NCs in our system in general,is likely facilitated by entropic forces;OA used in large excess during self-assembly may act as adepletion agent,inducing crystallization of NCs during hexane evaporation as reported previously (29).29.D.Baranov et al .,Nano Lett.10,743–749(2010).30.On the basis of measurements of electrophoretic mobility [see (34)]and the lack of literature reports on electric dipole moments of magnetite nanoparticles,we did not considerelectrostatic and electric dipole-dipole interactions in our analysis of interparticle interactions.At the same time,we cannot exclude 31.S.Srivastava et al .,Science 327,1355–1359(2010).32.S.Das et al .,Adv.Mater.25,422–426(2013).33.J.V.I.Timonen,tikka,L.Leibler,R.H.A.Ras,O.Ikkala,Science 341,253–257(2013).34.Previous self-assembly experiments performed in nonpolarsolvents excluded a significant role played by electrostatic interactions [e.g.,(35,36)].Although the degree ofdissociation of OA in hexane (dielectric constant =1.84)is negligible,the large excess of OA as well as the nature of our experimental setup (self-assembly at the liquid-air interface)might potentially promote dissociation of OA;to verify this possibility,we used a Malvern Zetasizer Nano ZS to perform electrophoretic mobility (m e )measurements of our nanocubes in hexane both in the absence and in the presence of additional OA (5%v/v).The results [0.00706(T 0.00104)×10−4cm 2V –1s –1and 0.0218(T 0.00710)×10−4cm 2V –1s –1,respectively]indicate that in both cases,the nanocubes are essentially neutral [compare with (37)].35.Z.Chen,J.Moore,G.Radtke,H.Sirringhaus,S.O ’Brien,J.Am.Chem.Soc.129,15702–15709(2007).37.S.A.Hasan,D.W.Kavich,J.H.Dickerson,mun.2009,3723–3725(2009).ACKNOWLEDGMENTSSupported by Israel Science Foundation grant 1463/11,theG.M.J.Schmidt-Minerva Center for Supramolecular Architectures,and the Minerva Foundation with funding from the Federal German Ministry for Education and Research (R.K.)and byNSF Division of Materials Research grant 1309765and American Chemical Society Petroleum Research Fund grant 53062-ND6(P.K.).SUPPLEMENTARY MATERIALS/content/345/6201/1149/suppl/DC1Materials and Methods Figs.S1to S28References (38–92)31March 2014;accepted 14July 2014Published online 24July 2014;METAL ALLOYSproperties required for structural applica-tions.Consequently,alloying elements are added to achieve a desired microstructure or combination of mechanical properties,such as strength and toughness,although the re-sulting alloys invariably still involve a single dom-inant constituent,such as iron in steels or nickel in superalloys.Additionally,many such alloys,such as precipitation-hardened aluminum alloys,rely on the presence of a second phase for me-chanical performance.High-entropy alloys (1–3)represent a radical departure from these notions.they contain high concentrations (20to 25atomic percent)of multiple elements with different crystal structures but can crystallize as a single phase (4–7).In many respects,these alloys rep-resent a new field of metallurgy that focuses attention away from the corners of alloy phase diagrams toward their centers;we believe that as this evolving field matures,a number of fas-cinating new materials may emerge.The CrMnFeCoNi alloy under study here is a case in point.Although first identified a decade ago (1),the alloy had never been investigated mechanically until recently (5,6,8),yet is clearly scientifically interesting from several perspec-tives.It is not obvious why an equiatomic five-element alloy —where two of the elements (Cr and Fe)crystallize with the body-centered cubic (bcc)structure,one (Ni)as face-centered cubic (fcc),one (Co)as hexagonal close-packed (hcp),and one (Mn)with the complex A 12structure —should form a single-phase fcc structure.Fur-thermore,several of its properties are quite unlike those of pure fcc metals.Recent studies indicatethat the alloy exhibits a strong temperature de-of the yield strength between ambient cryogenic temperatures,reminiscent of bcc and certain fcc solid-solution alloys (6).any temperature-dependent effect of rate on strength appears to be marginal (6).the marked temperature-dependent in strength is accompanied by a substan-increase in tensile ductility with decreasing between 293K and 77K (6),which counter to most other materials where an dependence of ductility and strength is seen (9).Preliminary indications sug-that this may be principally a result of the ’s high work-hardening capability,possi-associated with deformation-induced nano-which acts to delay the onset of any instability (i.e.,localized plastic deforma-that can lead to premature failure)to higher (5).We prepared the CrMnFeCoNi alloy with high-elemental starting materials by arc melting drop casting into rectangular-cross-section copper molds,followed by cold forging and cross rolling at room temperature into sheets roughly 10mm thick.After recrystallization,the alloy had an equiaxed grain structure.Uniaxial tensile spec-imens and compact-tension fracture toughness specimens in general accordance with ASTM standard E1820(10)were machined from these sheets by electrical discharge machining.