.png){kind=icon}
.png){kind=icon}
Return
Graphene-based H-shaped biosensor with high sensitivity and optimization using ML-based algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.aej.2023.01.002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a biosensing absorber based on phase transition material is presented. Dif-ferent phases of the Ge2Sb2Te5 (GST) substrate have been studied for the suggested absorber with controllable characteristics. The structure has been examined to determine the infrared absorption characteristics. The detection of varying volumes of hemoglobin and urine biomolecules is studied. The graphene-GST material is utilized for spectrum tuning. The tuning for two distinct phases of GST material, amorphous GST and crystalline GST is examined. The results for aGST and cGST are reported in the form of absorption. Different amounts of hemoglobin and urine biomolecules are used to tune these two GST stages. Based on the wavelength shifts at these various concentra-tions, the sensitivity is computed. The highest achievable sensitivity for hemoglobin and urine bio-molecules is 1500 nm/RIU and 1667 nm/RIU. The developed model is observed for various geometrical parameters and incidence angles, from which it is determined that the suggested struc-ture is insensitive to wide angles between 0 degrees and 60 degrees. For urine biomolecules, the aGST design is more sensitive than the cGST design, but similar results are achieved for hemoglobin biomolecules. Experiments are conducted with Machine Learning-based regression models to minimize the simu-lation time and resource requirements of biosensor design. The findings of the trials indicate that a regression model can accurately estimate the absorption values for intermediate wavelengths with an R2 score of 0.9999.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
Keywords:
Absorber
Biosensor
Extreme gradient boosting
Graphene
GST
Infrared
Regression
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.8
Papers:
6.3K
Citations:
2.6W
Organization
View more organizations