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<p>Early and precise identification of urinary biomarkers plays a vital role in the diagnosis of the kidney, metabolic, and systemic diseases. This paper has conducted a design using a simulation of a plasmonic photonic crystal fiber (PCF) biosensor that uses hybrid plasmonics of ZnO -Au. The aim is to explore the achievements of the ZnO dielectric spacer in influencing localized surface plasmon resonance (LSPR), materializing field localization and stabilizing sensor functionality as per physiological constraints. The proposed sensor has a high sensitivity of 5300&#xa0;nm/RIU and a detection limit of 0.002&#xa0;mg/dL with a large sensitivity to clinically relevant biomarkers such as creatinine, uric acid, glucose, albumin, and ketones, which are three simulations using FEM. ZnO–Au integration also elicits increase in sharpness of resonance, biocompatibility and stability, where performance reliability is realized at different concentrations of analytes and refractive indices. The comparison with the traditional plasmonic biosensors suggests the high accuracy, reduction down to miniature dimensions, and the possibility of point of care use. On balance, this paper presents with a sound, simulation-validated platform of non-invasive urinary-based health diagnostics, which may guide future experimental and clinical implementation.</p> Graphical Abstract <p></p>

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Modelling a Plasmonic ZnO-Integrated Photonic Sensor for Non-invasive Urinary Health Diagnostics

  • 期刊论文

  • 发表日期：

    2025年12月2日

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  • E. P. Prakash,
  • K. Srihari,
  • S. Karthik

摘要

Early and precise identification of urinary biomarkers plays a vital role in the diagnosis of the kidney, metabolic, and systemic diseases. This paper has conducted a design using a simulation of a plasmonic photonic crystal fiber (PCF) biosensor that uses hybrid plasmonics of ZnO -Au. The aim is to explore the achievements of the ZnO dielectric spacer in influencing localized surface plasmon resonance (LSPR), materializing field localization and stabilizing sensor functionality as per physiological constraints. The proposed sensor has a high sensitivity of 5300 nm/RIU and a detection limit of 0.002 mg/dL with a large sensitivity to clinically relevant biomarkers such as creatinine, uric acid, glucose, albumin, and ketones, which are three simulations using FEM. ZnO–Au integration also elicits increase in sharpness of resonance, biocompatibility and stability, where performance reliability is realized at different concentrations of analytes and refractive indices. The comparison with the traditional plasmonic biosensors suggests the high accuracy, reduction down to miniature dimensions, and the possibility of point of care use. On balance, this paper presents with a sound, simulation-validated platform of non-invasive urinary-based health diagnostics, which may guide future experimental and clinical implementation.

Graphical Abstract