检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Antonelli, Michela Reinke, Annika Bakas, Spyridon Farahani, Keyvan Kopp-Schneider, Annette Landman, Bennett A. Litjens, Geert Menze, Bjoern Ronneberger, Olaf Summers, Ronald M. van Ginneken, Bram Bilello, Michel Bilic, Patrick Christ, Patrick F. Do, Richard K.G. Gollub, Marc J. Heckers, Stephan H. Huisman, Henkjan Jarnagin, William R. McHugo, Maureen K. Napel, Sandy Golia Pernicka, Jennifer S. Rhode, Kawal Tobon-Gomez, Catalina Vorontsov, Eugene Hulsmon, Hankan Meakin, James A. Ourselin, Sebastien Wiesenfarth, Manuel Arbeláez, Pablo Bae, Byeonguk Chen, Sihong Daza, Laura Feng, Jianjiang He, Baochun Isensee, Fabian Ji, Yuanfeng Jia, Fucang Kim, Namkug Kim, Ildoo Merhof, Dorit Pai, Akshay Park, Beomhee Perslev, Mathias Rezaiifar, Ramin Rippel, Oliver Sarasua, Ignacio Shen, Wei Son, Jaemin Wachinger, Christian Wang, Liansheng Wang, Yan Xia, Yingda Xu, Daguang Xu, Zhanwei Zheng, Yefeng Simpson, Amber L. Maier-Hein, Lena Cardoso, M. Jorge School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany University of Pennsylvania PhiladelphiaPA United States BethesdaMD United States Heidelberg Germany Electrical Engineering and Computer Science Vanderbilt University NashvilleTN United States Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen Netherlands Quantitative Biomedicine University of Zurich Zurich Switzerland DeepMind London United Kingdom School of Computing Department of Biomedical and Molecular Sciences Queen’s University KingstonON Canada Department of Surgery Memorial Sloan Kettering Cancer Center New YorkNY United States BethesdaMD United States Department of Informatics Technische Universität München Germany Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Department of Psychiatry & Behavioral Sciences Vanderbilt University Medical Center NashvilleTN United States Department of Radiology Stanford University StanfordCA United States Department of Computer Science and Software Engineering École Polytechnique de Montréal Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Medical Faculty University of Heidelberg Heidelberg Germany VUNO Inc. Seoul Korea Republic of Universidad de los Andes Colombia Department of Automation Tsinghua University Beijing China Heidelberg Germany Department of Computer Science Xiamen University Xiamen361005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Kakao Brain Korea Republic of Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Cere

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks;(2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability;(3) the training of accurate AI segmentation models is now commoditized to non AI *** Codes 68T07. © 2021, CC BY-NC-SA.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

P-152: Field Emission Properties of t-RNA Wrapped Carbon Nanotube Emitters

引用

SID Symposium Digest of Technical Papers 2012年第1期39卷

作者： Woo-Sung Cho Byeong-Kwon Ju Hyun Yong Shin Seung Wook Kim Yong Churl Kim Jung-Ho Park Jong-Hon Han Hun Kuk Park Display and Nanosystem Lab. College of Engineering Korea University Anam-dong Seongbuk-gu Seoul 136-701 Republic of Korea Department of Chemical & Biological Engineering Korea University Anam-dong Seongbuk-gu Seoul 136-701 Republic of Korea Display Device and Material Lab. Samsung Advanced Institute of Technology Suwon 440-600 Republic of Korea Department of Electronics and Computer Engineering Korea University NT based Information & Energy Storage Research Center Korea Electronics Technology Institude Gyeonggi-do 463-816 Republic of Korea Department of Biomedical Engineering Kyung Hee University Seoul 130-702 Republic of Korea

We have developed a method for fabricating a good carbon nanotube (CNT) emitter using electrophoretic deposition (EDP) technique. A film of t-RNA (transfer-ribonucleic acid) wrapped CNTs was deposited on an ITO substrate from an aqueous mixture of single walled CNT (SWCNT), t-RNA, and detergent by electrophoresis. t-RNA wrapped SWCNTs are not only dispersed in de-ionized (DI) water but also deposited on an ITO substrate.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

