机器学习增强的多频段元材料传感器用于早期检测神经系统疾病
Asad Miah1, Sams Al Zafir2, Joyonta Das2
1Mymensingh Engineering College, Electrical and Electronic Engineering, Mymensingh, 2200, Bangladesh. asad.mec.eee@gmail.com.
Scientific reports
|February 6, 2026
概括
这项研究引入了一种紧的超材料吸收器,用于早期检测神经疾病. 它在多种频率上提供了高吸收和优异的灵敏度,为先进的医学诊断铺平了道路.
科学领域:
- 超材料科学是一门材料科学.
- 生物医学传感传感器
- 太赫兹技术是太赫兹技术.
背景情况:
- 神经系统疾病需要敏感和早期检测方法.
- 超材料为传感应用提供独特的电磁性质.
研究的目的:
- 开发一个紧的超材料吸收器,用于检测初始的神经疾病.
- 为了在特定的太赫兹频率上实现高吸收和灵敏度.
主要方法:
- 设计和模拟一个方形封闭的双八角形元材料吸收器.
- 分析电场,磁场和表面电流.
- 用于验证的同等电路建模.
- 机器学习的应用 (渐变增强) 用于性能优化和预测.
主要成果:
- 在4.632 THz,6 THz和7.384 THz时实现了>99%的吸收.
- 显示出出色的灵敏度 (1.5-1.8 THz/RIU) 和高的Q因子和FOM值.
- 机器学习以高精度将模拟时间减少了高达60%.
- 在各种大脑组织中成功进行传感测试.
结论:
- 拟议的超材料吸收器在神经疾病检测方面表现出卓越的性能.
- 它的高灵敏度和可靠性使它适合早期诊断.
- 机器学习集成提高了设计效率和预测能力.
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