适体
化学
纳米技术
纳米流体学
电解质
表面改性
生物传感器
离子键合
表面电荷
分子识别
氧化物
原位
纳米孔
化学物理
表征(材料科学)
原子力显微镜
分子动力学
离子强度
DNA
阳极
灵敏度(控制系统)
氧化铝
电荷密度
离子
作者
Zhengxu He,Defang Ding,Daisan Zha,Weiqi Zhang,Mengya Zhang,Li Dai,Xinyi Yang,Xin Lei,Yu Huang,Fan Xia
标识
DOI:10.1021/acs.analchem.5c08167
摘要
Solid-state nanochannel-based sensors show promise for highly sensitive toxic contaminant detection. However, traditional research often prioritizes sensitivity improvement, while specificity enhancement and its mechanisms are frequently overlooked. Herein, we develop a dual-side modified nanochannel sensor by in situ growth of Cu-TCPP metal–organic frameworks (MOFs) on the anodic aluminum oxide (AAO) nanochannel membranes, followed by functionalization with a supersandwich structured DNA (SSW-DNA) aptamer probe on the outer surface of nanochannels. Compared to the nanochannel modified with a single-stranded DNA (ssDNA) aptamer (ssDNA@MOFs), the SSW-DNA@MOFs nanochannel sensor demonstrates an improved specific recognition performance for microcystin-LR (MC-LR) over its structural analogue. Specifically, the discrepancy in ionic current variation rates ( R ) when SSW-DNA@MOFs senses MC-LR ( R = 48.20%) and MC-RR ( R = 10.60%) surpasses the corresponding difference when ssDNA@MOFs recognizes MC-LR ( R = 15.15%) and MC-RR ( R = 9.44%). We demonstrated that after binding the target the SSW-DNA probe undergoes a distinct conformational change, causing a large change in surface-charge density at the real first interface. This interface refers to the region where the aptamer probes on the outer surface of the nanochannels extend into the bulk electrolyte and interact directly with the surrounding solution. The resulting charge change far exceeds that of conventional ssDNA probes, contributing to the specificity enhancement. In addition, this SSW-DNA@MOFs nanochannel sensor has been designed as a portable, on-site test strip for real-world water sample detection. This investigation into the role of the RFI in enhancing specificity provides insight into high-specificity detection in complex environments.
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