引用

arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Random laser ablated tags for anticounterfeiting purposes and towards physically unclonable functions

引用

Nature communications 2024年第1期15卷 8772页

作者： Srinivas Gandla Jinsik Yoon Cheol-Woong Yang HyungJune Lee Wook Park Sunkook Kim Multifunctional Nano Bio Electronics Lab Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. Institute for Wearable Convergence Electronics Department of Electronics and Information Convergence Engineering Kyung Hee University Deogyeong-daero Giheung-gu Yongin-si Gyeonggi-do 17104 Republic of Korea. Electron Microscopy Research Laboratory Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. Intelligent Networked Systems Lab Department of Computer Science and Engineering Ewha Womans University Ewhayeodae-gil Seodaemun-gu Seoul 03760 Republic of Korea. Institute for Wearable Convergence Electronics Department of Electronics and Information Convergence Engineering Kyung Hee University Deogyeong-daero Giheung-gu Yongin-si Gyeonggi-do 17104 Republic of Korea. parkwook@khu.ac.kr. Multifunctional Nano Bio Electronics Lab Department of Advanced Materials Science and Engineering Sungkyunkwan University Cheoncheon-dong Jangan-gu Suwon-si Gyeonggi-do 16419 Republic of Korea. seonkuk@skku.edu.

来源：评论

学校读者我要写书评

暂无评论

Cover Picture: Laser-scribed Graphene Electrodes as an Electrochemical Immunosensing Platform for Cancer Biomarker ‘eIF3d’ (Electroanalysis 4/2021)

引用

Electroanalysis 2021年第4期33卷

作者： Simge Balaban Tutku Beduk Ceren Durmus Eda Aydindogan Khaled Nabil Salama Suna Timur Department of Biochemistry Faculty of Science Ege University 35100 Bornova Izmir Turkey Sensors Lab Advanced Membranes and Porous Materials Center Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) 23955-6900 Thuwal Saudi Arabia Central Research Testing and Analysis Laboratory Research and Application Center Ege University 35100 Bornova Izmir Turkey

来源：评论

学校读者我要写书评

暂无评论

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal–organic frameworks for carbon capture

arXiv

引用

arXiv 2023年

作者： Park, Hyun Yan, Xiaoli Zhu, Ruijie Huerta, Eliu A. Chaudhuri, Santanu Cooper, Donny Foster, Ian Tajkhorshid, Emad Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Visualization Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Metal–organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m molg−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

来源：评论

学校读者我要写书评

暂无评论

Roadmap for phase change materials in photonics and beyond (vol 26, 107946, 2023)

引用

ISCIENCE 2023年第12期26卷 107946页

作者： Prabhathan, Patinharekandy Sreekanth, Kandammathe Valiyaveedu Teng, Jinghua Ko, Joo Hwan Yoo, Young Jin Jeong, Hyeon-Ho Lee, Yubin Zhang, Shoujun Cao, Tun Popescu, Cosmin-Constantin Mills, Brian Gu, Tian Fang, Zhuoran Chen, Rui Tong, Hao Wang, Yi He, Qiang Lu, Yitao Liu, Zhiyuan Yu, Han Mandal, Avik Cui, Yihao Ansari, Abbas Sheikh Bhingardive, Viraj Kang, Myungkoo Lai, Choon Kong Merklein, Moritz Mueller, Maximilian J. Song, Young Min Tian, Zhen Hu, Juejun Losurdo, Maria Majumdar, Arka Miao, Xiangshui Chen, Xiao Gholipour, Behrad Richardson, Kathleen A. Eggleton, Benjamin J. Sharda, Kanudha Wuttig, Matthias Singh, Ranjan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore 637371 Singapore Centre for Disruptive Photonic Technologies The Photonic Institute 50 Nanyang Avenue Singapore 639798 Singapore Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A∗STAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Republic of Singapore School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju 61005 Republic of Korea School of Materials Science and Engineering Gwangju Institute of Science and Technology Gwangju 61005 Republic of Korea DELL Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering Key Laboratory of Optoelectronic Information Technology (Ministry of Education of China) Tianjin University Tianjin 300072 China DELL School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian 116024 China Department of Materials Science &amp Engineering Massachusetts Institute of Technology Cambridge MA USA Materials Research Laboratory Massachusetts Institute of Technology Cambridge MA USA Department of Electrical &amp Computer Engineering University of Washington Washington Seattle USA Wuhan National Research Center for Optoelectronics School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Institute of Advanced Materials Beijing Normal University Beijing 100875 China Nanoscale Optics Lab ECE Department University of Alberta Edmonton Canada CREOL College of Optics and Photonics University of Central Florida Orlando FL USA Institute of Photonics and Optical Science (IPOS) School of Physics The University of Sydney New South Wales NSW 2006 Australia The University of Sydney Nano Institute (Sydney Nano) The University of Sydney New South Wales NSW 2006 Australia Insti

