Honeycomb-Inspired Surface-Enhanced Raman Scattering Microarray for Large-Area Automated Testing of Urease in Saliva Samples

材料科学 表面增强拉曼光谱 纳米技术 拉曼散射 唾液 拉曼光谱 化学 光学 生物化学 物理
作者
Guoqian Li,Jinkun Fan,Ting Zhang,Ting Gao,Y.M. Chong,Minghui Liang,Liang Shen,Bo Hu,Langlang Yi,Zhao Lei,Hélène Castel
出处
期刊:ACS Sensors [American Chemical Society]
标识
DOI:10.1021/acssensors.4c00006
摘要

Surface-enhanced Raman scattering (SERS) technology, as an important analytical tool, has been widely applied in the field of chemical and biomedical sensing. Automated testing is often combined with biochemical analysis technologies to shorten the detection time and minimize human error. The present SERS substrates for sample detection are time-consuming and subject to high human error, which are not conducive to the combination of SERS and automated testing. Here, a novel honeycomb-inspired SERS microarray is designed for large-area automated testing of urease in saliva samples to shorten the detection time and minimize human error. The honeycomb-inspired SERS microarray is decorated with hexagonal microwells and a homogeneous distribution of silver nanostars. Compared with the other four common SERS substrates, the optimal honeycomb-inspired SERS microarray exhibits the best SERS performance. The RSD of 100 SERS spectra continuously collected from saliva samples is 6.56%, and the time of one detection is reduced from 5 min to 10 s. There is a noteworthy linear relationship with a R2 of 0.982 between SERS intensity and urease concentration, indicating the quantitative detection capability of the urease activity in saliva samples. The honeycomb-inspired SERS microarray, combined with automated testing, provides a new way in which SERS technology can be widely used in biomedical applications.
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