Scholarlink

<p>This work integrates computational simulations with a hybrid machine learning framework to investigate the nonlinear relationships between plasmonic layer geometry, refractive index variations, and spectral response in a photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor. The proposed approach achieves reliable detection of small refrative index chances from a simple yet optimized PCF SPR sensing structure, reaching competitive sensitivity levels in the refractive index range of 1.33–1.39. Accurate predictions were obtained with <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^{2}&gt; 0.99\)</EquationSource> </InlineEquation> and minimal error (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\epsilon &lt; 0.1\)</EquationSource> </InlineEquation>). A central contribution of this work is the simultaneous optimization of multiple optical metrics. Beyond maximizing wavelength sensitivity, the methodology balances sensitivity, figure of merit, Q-factor, and FWHM. This multiobjective strategy enables precise tailoring of the plasmonic layer geometry, producing sharp resonances, high-quality factors, and robust performance. Overall, the results demonstrate how plasmonic engineering in photonic crystal fibers can drive high-performance SPR sensing platforms. The methodology provides valuable insights into the geometry–plasmonics interplay while opening avenues for practical implementations in biochemical detection, environmental monitoring, and chemical sensing.</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

Shaping photonic crystal fibers: geometric optimization for SPR sensor performance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月27日

查看全文
  • Amanda F. Romeiro,
  • Anderson O. Silva,
  • João C. W. A. Costa,
  • Maria T. R. Giraldi,
  • A. Guerreiro,
  • José L. Santos

摘要

This work integrates computational simulations with a hybrid machine learning framework to investigate the nonlinear relationships between plasmonic layer geometry, refractive index variations, and spectral response in a photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor. The proposed approach achieves reliable detection of small refrative index chances from a simple yet optimized PCF SPR sensing structure, reaching competitive sensitivity levels in the refractive index range of 1.33–1.39. Accurate predictions were obtained with \(R^{2}> 0.99\) and minimal error ( \(\epsilon < 0.1\) ). A central contribution of this work is the simultaneous optimization of multiple optical metrics. Beyond maximizing wavelength sensitivity, the methodology balances sensitivity, figure of merit, Q-factor, and FWHM. This multiobjective strategy enables precise tailoring of the plasmonic layer geometry, producing sharp resonances, high-quality factors, and robust performance. Overall, the results demonstrate how plasmonic engineering in photonic crystal fibers can drive high-performance SPR sensing platforms. The methodology provides valuable insights into the geometry–plasmonics interplay while opening avenues for practical implementations in biochemical detection, environmental monitoring, and chemical sensing.