期刊
COLLOIDS AND SURFACES B-BIOINTERFACES
卷 214, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.colsurfb.2022.112471
关键词
Silver nanoplates; Plasmonic assay; Localized surface plasmon resonance; Pathogen detection
资金
- National Natural Science Foundation of China (NSFC) [21605126, 22134005]
- Fund of Chongqing Research Program of Basic Research and Frontier Technol-ogy [cstc2017jcyjAX0091]
- Chongqing Talents Program for Outstanding Scientists
This article presents a simple method for bacterial detection using silver nanoplates as a chromogenic substrate. The method takes advantage of the specificity and affinity of aptamers, as well as the ability of catalase to hydrolyze H2O2. By introducing catalase into a sandwich structure composed of dual-aptamer recognition strategy, the bacterial detection signal is converted into a peak shift of local surface plasmon resonance (LSPR) and color change. This method enables fast naked-eye detection of Staphylococcus aureus.
Development of rapid, sensitive, and selective method for pathogenic bacteria detection is of great importance for food safety, medical diagnostic, and environmental monitoring. Currently, most techniques for low numbers of bacteria detection require advanced instrumentation or skilled operators. Herein, we present a facile colorimetric detection platform for bacterial detection using Ag nanoplates as chromogenic substrate, which takes advantages of the high specificity and affinity of aptamer and the ability of catalase to hydrolyze H2O2 that can etch Ag nanoplates. By introducing catalase to the sandwich structure composed by dual-aptamer recognition strategy, bacteria detection signal is converted to the peak shift of LSPR and colorimetric change. This proposed method allows a fast naked-eye detection of S. aureus at the concentration of 60 CFU/mL based on the combination of streptavidin-biotin system and inherent sensitivity of plasmonic Ag nanoplates. Owing to the high selectivity and sensitivity, as well as the low-cost and good adaptability, this plasmonic assay is expected to be suitable for pathogenic bacteria detection in resource-limited settings.
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