Multilayer Ag/AlPO4/graphene/WS2/MoS2/black phosphorus surface plasmon resonance biosensor for highly sensitive COVID-19 antigen and amino acid detection with machine learning-assisted performance validation

Rapid, sensitive, and reliable biomolecular detection is essential for screening, early disease detection, and point-of-care diagnostics, while conventional analytical techniques often require complex instrumentation and time-consuming procedures. This study proposes a multilayer surface plasmon resonance (SPR) biosensor based on the Kretschmann configuration for COVID-19 viral antigen and amino acid detection. The proposed architecture incorporates silver (Ag), aluminum phosphate (AlPO 4 ), graphene, tungsten disulfide (WS 2 ), molybdenum disulfide (MoS 2 ), and black phosphorus (BP) on a BK7 prism to enhance plasmonic coupling, light–matter interaction, and sensing stability. The proposed biosensor was numerically evaluated using COMSOL Multiphysics to assess its sensing performance for both COVID-19 viral antigens and amino acids. For COVID-19 detection, the optimized sensor achieved an angular sensitivity of 320°/RIU with a corresponding figure of merit (FOM) of 86.486 RIU −1 . In the case of amino acid sensing, the sensor exhibited significantly enhanced performance, reaching a maximum sensitivity of 766.667°/RIU and an FOM of 123.656 RIU −1 . Random Forest Regression achieved R 2 > 0.998, confirming excellent predictive accuracy. These results highlight the proposed biosensor as a promising platform for rapid screening, early disease detection, and real-time biomedical and biochemical sensing applications.

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Publication Details

Journal
Materials Today Advances
Published
2026-07-28
DOI
https://doi.org/10.1016/j.mtadv.2026.100900
Primary Topic
SARS-CoV-2 detection and testing
Type
article
Field-Weighted Citation Impact
0.00

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article

Multilayer Ag/AlPO4/graphene/WS2/MoS2/black phosphorus surface plasmon resonance biosensor for highly sensitive COVID-19 antigen and amino acid detection with machine learning-assisted performance validation

Habib Kraiem, Pankaj Pathak, U. Arun Kumar, Aymen Flah et al.
Materials Today Advances
SARS-CoV-2 detection and testing
article

Multilayer Ag/AlPO4/graphene/WS2/MoS2/black phosphorus surface plasmon resonance biosensor for highly sensitive COVID-19 antigen and amino acid detection with machine learning-assisted performance validation

Habib Kraiem, Pankaj Pathak, U. Arun Kumar, Aymen Flah, P. Nagarajan
article en

Abstract

Rapid, sensitive, and reliable biomolecular detection is essential for screening, early disease detection, and point-of-care diagnostics, while conventional analytical techniques often require complex instrumentation and time-consuming procedures. This study proposes a multilayer surface plasmon resonance (SPR) biosensor based on the Kretschmann configuration for COVID-19 viral antigen and amino acid detection. The proposed architecture incorporates silver (Ag), aluminum phosphate (AlPO 4 ), graphene, tungsten disulfide (WS 2 ), molybdenum disulfide (MoS 2 ), and black phosphorus (BP) on a BK7 prism to enhance plasmonic coupling, light–matter interaction, and sensing stability. The proposed biosensor was numerically evaluated using COMSOL Multiphysics to assess its sensing performance for both COVID-19 viral antigens and amino acids. For COVID-19 detection, the optimized sensor achieved an angular sensitivity of 320°/RIU with a corresponding figure of merit (FOM) of 86.486 RIU −1 . In the case of amino acid sensing, the sensor exhibited significantly enhanced performance, reaching a maximum sensitivity of 766.667°/RIU and an FOM of 123.656 RIU −1 . Random Forest Regression achieved R 2 > 0.998, confirming excellent predictive accuracy. These results highlight the proposed biosensor as a promising platform for rapid screening, early disease detection, and real-time biomedical and biochemical sensing applications.

Materials Today AdvancesVol. 31
Northern Border University (SA), National Institute of Technology Tiruchirappalli (IN), VSB - Technical University of Ostrava (CZ), Symbiosis International University (IN), University of Business and Technology (SA), Karpagam Academy of Higher Education (IN)
Vysoká Škola Bánská - Technická Univerzita Ostrava, Northern Border University
Openalex Percentile: Top 9%
SARS-CoV-2 detection and testing
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