{
  "abstract": "Replacing toxic lead in perovskite-enhanced surface plasmon resonance (SPR) biosensors is critical for environmentally sustainable sensing, yet the performance of lead-free alternatives in multilayer architectures remains poorly characterized. We numerically investigate a lead-free perovskite/HfSe2 SPR biosensor within a CaF2/Ag/perovskite/HfSe2/sensing-medium Kretschmann stack. Four perovskites are evaluated: MAPBr3 as a Pb-based benchmark and the lead-free candidates Cs2AgBiBr6, Cs2AgBiCl6, and CsSnBr3. The optical response is calculated at 633 nm using a recursive Fresnel transfer-matrix method. Two refractive-index windows are optimized independently: a generic aqueous interval (1.3300–1.3350) and a bio/SARS-like interval (1.3348–1.3398). A multi-metric optimization framework balancing angular sensitivity, figure of merit (FoM), resonance depth, and linewidth is employed to identify balanced sensor designs. For the generic sensing interval, the optimal balanced lead-free design uses Cs2AgBiCl6 with Ag/Cs2AgBiCl6/HfSe2 thicknesses of 47/3.0/1.0 nm, achieving a sensitivity of 397.0 deg RIU−1 and a FoM of 103.66 RIU−1. For the bio/SARS-like interval, the optimal balanced design switches to CsSnBr3 (43/2.5/1.0 nm), providing 447.8 deg RIU−1 sensitivity and a FoM of 90.05 RIU−1, while MAPBr3 remains the highest-sensitivity material overall. Complementary Monte Carlo robustness analysis, local machine-learning surrogates, electric-field visualization, and layer-isolation studies are used to interpret the optimized sensor response. Our results demonstrate that optimal material selection is regime-dependent: Cs2AgBiCl6 performs best for generic aqueous sensing, whereas CsSnBr3 is superior for bio-relevant indices, providing application-specific design guidelines for lead-free SPR biosensors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "name": "Sadok Kouz"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7jpzzjs769465.bib",
  "cited_by": 0,
  "doi": "10.1016/j.sbsr.2026.101077",
  "fulltext_url": null,
  "id": "7jpzzjs769465",
  "issns": [
    "2214-1804"
  ],
  "landing_url": "https://hal.science/hal-05837688v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.phys"
    ],
    [
      "hal",
      "0.spi"
    ],
    [
      "hal",
      "1.phys.cond"
    ],
    [
      "hal",
      "1.spi.opti"
    ],
    [
      "hal",
      "2.phys.cond.cm-ms"
    ],
    [
      "openalex",
      "2204|Plasmonic and Surface Plasmon Research"
    ]
  ],
  "openalex": "W7203755363",
  "pdf_url": "https://hal.science/hal-05837688/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-19",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/7jpzzjs769465.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:01Z",
      "native_id": "hal-05837688",
      "source": "hal",
      "url": "https://hal.science/hal-05837688v1"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "hal:1.phys.cond",
      "subfield": "condensed-matter"
    }
  ],
  "title": "Lead-free perovskite/HfSe2 surface plasmon resonance biosensor design via multi-metric optimization, Monte Carlo robustness analysis, and machine learning",
  "title_text": "Lead-free perovskite/HfSe2 surface plasmon resonance biosensor design via multi-metric optimization, Monte Carlo robustness analysis, and machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:01Z",
  "url": "https://research.draconitic.com/paper/7jpzzjs769465",
  "venue": "Sensing and Bio-Sensing Research",
  "versions": []
}