Actively Controllable Solid-Phase Microextraction in a Hierarchically Organized Block Copolymer-Nanopore Electrode Array Sensor for Charge-Selective Detection of Bacterial Metabolites

分层组织的嵌段共聚物-纳米孔电极阵列传感器中的主动可控固相微萃取,用于细菌代谢物的电荷选择性检测

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作者:Jin Jia, Seung-Ryong Kwon, Seol Baek, Vignesh Sundaresan, Tianyuan Cao, Allison R Cutri, Kaiyu Fu, Bridget Roberts, Joshua D Shrout, Paul W Bohn

Abstract

Pseudomonas aeruginosa produces a number of phenazine metabolites, including pyocyanin (PYO), phenazine-1-carboxamide (PCN), and phenazine-1-carboxylic acid (PCA). Among these, PYO has been most widely studied as a biomarker of P. aeruginosa infection. However, despite its broad-spectrum antibiotic properties and its role as a precursor in the biosynthetic route leading to other secondary phenazines, PCA has attracted less attention, partially due to its relatively low concentration and interference from other highly abundant phenazines. This challenge is addressed here by constructing a hierarchically organized nanostructure consisting of a pH-responsive block copolymer (BCP) membrane with nanopore electrode arrays (NEAs) filled with gold nanoparticles (AuNPs) to separate and detect PCA in bacterial environments. The BCP@NEA strategy is designed such that adjusting the pH of the bacterial medium to 4.5, which is above the pKa of PCA but below the pKa of PYO and PCN, ensures that PCA is negatively charged and can be selectively transported across the BCP membrane. At pH 4.5, only PCA is transported into the AuNP-filled NEAs, while PYO and PCN are blocked. Structural characterization illustrates the rigorous spatial segregation of the AuNPs in the NEA nanopore volume, allowing PCA secreted from P. aeruginosa to be quantitatively determined as a function of incubation time using square-wave voltammetry and surface-enhanced Raman spectroscopy. The strategy proposed in this study can be extended by changing the nature of the hydrophilic block and subsequently applied to detect other redox-active metabolites at a low concentration in complex biological samples and, thus, help understand metabolism in microbial communities.

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