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作者： Boerio, F. James Dionysiou, Dionysios D. Papautsky, Ian Pelaez, Miguel Schulz, Mark Huth, Christopher Shanov, Vesselin N. Shi, Donglu Department of Materials Science and Engineering University of Cincinnati OH United States Department of Environmental Engineering and Science University of Cincinnati OH United States School of Electronics and Computing Systems University of Cincinnati OH United States Environmental Engineering Program UC United States School of Energy Environmental Biological and Medical Engineering University of Cincinnati OH United States

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Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

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Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

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Faculty characteristics that influence student performance in the first two years of engineering 123

Faculty characteristics that influence student performance i...

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123rd ASEE Annual Conference and Exposition

作者： Smith, Bethany B. Park, Yong-Seok Ross, Lydia Krause, Stephen J. Chen, Ying-Chih Middleton, James A. Judson, Eugene Culbertson, Robert J. Ankeny, Casey Jane Hjelmstad, Keith D. Yan, Claire Y. Materials Science Program Fulton School of Engineering Arizona State University United States Division of Teacher Preparation Mary Lou Fulton Teachers College Arizona State University TempeAZ United States Center for Research on Education in Science Mathematics Engineering and Technology Arizona State University United States School of Biological and Health Systems Engineering Arizona State University United States Department of Civil Engineering School of Sustainable Engineering and the Built Environment Arizona State University United States School of Engineering University of British Columbia Okanaga Canada

This is an evidence-based paper based on research that has shown faculty beliefs influence their classroom practices and reformed teaching methods like engagement teaching improve student performance and retention in science, technology, engineering, and mathematics (STEM) fields. To better understand the relationships between faculty beliefs and practice and student outcomes such as performance and attitudes, three tools were utilized. The first tool is a 24 question guided interview to gauge general beliefs towards teaching;the second is the Approaches to Teaching Inventory (ATI) that measures faculty beliefs towards instructor-centered knowledge transmission and instructor-centered strategies versus student-centered conceptual change intention and strategies. Lastly, the third tool is the Reformed Teaching Observational Protocol (RTOP) which is an observational protocol that quantitatively measures degree of student-centered classroom behaviors. By combining ATI and RTOP scores with emergent theme (ET) analysis on relevant interview questions, faculty characteristics influencing student outcomes can be determined. This work addressees the research questions, "What is the relationship between faculty beliefs and practice?" and "What is the relationship between faculty practice and student outcomes?" 30 faculty members who teach freshman or sophomore level science, math, or engineering courses at a large, southwestern university were interviewed about their teaching beliefs, were surveyed using the ATI, and were observed using the RTOP. Interview questions were analyzed using emergent theme analysis and related to their ATI responses and RTOP scores. The interview question responses were coded numerically as either teacher-centered (-1), student centered (+1), or mixed/neither (0) using the dimensions of the ATI as a basis. The total RTOP scores, the ATI dimension scores, and the sum of the interview ET analyses for every faculty member were then ranked in ascendin

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Connections among university faculty engaged in the first two years of engineering and their impact on faculty attitudes and practice 123

Connections among university faculty engaged in the first tw...

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123rd ASEE Annual Conference and Exposition

作者： Middleton, James A. Krause, Stephen J. Judson, Eugene Culbertson, Robert J. Ross, Lydia Hjelmstad, Keith D. Park, Yong-Seok Collofello, James Smith, Bethany B. Department of Mechanical and Aerospace Engineering United States Center for Research on Education in Science Mathematics Engineering and Technology Arizona State University United States Materials Science Program Fulton School of Engineering Arizona State University United States Arizona State University Mary Lou Fulton Teachers College United States Department of Physics United States Arizona State University Department of Civil Engineering School of Sustainable Engineering and the Built Environment United States Arizona State University Department of Computer Science and Engineering School of Computing Informatics and Decision Systems Engineering Ira A. Fulton Schools of Engineering United States

This research paper examined the connectedness of STEM faculty to others both within and across academic departments who might be potential resources for diffusion of Learner-centered practices, and the impact of participants' social networks on their Learner-centered beliefs and practices. The research question under investigation was: To what extent is the degree of social connectedness among faculty within and among STEM departments related to the degree of implementation of Learner-centered practices? Participants were recruited from Physics, Chemistry, Mathematics departments, and from departments in the College of engineering. The sample began with 21 randomly-selected faculty from each department engaged in the STEM instruction of first and second year engineering students. The second level consisted of the colleagues identified by the first level as people they utilize as resources for improving their instruction. The connectedness of faculty in the network was examined using network analysis, focusing on both number of connections, and depth of connections. Attitude and Observation Protocol measures were used as outcomes. Results show that faculty classified as Learner-Centered had deeper and more extensive social networks. Results are discussed in terms of the need for intra- and inter-departmental faculty professional development experiences that both introduce and encourage faculty to trial new tools and techniques, but that also run for the longterm, supporting collaborative organizations of faculty working together to transform early engineering experiences. © American Society for engineering Education, 2016.

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Adaptive navigation systems for an unmanned surface vehicle

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Proceedings of the Institute of Marine engineering, science and Technology Part A: Journal of Marine engineering and Technology 2011年第3期10卷 3-20页

作者： Sutton, R. Sharma, S. Marine and Industrial Dynamic Analysis Research Group Advanced Materials and Engineering Systems School of Marine Science and Engineering Plymouth University United Kingdom New and Renewable Energy Group School of Engineering and Computing Sciences Durham University United Kingdom

This paper reports the design of two potential navigation systems for use in an unmanned surface vehicle (USV) named *** approaches adopted are based on fuzzy multisensor data fusion (MSDF) and multiple model adaptive estimation (MMAE) algorithms with adaptive capabilities. A general description of the Springer USV is given along with details of its navigation sensor suite. Of particular interest are the three different types of electronic compass used to supply heading information. Using a system identification technique, state space models of the compasses are derived for use in a simulation study to compare the navigation systems. From the results presented, it is concluded the fuzzy MSDF algorithm is better than the MMAE methodology in terms of heading (yaw error) accuracy and robustness.

