纳米技术
材料科学
平版印刷术
表面增强拉曼光谱
可穿戴技术
计算机科学
可穿戴计算机
拉曼光谱
光电子学
拉曼散射
嵌入式系统
光学
物理
作者
Debadrita Paria,Kam Sang Kwok,Piyush Raj,Peng Zheng,David H. Gracias,Ishan Barman
出处
期刊:Nano Letters
[American Chemical Society]
日期:2022-03-29
卷期号:22 (9): 3620-3627
被引量:80
标识
DOI:10.1021/acs.nanolett.1c04722
摘要
Widespread testing and isolation of infected patients is a cornerstone of viral outbreak management, as underscored during the ongoing COVID-19 pandemic. Here, we report a large-area and label-free testing platform that combines surface-enhanced Raman spectroscopy and machine learning for the rapid and accurate detection of SARS-CoV-2. Spectroscopic signatures acquired from virus samples on metal–insulator–metal nanostructures, fabricated using nanoimprint lithography and transfer printing, can provide test results within 25 min. Not only can our technique accurately distinguish between different respiratory and nonrespiratory viruses, but it can also detect virus signatures in physiologically relevant matrices such as human saliva without any additional sample preparation. Furthermore, our large area nanopatterning approach allows sensors to be fabricated on flexible surfaces allowing them to be mounted on any surface or used as wearables. We envision that our versatile and portable label-free spectroscopic platform will offer an important tool for virus detection and future outbreak preparedness.
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