[See (11)for details of the processing procedures,sam-ple sizes,and testing methods.]Figure 1A shows a backscattered electron (BSE)micrograph of the fully recrystallized micro-structure with ~6-m m grains containing numer-ous recrystallization twins.Energy-dispersive x-ray (EDX)spectroscopy and x-ray diffraction (XRD)indicate the equiatomic elemental dis-tribution and single-phase character of the al-loy,respectively.Measured uniaxial stress-strain curves at room temperature (293K),in a dry ice –alcohol mixture (200K),and in liquid nitrogen (77K)are plotted in Fig.1B.With a decrease in temperature from 293K to 77K,the yield strength s y and ultimate tensile strength s utsSCIENCE 5SEPTEMBER 2014•VOL 345ISSUE 62011153RESEARCH |REPORTS1Materials Sciences Division,Lawrence Berkeley National Laboratory,Berkeley,CA 94720,USA.2Department of Materials Physics,Montanuniversität Leoben and Erich Schmid Institute of Materials Science,Austrian Academy of Sciences,Leoben 8700,Austria.3Materials Sciences and Technology Division,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA.4Materials Sciences and Engineering Department,University of Tennessee,Knoxville,TN 37996,USA.5Department of Materials Science and Engineering,University of California,Berkeley,CA 94720,USA.*Corresponding author.E-mail:georgeep@ (E.P.G.);roritchie@ (R.O.R.)increased by ~85%and ~70%,to 759and 1280MPa,respectively.Similarly,the tensile ductility (strain to failure,e f )increased by ~25%to >0.7;the strain-hardening exponent n remained high at ~0.4,such that there was an enhancement in the frac-ture energy (12)by more than a factor of 2.Table S1provides a detailed summary of the stresses and strains at the three different temperatures,as well as the corresponding strain-hardening exponents.In light of the extensive plasticity involved in the deformation of this alloy,we evaluated the fracture toughness of CrMnFeCoNi with non-linear elastic fracture mechanics,specifically with crack-resistance curve (R curve)measurements in terms ofthe J integral.Analogous to the stress intensity K for linear elastic analysis,provided that specific validity criteria are met,J unique-ly characterizes the stress and displacement fields in the vicinity of the crack tip for a non-linear elastic solid;as such,it is able to capture both the elastic and plastic contributions to the fracture process.J is also equivalent to the strain energy release rate G under linear elastic conditions;consequently,K values can be back-calculated from J measurements assuming a mode I equivalence between K and J :specifically,J =K 2/E ´,with E ´=E (Young ’s modulus)in plane stress and E /(1–n 2)(where n is Poisson ’s ratio)in plane strain.E and n values were determined by resonance ultrasound spectroscopy at each tem-perature (13).Our toughness results for the CrMnFeCoNi alloy at 293K,200K,and 77K are plotted in Fig.1C,in terms of J R (D a )–based resistance curves showing crack extension D a in precracked and side-grooved compact-tension specimens as a function of the applied J .Using these R curves to evaluate the fracture toughness for both the initiation and growth of a crack,we measured a crack initiation fracture toughness J Ic ,deter-mined essentially at D a →0,of 250kJ/m 2at 293K,which in terms of a stress intensity gives K JIc =217MPa·m 1/2.Despite a markedly increased strength at lower temperature,K JIc values at 200K and 77K remained relatively constant at K JIc =221MPa·m 1/2(J Ic =260kJ/m 2)and K JIc =219MPa·m 1/2(J Ic =255kJ/m 2),respectively.After11545SEPTEMBER 2014•VOL 345ISSUE SCIENCEFig.1.Microstructure and mechanical properties of the CrMnFeCoNi high-entropy alloy.(A )Fully recrystallized microstructure with an equiaxed grain structure and grain size of ~6m m;the composition is approximately equiatomic,and the alloy is single-phase,as shown from the EDX spectroscopy and XRD insets.(B )Yield strength s y ,ultimate tensile strength s uts ,and ductility (strain to failure,e f )all increase with decreasing temperature.The curves are typical tests at the individual temperatures,whereas the data points are means T SD of multiple tests;see table S1for exact values.