{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T21:23:40Z","timestamp":1791408220837,"version":"4.3.3"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Integrating machine learning (ML) with nanophotonic engineering, this work achieves unprecedented performance in surface plasmon resonance (SPR) biosensing through a co-designed gold-coated photonic crystal fiber (PCF-SPR) sensor and multi-algorithm computational framework. An asymmetric circular PCF structure with concentric air-hole rings (\n \n \n \n \n \n \n \u039b<\/mml:mi>\n 1<\/mml:mn>\n <\/mml:msub>\n =<\/mml:mo>\n 3.26<\/mml:mn>\n \u00a0<\/mml:mtext>\n \u03bc<\/mml:mi>\n m<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ,\n \n \n \n \n \n \n \u039b<\/mml:mi>\n 2<\/mml:mn>\n <\/mml:msub>\n =<\/mml:mo>\n 2.12<\/mml:mn>\n \u00a0<\/mml:mtext>\n \u03bc<\/mml:mi>\n m<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ) and a 50 nm gold layer maximizes evanescent field-analyte overlap, generating complex spectral signatures ideal for machine learning interpretation. High-fidelity COMSOL Multiphysics simulations produce 1560 synthetic data points across refractive indices (RIs) of 1.33\u20131.38, capturing confinement loss, wavelength sensitivity, and effective permittivity. Three regression models\u2014Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Random Forest Regression (RFR)\u2014are rigorously evaluated for predicting optical responses. The sensor demonstrates a record wavelength sensitivity of 31 846.46 nm\/RIU\n -1<\/jats:sup>\n at\n \n \n \n \n \n \n R<\/mml:mi>\n I<\/mml:mi>\n <\/mml:mrow>\n =<\/mml:mo>\n 1.33<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n , with minimal variation (0.02%) across the biological range, alongside a resolution of\n \n \n \n \n \n 1.57<\/mml:mn>\n \u00d7<\/mml:mi>\n \n 10<\/mml:mn>\n \n \u2212<\/mml:mo>\n 3<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n RIU. Crucially, MLR outperforms nonlinear counterparts, achieving superior accuracy in confinement loss (MAE\u2009=\u20093.97, RMSE\u2009=\u20095.03) and sensitivity prediction (MAE\u2009=\u200940.18, RMSE\u2009=\u200950.54). This synergy of optimized pure-gold microstructures and interpretable machine learning establishes a robust pipeline for high-sensitivity, noise-resilient biosensing, surpassing prior ML-enhanced plasmonic sensors in critical performance metrics while simplifying fabrication.\n <\/jats:p>","DOI":"10.1371\/journal.pone.0343113","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T17:47:16Z","timestamp":1773424036000},"page":"e0343113","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning-driven optimization of monolithic gold plasmonic sensors: Achieving ultrahigh sensitivity with interpretable linear 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