Design of high-performance plasmonic nanosensors by particle swarm optimization algorithm combined with machine learning
Design of high-performance plasmonic nanosensors by particle swarm optimization algorithm combined with machine learning
复制标题
粒子群优化算法结合机器学习设计高性能等离子体纳米传感器
DOI：
10.1088/1361-6528/ab95b8
复制
发表时间：
2020-05
期刊：
影响因子：
3.5
通讯作者：
Yue Xinzhao
中科院分区：
文献类型：
--
作者：
Yan Ruoqin;Wang Tao;Jiang Xiaoyun;Zhong Qingfang;Huang Xing;Wang Lu;Yue Xinzhao
超灵敏、无标记且能实时运行的金属等离子体纳米传感器在化学和生物研究领域极具潜力。传统上，这些纳米结构的设计在很大程度上依赖于耗时的……（原文此处“ele”不完整，推测可能是“electromagnetic simulations”之类表述，完整意思需结合完整原文确定 ）
Metallic plasmonic nanosensors that are ultra-sensitive, label-free, and operate in real time hold great promise in the field of chemical and biological research. Conventionally, the design of these nanostructures has strongly relied on time-consuming electromagnetic simulations that iteratively solve Maxwell’s equations to scan multi-dimensional parameter space until the desired sensing performance is attained. Here, we propose an algorithm based on particle swarm optimization (PSO), which in combination with a machine learning (ML) model, is used to design plasmonic sensors. The ML model is trained with the geometric structure and sensing performance of the plasmonic sensor to accurately capture the geometry-sensing performance relationships, and the well-trained ML model is then applied to the PSO algorithm to obtain the plasmonic structure with the desired sensing performance. Using the trained ML model to predict the sensing performance instead of using complex electromagnetic calculation methods allows the PSO algorithm to optimize the solutions fours orders of magnitude faster. Implementation of this composite algorithm enabled us to quickly and accurately realize a nanoridge plasmonic sensor with sensitivity as high as 142,500 nm/RIU. We expect this efficient and accurate approach to pave the way for the design of nanophotonic devices in future.
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DOI：
10.1016/b978-0-12-813724-6.00039-6
发表时间：
2020
期刊：
Information Resources in Toxicology
影响因子：
--
作者：
Sara T. Humes;T. Sabo-Attwood
通讯作者：
Sara T. Humes;T. Sabo-Attwood
影响因子：
3.6
作者：
Shokooh-Saremi, Mehrdad;Magnusson, Robert
通讯作者：
Magnusson, Robert
影响因子：
7.5
作者：
Yao K;Unni R;Zheng Y
通讯作者：
Zheng Y
DOI：
10.1016/b978-0-12-821233-2.00012-5
发表时间：
2021
期刊：
Sustainable Production Technology in Food
影响因子：
--
作者：
P. E. Munekata;M. Pateiro;Rubén Domínguez;M. Farag;T. Varzakas;José M. Lorenzo
通讯作者：
P. E. Munekata;M. Pateiro;Rubén Domínguez;M. Farag;T. Varzakas;José M. Lorenzo
影响因子：
3.8
作者：
Asano, Takashi;Noda, Susumu
通讯作者：
Noda, Susumu