(C )Fracture toughness measure-ments show K JIc values of 217MPa·m 1/2,221MPa·m 1/2,and 219MPa·m 1/2at 293K,200K,and 77K,respectively,and an increasing fracture resistance in terms of the J integral as a function of crack extension D a [i.e.,resistance curve (R curve)behavior].(D )Similar to austenitic stainless steels (e.g.,304,316,or cryogenic Ni steels),the strength of the high-entropy alloy (solid lines)increases with decreasing temperature;although the toughness of the other materials decreases with decreasing temperature,the toughness of the high-entropy alloy remains unchanged,and by some measures it actually increases at lower temperatures.(The dashed lines in the plots mark the upper and lower limits of data found in the literature.)RESEARCH |REPORTSinitiation,the fracture resistance further increased with extensive subcritical crack growth;after just over 2mm of such crack extension,a crack growth toughness exceeding K =300MPa·m 1/2(J =500kJ/m 2)was recorded [representing,in terms of ASTM standards,the maximum (valid)crack extension capacity of our samples].Such toughness values compare favorably to those of highly alloyed,austenitic stainless steels such as 304L and 316L,which have reported tough-nesses in the range of K Q =175to 400MPa·m 1/2at room temperature (14–16),and the best cryogenic steels such as 5Ni or 9Ni steels,with K Q =100to 325MPa·m 1/2at 77K (17–19).Similar to the high-entropy alloy,these materials show an expected increase in strength with decreasing temper-ature to 77K;however,unlike the high-entropy alloy,their reported fracture toughness values are invariably reduced with decreasing temperature (20)(Fig.1D)and furthermore are rarely valid (i.e.,they are size-and geometry-dependent and thus not strictly material parameters).The high fracture toughness values of the CrMnFeCoNi alloy were associated with a 100%ductile fracture by microvoid coalescence,with the extent of deformation and necking behavior being progressively lessapparent at the lower temperatures (Fig.2,A and B).EDX analysis of the particles,which were found inside the voids of the fracture surface and acted as initiation sites for their formation,indicated either Cr-rich or Mn-rich compounds (Fig.2B,inset).These particles are likely oxides associated with the Mn additions;preliminary indications are that they are absent in the Mn-free (CoCrFeNi)alloy (6).Both microvoid size and particle size varied markedly;the microvoids ranged in size from ~1m m to tens of micrometers,with particle sizes ranging from <1m m to ~5m m (Fig.2B,inset)with an average size of 1.6m m and average spacing d p ≈49.6m m,respectively.To verify the high measured fracture tough-ness values,we used three-dimensional (3D)ster-eophotogrammetry of the morphology of these fracture surfaces to estimate local crack initia-tion toughness (K i )values for comparison with the global,ASTM-based K JIc measurements.This technique is an alternative means to characterize the onset of cracking,particularly under large-scale yielding conditions.Under mode I (tensile)loading,the crack surfaces completely separate from each other,with the regions of first sepa-ration moving the farthest apart and progres-sively less separation occurring in regions that crack later.Accordingly,the formation and coa-lescence of microvoids and their linkage with the crack tip allow for the precise reconstruction of the point of initial crack advance from the juxta-position of the stereo images of each fracture sur-face.This enables an evaluation of the crack tip opening displacement at crack initiation,CTOD i ,which then can be used to estimate the local stress intensity K i at the midsection of the sample at the onset of physical crack extension,where D a =0(21).Specifically,we used an automatic fracture surface analysis system that creates 3D digital surface models from stereo-image pairs of the corresponding fracture surfaces taken in the scan-ning electron microscope (Fig.2C);digitally re-constructing the crack profiles by superimposing the stereo-image pairs allows for a precise mea-surement of the CTOD i s of arbitrarily chosen crack paths (which must be identical on both fracture surfaces).Figure 2D indicates two examples of the approximately 10crack paths taken on both fracture surfaces of samples tested at 293K and 77K.The two corresponding profiles show the point at which the first void,formed ahead of the fatigue precrack,coalesced with this pre-crack to mark the initial crack extension,there-by locally defining the crack initiation event andSCIENCE 5SEPTEMBER 2014•VOL 345ISSUE 62011155Fig.2.Images of fractured CrMnFeCoNi samples.