转移学习授权多指标优化设计用于特拉赫兹近界状态在连续性的生物传感器
Shengfeng Wang1, Bingwei Liu1, Xu Wu1
1Shidong Hospital Affiliated to University of Shanghai for Science and Technology, Terahertz Technology Innovation Research Institute, Shanghai Key Lab of Modern Optical System, Shanghai Institute of Intelligent Science and Technology, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai, Shanghai, 200093, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|April 27, 2025
概括
这项研究引入了一种新的转移学习方法,用于设计太赫兹超表面生物传感器,同时优化多个性能指标. 这种方法使得超灵敏的检测生物标志物,如同类半氨酸与减少的数据需求.
科学领域:
- 生物医学工程 生物医学工程
- 纳米技术纳米技术
- 光学是什么?光学是什么?光学是什么?
背景情况:
- 太赫兹超表面生物传感器利用连续性的准束状态 (QBIC) 实现无标签,快速和超敏感的生物医学检测.
- 深度学习已经推进了 metasurface 设计,但现有的方法往往忽视了关键性能指标之间的权衡.
- 同时优化多个指标,如质量 (Q) 因素,优点数 (FoM) 和有效传感面积 (ESA),对于卓越的生物传感器性能至关重要.
研究的目的:
- 开发一种新的转移学习方法,以优化太赫兹超表面生物传感器设计中的多个性能指标.
- 为了实现Q因子,FoM和ESA的全面优化,以提高生物传感能力.
- 展示设计高度敏感和高效的超表面生物传感器的新范式.
主要方法:
- 采用了两阶段的转移学习策略,包括对低维数据集的预训练和对高维任务的微调.
- 使用频率转移作为统一的标准来量化每个绩效指标的贡献.
- 该方法通过设计生物传感器来检测同型半氨酸来验证.
主要成果:
- 转移学习方法实现了Q因子 (26.09%),FoM (48.42%) 和ESA (25.49%) 的同时优化.
- 与传统的深度学习方法相比,数据需求减少了50%.
- 设计的生物传感器证明了在ngμL-1水平上检测同型半氨酸,实验结果与理论预测一致.
结论:
- 这项研究提出了一种使用转移学习的多指标优化方法,用于超表面生物传感器设计中的多指标优化.
- 开发的方法显著提高了生物传感器的性能,并减少了数据需求.
- 这项工作为先进的微生物检测技术铺平了道路.
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