关键词： Unmanned surface vehicles

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Fluorescence of CdSe/ZnS quantum dots in toluene: effect of cyclic temperature

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Energy Procedia 2019年 158卷 5895-5900页

作者： Ying Chen Weiling Luan Shaofu Zhang Fuqian Yang Key Laboratory of Pressure Systems and Safety (MOE) School of Mechanical and Power Engineering East China University of Science and Technology Shanghai 200237 P.R. China Materials Program Department of Chemical and Materials Engineering University of Kentucky Lexington KY 40513 USA

Quantum dots (QDs) are the potential material for the application in optical thermometry, and have been successfully applied to solar cells, LEDs, bio-labeling, structural health monitoring, etc. In this paper, we study the fluorescence properties of CdSe/ZnS QDs in toluene under the action of heating-cooling cycles. The experimental results show that, in a heating-cooling cycle, increasing temperature causes red-shift of the emission peak and the decrease of the PL intensity, and decreasing temperature causes blue-shift of the emission peak and the increase of the PL intensity. The surface structures of the QDs likely are dependent on the cycle numbers, which cause the change of the excited energy state of the QDs in toluene. The results presented in this paper reveals the strong effects of cyclic temperature on the photoluminescence characteristics of QDs.

关键词： QDs heating-cooling photoluminescence toluene

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Pore structure and bifunctional catalyst activity of overlayers applied by atomic layer deposition on copper nanoparticles

Pore structure and bifunctional catalyst activity of overlay...

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Fuels and Petrochemicals Division 2014 - Core programming Area at the 2014 AIChE Annual Meeting

作者： Alba-Rubio, Ana C. O'Neill, Brandon J. Shi, Fengyuan Akatay, Cem Canlas, Christian Li, Tao Winans, Randall Elam, Jeffrey W. Stach, Eric A. Voyles, Paul M. Dumesic, James A. Department of Chemical and Biological Engineering University of Wisconsin MadisonWI United States Materials Science Program University of Wisconsin MadisonWI United States School of Materials Engineering Purdue University West LafayetteIN United States Energy Systems Division Argonne National Laboratory ArgonneIL United States X-ray Sciences Division Argonne National Laboratory ArgonneIL United States Center for Functional Materials Brookhaven National Laboratory UptonNY United States

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Work in progress: Measuring dispositions toward teaching strategies and their reported use 123

Work in progress: Measuring dispositions toward teaching str...

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123rd ASEE Annual Conference and Exposition

作者： Judson, Eugene Ross, Lydia Middleton, James A. Krause, Stephen J. Ankeny, Casey Jane Chen, Ying-Chih Culbertson, Robert J. Hjelmstad, Keith D. Park, Yong-Seok Division of Teacher Preparation Mary Lou Fulton Teachers College Arizona State University TempeAZ United States Arizona State University Center for Research on Education in Science Mathematics Engineering and Technology United States Arizona State University Materials Science Program Fulton School of Engineering United States School of Biological and Health Systems Engineering Arizona State University United States Department of Civil Engineering School of Sustainable Engineering and the Built Environment Arizona State University United States Arizona State University STEM Education Research Group United States

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Effect of contextualization of content and concepts on students' course relevance and value in introductory materials classes 123

Effect of contextualization of content and concepts on stude...

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123rd ASEE Annual Conference and Exposition

作者： Krause, Stephen J. Waters, Cindy Stuart, William Joseph Judson, Eugene Ankeny, Casey Jane Smith, Bethany B. Materials Science Program Fulton School of Engineering Arizona State University United States North Carolina A and T State University United States Oregon Institute of Technology United States Mary Lou Fulton Teachers College Arizona State University United States School of Biological and Health Systems Engineering Arizona State University United States Arizona State University United States

Contextualization of a course's content and concepts can improve student motivation, learning, and persistence. In this research eight faculty at four institutions implemented webenabled, engagement and feedback pedagogy in an NSF TUES Type 2 project, JTF (Just-in-Time-Teaching with Interactive Frequent Formative Feedback). A key feature of the pedagogy is contextualization of content and concepts in introductory materials science courses. The theoretical framework used to structure the research is based on principles described in the book How People Learn. The book discusses how cognitive processes act to achieve learning through conceptual change based on three major principles, which include the following. For more effective learning, instructors need to: 1) identify students' prior knowledge to inform instruction;2) engage students to promote conceptual change so they can construct deep knowledge organized in a conceptual framework;and 3) encourage metacognition to build habits of expert learners who define their learning goals and monitor their own progress. The research question is, "What is the role and impact of contextualization of content with respect to student attitude, achievement and persistence." The use of contextualization of content is supported by the three principles. For the first principle, prior knowledge, it has been shown that instruction with contextualized content can activate learners' prior knowledge and promote more effective problem solving. One student said in a reflection, "Relating things to my daily life helped me to retain information better." For the second principle, promoting conceptual change, contextualization of content in interactive classroom engagement activities that motivates students with a concept's relevance can improve learning. One example was a video on precipitation of a supersaturated solution, which improved student learning in a concept quiz on solutions and solubility, from Hake gain of 33% without a video to

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