(A )Stereomicroscopic photographs of the fracture surfaces after testing indicate less lateral defor-mation and necking-like behavior with decreasing temperature.(B )SEM image of the fracture surface of a sample tested at room temperature shows ductile dimpled fracture where the void initiation sites are mainly Mn-rich or Cr-rich par-ticles,as shown by the EDX data (insets).(C )Three-dimensional digital fracturesurface models were derived from SEM stereo-image pairs,which indicate the transition from fatigue precrack to ductile dimpled fracture and the presence of the stretch zone.(D )Profiles of identical crack paths from both fracture halves of the fracture surface models were extracted to evaluate the crack tip opening displacement at the first physical crack extension,CTOD i ,which was then converted to J i using the relationship of the equivalence of J and CTOD (50).RESEARCH |REPORTSthe fracture toughness (22).Using these pro-cedures,the initial crack tip opening displace-ments at crack initiation were found to be CTOD i =57T 19m m at 293K and 49T 13m m at ing the standard J-CTOD equivalence relationship of J i ºs o CTOD i =K i 2/E ´gives es-timates of the crack initiation fracture toughness:K i =191MPa·m 1/2and 203MPa·m 1/2at 293K and 77K,respectively.These values are slightly con-servative with respect to the global R curve –based values in Fig.1C;however,this is to be expected,as theyare estimated at the initial point of physical contact of the first nucleated void with the precrack,whereas the ASTM-based measurements use an operational definition of crack initiation involving subcritical crack ex-tension of D a =200m m.To discern the micromechanisms underlying the excellent fracture toughness behavior,we fur-ther analyzed the fracture surfaces of samples tested at 293K and 77K by means of stereomi-croscopy and scanning electron microcopy (SEM).Some samples were additionally sliced in two halves,embedded,and metallographically pol-ished for BSE microscopy and electron back-scatter diffraction (EBSD)analysis of the region in the immediate vicinity of the crack tip and in the wake of the crack,close to the crack flanks,specifically “inside ”the sample where fully plane-strain conditions prevail.SEM images of the crack tip region of sam-ples tested at ambient and liquid nitrogen tem-peratures show the formation of voids and their coalescence characteristic of the microvoid co-alescence fracture process (Fig.3A).A large population of the particles that act as the void11565SEPTEMBER 2014•VOL 345ISSUE 6201 SCIENCEFig.3.Deformation mechanisms in the vicinity of the crack tip in the center (plane-strain)section of CrMnFeCoNi high-entropy alloy samples.(A )Low-magnification SEM images of samples tested at 293K and 77K show ductile fracture by microvoid coalescence,with a somewhat more distorted crack path at the lower temperature.(B )EBSD images show numerous annealing twins and pronounced grain misorientations due to dislocations —the primary defor-mation mechanism at 293K.(C )At 77K,BSE images taken in the wake of the propagated crack show the formation of pronounced cell structures resulting from dislocation activity.Both BSE and EBSD images show deformation-induced nanotwinning as an additional mechanism at 77K.[The EBSD image is an overlay to an image quality (IQ)map,which is a measure of the quality of the collected EBSD pattern used to visualize certain microstructural features.]RESEARCH |REPORTSinitiation sites can be seen on the fracture surfaces (Fig.2B);these particles have a substan-tial influence on material ductility and likely contribute to the measured scatter in the failure strains (Fig.1B).Macroscopically,fracture sur-faces at 77K appear significantly more deviated from a mode I (K II =0)crack path than at 293K (Fig.3A).Although such deflected crack paths act to reduce the local crack-driving force at the crack tip (23)and hence contribute to the rising R curve behavior (i.e.,crack growth toughness),this mech-anism cannot be responsible for the exceptional crack initiation toughness of this alloy.Such high K i values are conversely derived from the large CTOD s at crack initiation and are associated with the intrinsic process of microvoid coalescence;as such,they are highly dependent on the formation and size of voids,the prevailing deformation and flow conditions,and the presence of steady strain hardening to suppress local necking.Using simple micromechanical models for fracture (24),we can