학술논문

Optimization of Novel 2D Material Based SPR Biosensor Using Machine Learning
Document Type
Periodical
Source
IEEE Transactions on NanoBioscience IEEE Trans.on Nanobioscience NanoBioscience, IEEE Transactions on. 23(2):328-335 Apr, 2024
Subject
Bioengineering
Components, Circuits, Devices and Systems
Biosensors
Graphene
Sensors
Sensitivity
Molecular biophysics
Zinc oxide
Reflectivity
Biosensor
graphene
hemoglobin monitoring
locally weighted linear regression
sensitivity
Language
ISSN
1536-1241
1558-2639
Abstract
Biosensors are needed for today’s health monitoring system for detecting different biomolecules. Graphene is a monolayer material that can be utilized to sense biomolecules and design biosensors. We have proposed a Graphene-Gold-Silver hybrid structure design based on Zinc Oxide which gives sensitive performance to detect hemoglobin biomolecules. The advanced biosensor designed based on this hybrid structure shows the highest sensitivity of 1000 nm/RIU which is far better concerning similar structure previously analyzed. The graphene-gold-silver hybrid structure is presented for its possible reflectance results and electric field results. The E-field results match well with the reflectance results given by the sensitive hybrid structure. The sensing biomolecules are presented above the structure where a combination of graphene-gold-silver hybrid structure improves the sensitivity to a great extent. The optimized parameters are obtained by applying variations in the physical parameters of the design. The machine learning algorithm employed for reflectance prediction shows a high prediction accuracy and can be utilized for simulation resource reduction. The proposed biosensor can be used in real-time hemoglobin monitoring.