Phase Change Materials (PCMs) have demonstrated tremendous potential as a platform for achieving diverse functionalities in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum, ranging from terahertz to visible frequencies. This comprehensive roadmap reviews the material and device aspects of PCMs, and their diverse applications in active and reconfigurable micro-nanophotonic devices across the electromagnetic spectrum. It discusses various device configurations and optimization techniques, including deep learning-based metasurface design. The integration of PCMs with Photonic Integrated Circuits and advanced electric-driven PCMs are explored. PCMs hold great promise for multifunctional device development, including applications in non-volatile memory, optical data storage, photonics, energy harvesting, biomedical technology, neuromorphic computing, thermal management, and flexible electronics.

关键词： Physics Applied physics Materials application

来源：评论

学校读者我要写书评

暂无评论

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

arXiv

引用

arXiv 2022年

作者： Nwoye, Chinedu Innocent Alapatt, Deepak Yu, Tong Vardazaryan, Armine Xia, Fangfang Zhao, Zixuan Xia, Tong Jia, Fucang Yang, Yuxuan Wang, Hao Yu, Derong Zheng, Guoyan Duan, Xiaotian Getty, Neil Sanchez-Matilla, Ricardo Robu, Maria Zhang, Li Chen, Huabin Wang, Jiacheng Wang, Liansheng Zhang, Bokai Gerats, Beerend Raviteja, Sista Sathish, Rachana Tao, Rong Kondo, Satoshi Pang, Winnie Ren, Hongliang Abbing, Julian Ronald Sarhan, Mohammad Hasan Bodenstedt, Sebastian Bhasker, Nithya Oliveira, Bruno Torres, Helena R. Ling, Li Gaida, Finn Czempiel, Tobias Vilaça, João L. Morais, Pedro Fonseca, Jaime Egging, Ruby Mae Wijma, Inge Nicole Qian, Chen Bian, Guibin Li, Zhen Balasubramanian, Velmurugan Sheet, Debdoot Luengo, Imanol Zhu, Yuanbo Ding, Shuai Aschenbrenner, Jakob-Anton van der Kar, Nicolas Elini Xu, Mengya Islam, Mobarakol Seenivasan, Lalithkumar Jenke, Alexander Stoyanov, Danail Mutter, Didier Mascagni, Pietro Seeliger, Barbara Gonzalez, Cristians Padoy, Nicolas ICube University of Strasbourg CNRS France IHU Strasbourg France Department of Computer Science University of Chicago United States Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Management Hefei University of Technology Hefei China Institue of Medical Robotics Shanghai Jiao Tong University Shanghai China Argonne National Laboratory 9700 S Cass Ave LemontIL60439 United States Digital Surgery A Medtronic Company London United Kingdom Institute of Automation Chinese Academy of Sciences China Department of Computer Science School of Informatics Xiamen University Xiamen China Johnson & Johnson Meander Medical Centre Netherlands Indian Institute of Technology Kharagpur India Muroran Institute of Technology Japan Department of Biomedical Engineering National University of Singapore Singapore Department for Translational Surgical Oncology National Center for Tumor Diseases Partner Site Dresden Germany 2Ai School of Technology IPCA Barcelos Portugal School of Medicine University of Minho Braga Portugal Algoritimi Center School of Engineering University of Minho Guimeraes Portugal Technical University of Munich Germany Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Wellcome EPSRC Center for Interventional and Surgical Science University College London United Kingdom Fondazione Policlinico Universitario Agostino Gemelli IRCCS Rome Italy University Hospital of Strasbourg France

