Advances in Surface-Enhanced Raman Spectroscopy for Urinary Metabolite Analysis: Exploiting Noble Metal Nanohybrids

纳米技术 表面增强拉曼光谱 材料科学 贵金属 石墨烯 计算机科学 拉曼光谱 拉曼散射 金属 物理 光学 冶金
作者
Ningbin Zhao,Peizheng Shi,Zhicheng Wang,Zhuang Sun,Kaiqiang Sun,Chen Ye,Li Fu,Cheng‐Te Lin
出处
期刊:Biosensors [Multidisciplinary Digital Publishing Institute]
卷期号:14 (12): 564-564 被引量:3
标识
DOI:10.3390/bios14120564
摘要

This review examines recent advances in surface-enhanced Raman spectroscopy (SERS) for urinary metabolite analysis, focusing on the development and application of noble metal nanohybrids. We explore the diverse range of hybrid materials, including carbon-based, metal–organic-framework (MOF), silicon-based, semiconductor, and polymer-based systems, which have significantly improved SERS performance for detecting key urinary biomarkers. The principles underlying SERS enhancement in these nanohybrids are discussed, elucidating both electromagnetic and chemical enhancement mechanisms. We analyze various fabrication methods that enable precise control over nanostructure morphology, composition, and surface chemistry. The review critically evaluates the analytical performance of different hybrid systems for detecting specific urinary metabolites, considering factors such as sensitivity, selectivity, and stability. We address the analytical challenges associated with SERS-based urinary metabolite analysis, including sample preparation, matrix effects, and data interpretation. Innovative solutions, such as the integration of SERS with microfluidic devices and the application of machine learning algorithms for spectral analysis, are highlighted. The potential of these advanced SERS platforms for point-of-care diagnostics and personalized medicine is discussed, along with future perspectives on wearable SERS sensors and multi-modal analysis techniques. This comprehensive overview provides insights into the current state and future directions of SERS technology for urinary metabolite detection, emphasizing its potential to revolutionize non-invasive health monitoring and disease diagnosis.
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