Evidence map›Paper›PMID 41870732›Full record

ArticleMikrochimica acta2026

Ultra-sensitive surface plasmon resonance biosensor for non-invasive diabetes monitoring based on 2D nanomaterial heterostructures.

Soumo Paul, Tonima Ghosh, Raihan Ahmed, Faysal Nayan

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Article in Mikrochimica acta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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5 · Who and what money

Authors and funding

4 authors.

Soumo PaulDepartment of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh.
Tonima GhoshDepartment of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh.
Raihan AhmedDepartment of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh.
Faysal NayanDepartment of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh. faysal.nayan.eee@aust.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A highly sensitive surface plasmon resonance (SPR)-based biosensor is introduced designed for the non-invasive detection of diabetes levels using urine samples. The proposed multilayer sensor architecture comprises a CaF2 glass prism, a 12 nm ZnO adhesion layer, a 40 nm Ag plasmonic film, a 2 nm TiO2 dielectric layer, a 0.75 nm BlueP/WS2 heterostructure, and the sensing medium (urine). The sensor is engineered to detect glucose concentrations ranging from 0 to 15 mg/dl (non-diabetic) to 10 g/dl (severely diabetic), corresponding to refractive index values between 1.335 and 1.347. Structural optimization was performed via the Transfer Matrix Method (TMM) at a wavelength of 633 nm, targeting key performance indicators such as angular sensitivity, figure of merit (FOM), full width at half maximum (FWHM), detection accuracy (DA), limit of detection (LOD), and quality factor (QF). To validate the TMM results, complementary Finite Element Method (FEM) simulations were carried out. The optimized biosensor demonstrated a peak angular sensitivity of 530 Deg/RIU. This result, to the best of our knowledge, represents the highest sensitivity reported to date for urine-based diabetes detection, while achieving a minimum reflectivity of 0.11622. The additional performance metrics achieved include an FOM of 103.721, FWHM of 4.516°, DA of 0.221 Deg-1, LOD of 1.887 × 10− 6, and a QF of 117.360 RIU-1. These findings underscore the proposed sensor’s potential for accurate, real-time glucose monitoring and highlight its promise for application in advanced clinical diagnostics.

Indexed as

Biosensing TechniquesDiabetes MellitusNanostructuresSurface Plasmon ResonanceHumansLimit of DetectionTitaniumZinc OxideTitaniumZinc OxideBlue phosphorus (BlueP)Finite element method (FEM)Multilayer sensorSurface plasmon resonance (SPR)Transfer matrix method (TMM)Transition metal dichalcogenides (TMDCs)

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.