Nanocatalyst coupled with a latent-ratiometric electrochemical switch for label-free zero-tolerance rapid detection of live Salmonella in whole blood samples

检出限 沙门氏菌 线性范围 量子点 纳米技术 胶体金 材料科学 纳米颗粒 化学 色谱法 生物 细菌 遗传学
作者
Namasivayam Kumaragurubaran,P. Arul,Sheng‐Tung Huang,Chih‐Hung Huang,Shiuh-Bin Fang,Ying-Hsiu Lin
出处
期刊:Sensors and Actuators B-chemical [Elsevier]
卷期号:381: 133428-133428 被引量:4
标识
DOI:10.1016/j.snb.2023.133428
摘要

Developing a simple and rapid analytical tool for detecting live Salmonella is urgently needed for clinical diagnosis and food surveillance. In our previous studies, an activity-based latent ratiometric electrochemical switch was successfully deployed to monitor Salmonella but it could not achieve "zero-tolerance" detection; thus, improving the detection sensitivity from that of our previous approach is needed in order to obtain a practical tool for detecting Salmonella. In this study, a latent ratiometric electrochemical switch Sal-CAF coupled with graphene quantum dots-gold nanoparticles (GQDs-AuNPs) modified electrode was developed to meet the needs of selective detection of live Salmonella. Stable and uniformly sized GQDs-AuNPs (14.25 ± 0.2 nm) were achieved by using a free of reducing agent. The Sal-CAF coupled with GQDs-AuNPs/GCE showed an enhanced current response with an extended linear Salmonella detection concentration range of 1.0 × 103 to 1.0 × 1010 CFU/mL and a limit of detection of 35.62 × 101 CFU/mL within 4 h. Moreover, the detection of 100 CFU/mL of the bacteria could be achieved through a dynamic monitoring method in under 12 h without sample pretreatment or enrichment. Additionally, combining GQDs-AuNPs/GCE with the Sal-CAF platform achieved excellent selectivity, serotypes of pathogenic bacteria, detection of dry-emphasized Salmonella without carrying out a pre-enrichment process, the ability to distinguish live from dead cells, and the ability to work in turbid whole blood media. The present approach constitutes a new achievement for the concept of "zero-tolerance," as it can be used to detect bacterial pathogens ahead of infection control.

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