Single-Probe-Based Colorimetric and Photothermal Dual-Mode Identification of Multiple Bacteria

化学 光热治疗 细菌 鉴定(生物学) 纳米技术 对偶(语法数字) 双模 生物系统 色谱法 材料科学 生物 遗传学 植物 航空航天工程 艺术 工程类 文学类
作者
Ying Wang,Jingwen Li,Hongyu Liu,Xu Du,Limin Yang,Jingbin Zeng
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:95 (5): 3037-3044 被引量:55
标识
DOI:10.1021/acs.analchem.2c05140
摘要

Effective identification of multiple pathogenic bacteria in unknown samples is important for disease prevention and control but remains a challenge yet. A single-mode array-based sensing approach is simple and sensitive, but it usually relies on the use of multiple cross-reactive receptors to construct sensor arrays, which is cumbersome and insufficiently accurate. Here, we developed a sensor array with colorimetric and photothermal dual mode of differentiating multiple pathogenic bacteria. The sensor array was based on boronic acid-functionalized Au–Fe 3 O 4 nanoparticles (BA–GMNPs), which not only possess localized surface plasmon resonance properties, showing a burgundy color similar to that of AuNPs, but also exhibit mild superparamagnetism, allowing for the differentiation of bacteria before and after binding to the nanoparticles. Immobilization of BA–GMNPs on the bacterial cell surface by covalent bonding would diminish NaCl-induced assembly of BA–GMNPs. Different BA–GMNPs@bacterial complexes differed in their ability to resist assembly and produced different colorimetric and photothermal response signals. A unique molecular fingerprint of each bacterium was obtained by linear discriminant analysis of the response patterns, demonstrating an effective differentiation among the six species studied. Compared with single-mode sensing arrays based on multiple receptors, this method only requires the preparation of a single nanomaterial, which produces two signal outputs for the identification of multiple bacteria with better differentiation. It can distinguish not only multiple pathogenic bacteria but also Gram-negative and Gram-positive bacteria, and, more importantly, it can perform preliminary discrimination of unknown samples.
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