take advantage of a stress state –modified critical strain criterion for ductile fracture to derive estimates for these high tough-ness values (25–27).This yields expressions forthe fracture toughness in the form J Ic ≈s o e f l *o,where s o is the flow stress,e f is the fracture strain in the highly constrained stress state in the vicinity of the crack tip [which is roughly an order of magnitude smaller than the un-iaxial tensile ductility (28)],and l *ois the char-acteristic distanceahead of the tip over which this critical strain must be met for fracture (which can be equated to the particle spacing d p ).Assum-ing Hutchinson-Rice-Rosengren (HRR)stress-strain distributions ahead of a crack tip in plane strain for a nonlinear elastic,power-law hard-ening solid (strain-hardening coefficient of n )(29,30),and the measured properties,specifically E ,s o ,e f ,n ,n ,and d p ,for this alloy (11),estimates of the fracture toughness of K JIc =(J Ic E ´)1/2of ~150to 215MPa·m 1/2can be obtained for the measured particle spacing of d p ~50m m.Although approx-imate,these toughness predictions from the critical fracture strain model are completely consistent with a fracture toughness on the order of 200MPa·m 1/2,as measured for the CrMnFeCoNi alloy in this study (Fig.1C).In addition to crack initiation toughnesses of 200MPa·m 1/2or more,this alloy develops even higher crack growth toughness with stable crack growth at “valid ”stress intensities above 300MPa·m 1/2.These are astonishing toughness levels by any standard,particularly because they are retained at cryogenic temperatures.A primary factor here is the mode of plastic deformation,which induces a steady degree of strain hardening to suppress plastic instabilities;expressly,the mea-sured strain-hardening exponents of n ~0.4are very high relative to the vast majority of metals,particularly at this strength level.Recent studies have shown that,similar to mechanisms known for binary fcc solid solutions (31,32),plastic de-formation in the CrMnFeCoNi alloy at ambienttemperatures is associated with planar glide of 1/2〈110〉dislocations on {111}planes leading to the formation of pronounced cell structures at higher strains (5).However,at 77K,in addition to planar slip,deformation-induced nanoscale twinning has been observed both previously (5)and in the present study (Fig.3C)and contributes to the increased ductility and strain hardening at lower temperatures.Both the planar slip and nanotwinning mechanisms are highly active in the vicinity of the crack tip during fracture,as illustrated in Fig.3.EBSD images taken ahead of the crack tip inside the sample of a fracture toughness test performed at room temperature show grain misorientations resulting from dis-location activity as the only deformation mech-anism (Fig.3B).Aside from numerous annealing twins resulting from the recrystallization step during processing,twinning does not play a role at ambient temperatures,with only a few single nanotwins in evidence.With decrease in tem-perature,cell structure formation is more appar-ent,as shown by the BSE image in Fig.3C,taken in the wake of a crack propagating at 77K.Here,however,excessive deformation-induced nano-scale twinning occurs simultaneously with planar dislocation slip,leading to a highly distorted grain structure,which can be seen in both the BSE and IQ +EBSD images in the vicinity of the growing crack.[The EBSD image is shown as an overlay of an image quality (IQ)map to enhance visual-ization of structural deformations of the grains.]Note that several other classes of materials show good combinations of strength and ductility when twinning is the dominant deformation mecha-nism.These include copper thin films (33–36)and the recently developed twinning-induced plas-ticity (TWIP)steels (37–40),which are of great interest to the car industry as high-Mn steels (41–44).We believe that the additional plasticity mechanism of nanotwinning in CrMnFeCoNi is critical to sustaining a high level of strain hard-ening at decreasing temperatures;this in turn acts to enhance the tensile ductility,which,to-gether with the higher strength at low tem-peratures,preserves the exceptional fracture toughness of this alloy down to 77K.We conclude that the high-entropy CrMnFeCoNi alloy displays remarkable fracture toughness properties at tensile strengths of 730to 1280GPa,which exceed 200MPa·m 1/2at crack initiation and rise to >300MPa·m 1/2for stable crack growth at cryogenic temperatures down to 77K.The alloy has toughness levels that are comparable to the very best cryogenic steels,specifically cer-tain