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高熵能源前沿论坛-51 Finding a Needle in a Haystack - Towards Highly Sensitive Optical Diagnostics
发表日期: 2024-06-24 文章来源:
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  报告人: 赵弈平  University of Georgia  

  报告时间:202462110:00 

  报告地点:科研楼一楼报告厅 

  联系人: 人教办 

  报告人简介: 

  Dr. Yiping Zhao received his B.S. degree in Electronics from Peking University in 1991, and MS degree in condensed matter physics from Institute of Semiconductors, Chinese Academy of Sciences in 1994. He obtained his Ph.D. degree in Physics at Rensselaer Polytechnic Institute in 1999. He is currently a Distinguished Research Professor at the Department of Physics and Astronomy in University of Georgia, Fellow of  SPIE (the International Society of Optics and Photonics), and Fellow of AVS (American Vacuum Society).  Prof. Zhao is the author or co-author of more than 330 peer reviewed journal papers, 30 conference proceeding papers, 3 books, 9 book chapters, and 12 US patents. His major research interests are nanostructures and thin films fabrication and characterization, plasmonic nanostructures, chemical and biological sensors, nano-photocatalysts, antimicrobial materials, nanomotors, and nanotechnology for stroke treatment. 

  报告摘要: 

  Advanced sensing techniques are central in infectious disease diagnostics, food safety and security, sustainable agriculture, as well as renewable energy. Surface-enhanced Raman scattering (SERS) is a powerful analytical tool for chemical and biological sensing applications since its discovery in the 1970’s. One hurdle that has limited SERS biosensing applications is the ability to produce uniform, highly sensitive, and reproducible SERS substrates. In this talk, I will discuss the invention of the Ag nanorod (AgNR) array SERS substrates fabricated by oblique angle deposition, which overcomes the limitation and provides an unprecedented opportunity to practical SERS based sensors for various applications. The optimization of the structural design, the effect of the optical configuration, and the possible enhancement mechanism of the AgNR substrates will be reviewed systematically and various AgNR based sensing devices developed will be given. Finally, our recent results on the combination of AgNR-based SERS sensors and machine learning/deep learning methods for multi-virus detection, especially COVID-19 detection from real patient specimens, will be presented. 

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