arrow
返回

An Optical Modeling Framework for Coronavirus Detection Using Graphene-Based Nanosensor

delete2022-08-20
delete2
delete
OA
AI
A
Amir Maghoul
I
Ingve Simonsen *
A
Ali Rostami
P
Peyman Mirtaheri *
DOI:10.3390/nano12162868delete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
The outbreak of the COVID-19 virus has faced the world with a new and dangerous challenge due to its contagious nature. Hence, developing sensory technologies to detect the coronavirus rapidly can provide a favorable condition for pandemic control of dangerous diseases. In between, because of the nanoscale size of this virus, there is a need for a good understanding of its optical behavior, which can give an extraordinary insight into the more efficient design of sensory devices. For the first time, this paper presents an optical modeling framework for a COVID-19 particle in the blood and extracts its optical characteristics based on numerical computations. To this end, a theoretical foundation of a COVID-19 particle is proposed based on the most recent experimental results available in the literature to simulate the optical behavior of the coronavirus under varying physical conditions. In order to obtain the optical properties of the COVID-19 model, the light reflectance by the structure is then simulated for different geometrical sizes, including the diameter of the COVID-19 particle and the size of the spikes surrounding it. It is found that the reflectance spectra are very sensitive to geometric changes of the coronavirus. Furthermore, the density of COVID-19 particles is investigated when the light is incident on different sides of the sample. Following this, we propose a nanosensor based on graphene, silicon, and gold nanodisks and demonstrate the functionality of the designed devices for detecting COVID-19 particles inside the blood samples. Indeed, the presented nanosensor design can be promoted as a practical procedure for creating nanoelectronic kits and wearable devices with considerable potential for fast virus detection.
Keyword:
COVID-19 particle model
COVID-19 spikes
reflectance
graphene-based nanosensor
gold nanodisks
blood sample
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

N
Nanomaterials
IF:
4.3
论文数:
2.3W
被引数:
8.1W

机构

U
University of Tabriz
学者数:
9.3K
论文数: 8.5K
被引数: 1.0W
O
oslo metropolitan university (oslomet)
学者数:
2.4K
论文数: 2.3K
被引数: 3
引用论文

引用论文

Semantic Sovereignty
err2012-09-13
err0
PREAI
errSTEPHEN KEARNS; OFRA MAGIDOR
err分享
err收藏
Overcoming the bottleneck to widespread testing: a rapid review of nucleic acid testing approaches for COVID-19 detection
errRNA
IF5
err2020-05-01
err376
errOAAI
errEsbin, Meagan N.; Whitney, Oscar N.; Chong, Shasha; Maurer, Anna; Darzacq, Xavier; Tjian, Robert
err分享
err收藏
Detection of COVID-19: A review of the current literature and future perspectives新型冠状病毒肺炎的检测: 当前文献综述与未来展望
err2020-10-01
err298
errOAAI
errJi, Tianxing; Liu, Zhenwei; Wang, GuoQiang; Guo, Xuguang; Khan, Shahzad Akbar; Lai, Changchun; Chen, Haoyu; Huang, Shiwen; Xia, Shaomei; Chen, Bo; Jia, Hongyun; Chen, Yangchao; Zhou, Qiang
err分享
err收藏
Rapid SARS-CoV-2 Spike Protein Detection by Carbon Nanotube-Based Near-Infrared Nanosensors基于碳纳米管的近红外纳米传感器快速检测SARS-CoV-2刺突蛋白
err2021-02-26
err143
errOAAI
errPinals, Rebecca L.; Ledesma, Francis; Yang, Darwin; Navarro, Nicole; Jeong, Sanghwa; Pak, John E.; Kuo, Lili; Chuang, Yung-Chun; Cheng, Yu-Wei; Sun, Hung-Yu; Landry, Markita P.
err分享
err收藏
Combining Nakagami imaging and convolutional neural network for breast lesion classification
err2017-09-01
err0
PREAI
errMichał Byra; Hanna Piotrzkowska-Wróblewska; Katarzyna Dobruch-Sobczak; Andrzej Nowicki
err分享
err收藏
Drug-induced cystitis caused by herbal medicine (Bofutsushosan)
err2021-09-01
err0
errOAAI
errKumiko Kato; Aika Matsushita; Shoji Suzuki; Hiroki Sai; Hiroki Hirabayashi; Ryohei Hattori
err分享
err收藏
学者 查看更多内容