austenitic stainless steels (15,16)and high-Ni steels (17–19,45–48),which also have outstanding combinations of strength and ductility.With respect to the alloy ’s damage tolerance,a comparison with the other major material classes is shown on the Ashby plot of fracture toughness versus yield strength (49)in Fig.4.There are clearly stronger materials,which is understand-able given that CrMnFeCoNi is a single-phase material,but the toughness of this high-entropy alloy exceeds that of virtually all pure metals and metallic alloys (9,49).SCIENCE 5SEPTEMBER 2014•VOL 345ISSUE 62011157Fig.4.Ashby map showing fracture toughness as a function of yield strength for high-entropy alloys in relation to a wide range of material systems.The excellent damage tolerance (toughness combined with strength)of the CrMnFeCoNi alloy is evident in that the high-entropy alloy exceeds the toughness of most pure metals and most metallic alloys (9,49)and has a strength comparable to that of structural ceramics (49)and close to that of some bulk-metallic glasses (51–55).RESEARCH |REPORTSREFERENCES AND NOTES1. B.Cantor,I.T.H.Chang,P.Knight,A.J.B.Vincent,Mater.Sci.Eng.A 375–377,213–218(2004).2.J.-W.Yeh et al .,Adv.Eng.Mater.6,299–303(2004).3. C.-Y.Hsu,J.-W.Yeh,S.-K.Chen,T.-T.Shun,Metall.Mater.Trans.A 35,1465–1469(2004).4.O.N.Senkov,G.B.Wilks,J.M.Scott,D.B.Miracle,Intermetallics 19,698–706(2011).5. F.Otto et al .,Acta Mater.61,5743–5755(2013).6. A.Gali,E.P.George,Intermetallics 39,74–78(2013).7. F.Otto,Y.Yang,H.Bei,E.P.George,Acta Mater.61,2628–2638(2013).8.W.H.Liu,Y.Wu,J.Y.He,T.G.Nieh,Z.P.Lu,Scr.Mater.68,526–529(2013).9.R.O.Ritchie,Nat.Mater.10,817–822(2011).10.E08Committee,E1820-13Standard Test Method forMeasurement of Fracture Toughness (ASTM International,2013).11.See supplementary materials on Science Online.12.As a preliminary estimate of the fracture resistance,thearea under the load displacement curve of a tensile test was used to compute the fracture energy (sometimes termed the work to fracture),which was calculated from this area divided by twice the area of the crack surface.13.J.Maynard,Phys.Today 49,26–31(1996).14.K Q values refer to fracture toughnesses that are notnecessarily valid by ASTM standards (i.e.,they do not meet the J -validity and/or plane strain conditions).Consequently,these toughnesses are likely to be inflated relative to truly valid numbers and are size-and geometry-dependent;they are not strictly material parameters.When comparing these values to the toughnesses measured in this study forCoCrFeMnNi,it is important to note that all values determined here for the high-entropy alloy were strictly valid,meeting ASTM standards for both J validity and plane ls,Int.Mater.Rev.42,45–82(1997).16.M.Sokolov et al .,in Effects of Radiation on Materials:20thInternational Symposium ,S.Rosinski,M.Grossbeck,T.Allen,A.Kumar,Eds.(ASTM International,West Conshohocken,PA,2001),pp.125–147.17.J.R.Strife,D.E.Passoja,Metall.Trans.A 11,1341–1350(1980).18.C.K.Syn,J.W.Morris,S.Jin,Metall.Trans.A 7,1827–1832(1976).19.A.W.Pense,R.D.Stout,Weld.Res.Counc.Bull.205,1–43(1975).20.Note that despite the uncertainty in the (valid)toughnessvalues for the stainless and high Ni steels,their upper toughness range could possibly be higher than the current measurements for the CrMnFeCoNi alloy.It must beremembered,however,that these materials are microalloyed and highly tuned with respect to grain size/orientation,tempering,precipitation hardening,etc.,to achieve their mechanical properties,whereas the current CrMnFeCoNi alloy is a single-phase material that undoubtedly can be further improved through second-phase additions and grain control.21.J.Stampfl,S.Scherer,M.Gruber,O.Kolednik,Appl.Phys.A 63,341–346(1996).22.J.Stampfl,S.Scherer,M.Berchthaler,M.Gruber,O.Kolednik,Int.J.Fract.78,35–44(1996).23.B.Cotterell,J.Rice,Int.J.Fract.16,155–169(1980).24.R.O.Ritchie,A.W.Thompson,Metall.Trans.A 16,233–248(1985).25.A.C.Mackenzie,J.W.Hancock,D.K.Brown,Eng.Fract.Mech.9,167–188(1977).26.R.O.Ritchie,W.L.Server,R.A.Wullaert,Metall.Trans.A 10,1557–1570(1979).27.Details of the critical strain model for ductile fracture (25,26)and the method of estimating the fracture toughness are described in the supplementary materials.28.J.R.Rice,D.M.Tracey,J.Mech.Phys.Solids 17,201–217(1969).29.J.W.Hutchinson,J.Mech.Phys.Solids 16,13–31(1968).30.J.R.Rice,G.F.Rosengren,J.Mech.Phys.Solids 16,1–12(1968).31.H.Neuhäuser,Acta Metall.23,455–462(1975).32.V.Gerold,H.P.Karnthaler,Acta Metall.37,2177–2183(1989).33.M.Dao,L.Lu,Y.F.Shen,S.Suresh,Acta Mater.54,5421–5432(2006).34.L.Lu,X.Chen,X.Huang,K.Lu,Science 