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity;yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of combination delivers comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms by competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery. © 2022, CC BY-NC-SA.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Author Correction:Data augmentation in microscopic images for material data mining

引用

npj Computational Materials 2020年第1期6卷 476-476页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China School of Materials Science and Engineering University of Science and Technology Beijing Beijing China School of Materials Science and Technology Liaoning Technical University Liaoning China The Institute of Statistical Mathematics Research Organization of Information and Systems Tachikawa Tokyo Japan National Intellectual Property Administration Beijing China Faculty of Engineering Sciences University of Burundi Bujumbura Burundi Faculty of Engineering and Technology Palestine Technical University – Kadoorie Tulkarem Palestine College of Information Science and Engineering China University of Petroleum Beijing China Key Lab of Petroleum Data Mining China University of Petroleum Beijing China

The original version of this article omitted the following from the Acknowledgements:“This work was supported by Beijing Top Discipline for Artificial Intelligent Science and engineering,University of Science and Tec... 详细信息

关键词： HTML knowledge mining

来源：评论

学校读者我要写书评

暂无评论

Sensitive and selective ozone sensor based on CuCo2O4 synthesized by a facile solution combustion method

SSRN

引用

SSRN 2022年

作者： Zhao, Zhongyao Deng, Zanhong Zhang, Ruofan Klamchuen, Annop He, Yong Horprathum, Mati Chang, Junqing Mi, Longqing Li, Meng Wang, Shimao Fang, Xiaodong Meng, Gang School of Environmental Science and Optoelectronic Technology University of Science and Technology of China Hefei230026 China Anhui Provincial Key Laboratory of Photonic Devices and Materials Anhui Institute of Optics and Fine Mechanics Key Lab of Photovoltaic and Energy Conservation Materials Chinese Academy of Sciences Hefei230031 China Advanced Laser Technology Laboratory of Anhui Province Hefei230037 China National Nanotechnology Center National Science and Technology Development Agency Pathum Thani 12120 Thailand Key Laboratory of Optoelectronic Technology and Systems The Education Ministry of China College of Optoelectronic Engineering Chongqing University Chongqing400044 China Opto-Electrochemical Sensing Research Team Spectroscopic and Sensing Devices Research Group National Electronics and Computer Technology Center Pathum Thani 12120 Thailand College of New Materials and New Energies Shenzhen Technology University Shenzhen518118 China Shenzhen Shengfang Technology Company Limited Shenzheng518116 China

In this study, p-type spinel CuCo2O4 nanomaterials with abundant oxygen vacancies were synthesized by a facile one-step solution combustion method, and their sensing characteristics have been investigated. The p-type CuCo2O4 sensor shows a high response of 11 to 1 ppm O3 and low responses (3, NO2, SO2, and 1000 ppm ethanol, methanol and acetone) at 90 °C in dry air, indicating a superior selectivity. The sensor also possesses a wide O3 detection range from 10 to 3000 ppb and good repeatability. More importantly, the relative humidity (RH) could promote the electrical response to O3, and the response to 1 ppm O3 only deviates from 24 to 27 as the RH increases from 50% to 90%, benefitting for practical applications in ambient humid air atmospheres. The humidity enhanced response for p-type CuCo2O4 could be ascribed to the competition of hole carrier reduction, O2− active sites reduction and O3 decomposition promotion induced by the hydroxyl groups. © 2022, The Authors. All rights reserved.

关键词： Copper compounds

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：