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Kim HS, Kim HJ, Lee J, Lee T, Yun J, Lee G, Hong Y. Hand-Held Raman Spectrometer-Based Dual Detection of Creatinine and Cortisol in Human Sweat Using Silver Nanoflakes. Anal Chem 2021; 93:14996-15004. [PMID: 34736319 DOI: 10.1021/acs.analchem.1c02496] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The conventional tissue biopsy method yields isolated snapshots of a narrow region. Therefore, it cannot facilitate comprehensive disease characterization and monitoring. Recently, the detection of tumor-derived components in body fluids─a practice known as liquid biopsy─has attracted increased attention from the biochemical research and clinical application viewpoints. In this vein, surface-enhanced Raman scattering (SERS) has been identified as one of the most powerful liquid-biopsy analysis techniques, owing to its high sensitivity and specificity. Moreover, it affords high-capacity spectral multiplexing for simultaneous target detection and a unique ability to obtain intrinsic biomolecule-fingerprint spectra. This paper presents the fabrication of silver nanosnowflakes (SNSFs) using the polyol method and their subsequent dropping onto a hydrophobic filter paper. The SERS substrate, which comprises the SNSFs and hydrophobic filter paper, facilitates the simultaneous detection of creatinine and cortisol in human sweat using a hand-held Raman spectrometer. The proposed SERS system affords Raman spectrometry to be performed on small sample volumes (2 μL) to identify the normal and at-risk creatinine and cortisol groups.
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Affiliation(s)
- Hyun Soo Kim
- Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Hyun Jung Kim
- Department of Medical Device, Korea Institute of Machinery and Materials (KIMM), Daegu 42994, Republic of Korea.,Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea
| | - Jaehun Lee
- Department of Medical Device, Korea Institute of Machinery and Materials (KIMM), Daegu 42994, Republic of Korea
| | - Taeha Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea.,Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong 30019, Republic of Korea
| | - Jongsu Yun
- Department of Medical Device, Korea Institute of Machinery and Materials (KIMM), Daegu 42994, Republic of Korea
| | - Gyudo Lee
- Department of Biotechnology and Bioinformatics, Korea University, Sejong 30019, Republic of Korea.,Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong 30019, Republic of Korea
| | - Yoochan Hong
- Department of Medical Device, Korea Institute of Machinery and Materials (KIMM), Daegu 42994, Republic of Korea
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