323,607–610(2009).35.K.Lu,L.Lu,S.Suresh,Science 324,349–352(2009).36.A.Singh,L.Tang,M.Dao,L.Lu,S.Suresh,Acta Mater.59,2437–2446(2011).37.R.A.Hadfield,Science 12,284–286(1888).38.V.H.Schumann,Neue Hütte 17,605–609(1972).39.L.Remy,A.Pineau,Mater.Sci.Eng.28,99–107(1977).40.T.W.Kim,Y.G.Kim,Mater.Sci.Eng.A 160,13–15(1993).41.O.Grässel,G.Frommeyer,C.Derder,H.Hofmann,J.Phys.IV07,C5-383–C5-388(1997).42.O.Grässel,L.Krüger,G.Frommeyer,L.W.Meyer,Int.J.Plast.16,1391–1409(2000).43.G.Frommeyer,U.Brüx,P.Neumann,ISIJ Int.43,438–446(2003).44.L.Chen,Y.Zhao,X.Qin,Acta Metall.Sin.Engl.Lett.26,1–15(2013).45.D.T.Read,R.P.Reed,Cryogenics 21,415–417(1981).46.R.D.Stout,S.J.Wiersma,in Advances in CryogenicEngineering Materials ,R.P.Reed,A.F.Clark,Eds.(Springer,New York,1986),pp.389–395.47.Y.Shindo,K.Horiguchi,Sci.Technol.Adv.Mater.4,319–326(2003).48.J.W.Sa et al .,in Twenty-First IEEE/NPS Symposium on FusionEngineering 2005(IEEE,Piscataway,NJ,2005),pp.1–4.49.M.F.Ashby,in Materials Selection in Mechanical Design ,M.F.Ashby,Ed.(Butterworth-Heinemann,Oxford,ed.4,2011),pp.31–56.50.C.F.Shih,J.Mech.Phys.Solids 29,305–326(1981).51.C.J.Gilbert,R.O.Ritchie,W.L.Johnson,Appl.Phys.Lett.71,476–478(1997).52.A.Kawashima,H.Kurishita,H.Kimura,T.Zhang,A.Inoue,53.A.Shamimi Nouri,X.J.Gu,S.J.Poon,G.J.Shiflet,J.J.Lewandowski,Philos.Mag.Lett.88,853–861(2008).54.M.D.Demetriou et al .,Appl.Phys.Lett.95,041907,041907–3(2009).55.M.D.Demetriou et al .,Nat.Mater.10,123–128(2011).ACKNOWLEDGMENTSSponsored by the U.S.Department of Energy,Office ofScience,Office of Basic Energy Sciences,Materials Sciences and Engineering Division.All data presented in this article can additionally be found in the supplementary materials.Author contributions:E.P.G.and R.O.R.had full access to theexperimental results in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.The alloys were processed by D.C.and mechanically characterized by B.G.,A.H.,and D.C.Study design,interpretation and analysis of data,and preparation of the manuscript were performed jointly by B.G.,A.H.,D.C.,E.H.C.,E.P.G.,and R.O.R.The authors declare no conflict of interest.SUPPLEMENTARY MATERIALS/content/345/6201/1153/suppl/DC1Materials and Methods Supplementary Text Fig.S1Table S19April 2014;accepted 18July 2014process,representing the initial transfor-mation of a disordered phase into an or-dered one.It is also the most difficult part of the process to observe because it hap-pens on very short time and length scales.In thebate as to whether classical nucleation theory (CNT),as initially developed by Gibbs (1),is a suitable framework within which to describe the process,or whether nonclassical elements such as dense liquid phases (2–4)or (meta)stable clusters (5)play important roles.Furthermore,uncertainty exists as to whether a final,stable phase can nucleate directly from solution or whether it forms through a multistep,multi-phase evolution (6,7).In the case of multistep nucleation pathways,whether transformation from one phase to another occurs through nu-cleation of the more stable phase within the11585SEPTEMBER 2014•VOL 345ISSUE 6201 SCIENCE1Department of Materials Science and Engineering,University of California,Berkeley,CA 94720,USA.2Molecular Foundry,Lawrence Berkeley National Laboratory,Berkeley,CA 94720,USA.3Physical Sciences Division,Pacific Northwest National Laboratory,Richland,WA 99352,USA.4Department ofMaterials Science and Engineering,University of Washington,Seattle,WA 98195,USA.*Corresponding author.E-mail:james.deyoreo@RESEARCH |REPORTS。

中石油-2050年世界与中国能源展望(2019版)-2019.12

展望期内,世界仅以36%的能源需求增长支撑了172%的经济增长,能效提高是主要动 因。到2050年,能源消费强度降至0.88吨标油/万美元,较2015年下降50%,年均下 降2%。
世界一次能源需求量及年均增速变化
200
世界一次能源需求和消费强度
2.0
190
一次能源消费(亿吨标油,左轴)
年均增速(%,右轴) 3.5
Energy Outlook 2050
10
CNPC ETRI 2019
一次能源
Energy Outlook 2050
11
CNPC ETRI 2019
一次能源需求维持增长势头,能源消费强度持续下降
世界一次能源需求将持续增长,考虑贸易摩擦加剧,影响技术扩散和能源效率提高, 需求总量高于上年预期,2050年达到182亿吨标油,年均增长0.89%。其中,20152035年年均增长1.2%,2036-2050年年均增长0.45%,增速逐渐放缓。
Energy Outlook 2050
8
CNPC ETRI 2019
非洲和亚太主导世界人口持续增长,劳动人口占比下降
2050 年 , 世 界 人 口 将 达 到 97.7 亿 , 比 2015 年 增 加 32.4% , 2015-2050 年 年 均 增 长 0.8%。非洲与亚太地区分别增长13.3亿与6.5亿,贡献世界新增人口的82.9%。 OECD国家人口占比从2015年的14.7%下降到12.1%,非OECD国家占比从85.3%上升到 87.9%。 世界劳动力人口(15-64岁)占比62.8%,较2015年下降2.7个百分点。
0.0 2050
0
0.0 2015 2020 2025 2030 2035 2040 2045 2050
  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。

Overview of indices: Issue 27
The Ernst & Young country attractiveness indices provide scores for national renewable energy markets, renewable energy infrastructures and their suitability for individual technologies. The indices provide scores out of 100 and are updated on a regular basis.
