Scholarlink

<p>A highly sensitive photonic crystal fiber (PCF) refractive index (RI) sensor based on surface plasmon resonance (SPR) has been proposed and evaluated numerically. The sensor features a solid silica core embedded within a periodic air-hole structure and microchannels coated in gold to improve plasmonic coupling. Numerical analysis using the finite element method (FEM) indicates that the sensor exhibits a clear redshift in resonance wavelength as the analyte RI goes up. Over the RI range of 1.37–1.42, it achieves a maximum sensitivity of 2800&#xa0;nm/RIU and a resolution of 3.5 × 10<sup>− 5</sup> RIU. The suggested sensor’s small size and excellent sensitivity make it ideal for advanced biological, chemical, and environmental monitoring. The results of the SPR-based PCF RI sensor are predicted through comparative analysis of several machine learning models, such as Support vector regression, Random Forest regression, and Extreme gradient boosting approaches.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Microfluidic-Integrated Gold-Coated SPR Photonic Crystal Fiber for Refractive Index Sensing with Machine Learning-Based Analysis

  • 期刊论文

  • 发表日期：

    2026年7月9日

查看全文
  • Dhananjay Prajapati,
  • Dharmendra Kumar,
  • Beaulah Nath,
  • Vijay Shanker Chaudhary,
  • Sudakar Singh Chauhan,
  • Sneha Sharma,
  • Santosh Kumar

摘要

A highly sensitive photonic crystal fiber (PCF) refractive index (RI) sensor based on surface plasmon resonance (SPR) has been proposed and evaluated numerically. The sensor features a solid silica core embedded within a periodic air-hole structure and microchannels coated in gold to improve plasmonic coupling. Numerical analysis using the finite element method (FEM) indicates that the sensor exhibits a clear redshift in resonance wavelength as the analyte RI goes up. Over the RI range of 1.37–1.42, it achieves a maximum sensitivity of 2800 nm/RIU and a resolution of 3.5 × 10− 5 RIU. The suggested sensor’s small size and excellent sensitivity make it ideal for advanced biological, chemical, and environmental monitoring. The results of the SPR-based PCF RI sensor are predicted through comparative analysis of several machine learning models, such as Support vector regression, Random Forest regression, and Extreme gradient boosting approaches.