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Design and Engineering of Silver Nanomushroom Arrays as a Universal Solid-State SERS Platform for the Label-Free, Sensitive, and Quantitative Detection of Trace Proteins

拉曼散射 生物分子 纳米技术 分析物 基质(水族馆) 拉曼光谱 蛋白质检测 表面增强拉曼光谱 检出限 纳米尺度 材料科学 化学 色谱法 光学 物理 海洋学 地质学
作者
Kang Chen,Qian Zhao,Yi Wei,Jingtao Sun,Yanyan Lu,Tingting Xiao,Hongwen Zhang,Weiping Cai,Kang Chen,Qian Zhao,Yi Wei,Jingtao Sun,Yanyan Lu,Tingting Xiao,Hongwen Zhang,Weiping Cai
出处
期刊:ACS applied bio materials [American Chemical Society]
卷期号:8 (2): 1484-1492 被引量:1
标识
DOI:10.1021/acsabm.4c01697
摘要

Surface-enhanced Raman scattering (SERS) is an ultrasensitive optical technique that is critical for protein detection and essential for identifying protein structure and concentrations in various biomedical and diagnostic applications. However, achieving highly sensitive and reproducible SERS signals for label-free proteins remains challenging due to their weak Raman signals and structural complexity. In this study, silver nanomushroom arrays (Ag NMAs) as SERS substrates were readily prepared and surface-engineered using a facile template-assisted micro- and nanofabrication approach. The surface of the substrate exhibits nanoscale roughness, long-range order, and hydrophilicity, enabling rapid and uniform dispersion of protein molecules. These molecules are anchored through Ag-S bonds, resulting in ultrasensitive Raman signals driven by strong electromagnetic enhancement effects. The highly ordered array structure improves signal repeatability, achieving a relative standard deviation of as low as 4.32%. Additionally, utilizing the silicon characteristic peak of the SERS substrate as an internal standard significantly reduces measurement errors, allowing for reliable and precise quantitative detection of protein molecules, with a linear correlation coefficient (R2) exceeding 0.96. Ultrasensitive SERS detection and effective protein discrimination via principal component analysis further validate the Ag NMA substrate's potential for universal trace protein detection. This study presents an advanced SERS platform for the sensitive and rapid detection of trace proteins, showcasing significant potential in pharmaceutical research, metabolic studies, diagnostic medicine, and protein engineering.
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