South Korea leads the new entrants to secure 18th position, on the back of its ambitious targets, strong incentives, and robust supply chain. Romania and Egypt both achieved a ranking of 22 as a result of their fast-growing wind markets, while Mexico completes the new line up, ranking 25th, benefiting from challenging targets and strong wind and solar resources. As a result, the Czech Republic has fallen outside the top 30 countries, mostly due to various plans by Parliament to remove or significantly reduce solar subsidies.
Ernst & Young was ranked the leading project finance advisor in the Americas, Europe, Middle East and Africa between 2001 and 2009 by Project Finance International
A new addition to this issue is a page dedicated to the Solar indices, with commentary on key movements in the photovoltaic (PV) and concentrated solar power (CSP) markets. This will be a regular feature, and is supplemented in this issue with a technology focus article on solar CSP, discussing the key markets, the four major technologies, regulatory drivers and recent news.
The lead article discusses progress post the credit crunch, highlighting the differing pace of recovery between Western and BRIC (Brazil, Russia, India and China) nations, and analyzing the effect of commodity and carbon prices. It also looks forward to Cancun and the issues critical to shaping the future low carbon economy, and the need to ensure any agreements reached (however limited) do not ignore the developing world poor.
November 2010 Issue 27
Renewable energy country attractiveness indices
In this issue:
Overview of indices
1
Cancun — low carbon must not ignore the developing world poor2
Wind indices
13
Near-term wind index
14
Solar indices
15
Country focus —
16
China, US, Germany, India, UK, France, Japan
Introductory country focus — 23
South Korea, Romania, Egypt, Mexico
India’s National Solar Mission 6
Technology focus — concentrated
solar power
7
Issue highlights — equity
8
M&A activity
9
IPO activity
10
All renewables index
11
The country attractiveness indices take a generic view, and different sponsor/financier requirements will clearly affect how countries are rated. Ernst & Young’s Renewable Energy Group can provide detailed studies to meet specific corporate objectives. It is important that readers refer to the guidance notes set out on pages 27-28 when referring to the indices.
The US, which topped the indices between November 2006 and May 2010, is now five points behind the ascendant China. The continued repercussions of the financial crisis, low gas prices and the uncertain medium-to-long term policy environment have prompted a one point fall this quarter, while China rose two points.
Commentary — guidance notes 27
Company index
29
Glossary
30
Global highlights
A new world order is emerging in the clean energy sector with China now the clear leader in the global renewables market, according to this issue of Ernst & Young’s country attractiveness indices. The new order also includes four significant new entrants in the rankings: South Korea, Romania, Egypt, and Mexico.
Elsewhere in the global economy, India gained a point following the completion of regulations for the trading of renewable energy certificates (RECs) by seven Indian states, with another nine having now prepared drafts. The UK has also climbed a point following its Government’s public Spending Review and the publication of a National Infrastructure Plan — both of which signalled strong support for renewables and specific investment in offshore wind. Japan jumped three points due to its solar market expected to increase to four times the 2009 level by 2020.
Long-term indices
The long-term indices are forward looking and take a long-term view, hence the UK’s high ranking in the wind index, explained by the large amount of unexploited wind resource, strong offshore regime and attractive tariffs available under the Renewables Obligation (RO) mechanism. Conversely, although Denmark has the highest proportiono population level, it scores relatively low because of its restricted grid capacity and reduced tariff incentives.
相关文档
最新文档