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Ultra-sensitive, multi-component, and real-time detection of heavy metal ions via AgNPs@Cu-TCPP(Pt) sensitized strong coupling plasmonic sensor

Journal 2025 · IF 13.2 · 发表于 2025 年 4 月 17 日
作者: Jie Yan,Runcheng Liu,Jian Shi,Wen Yang,Shouzhen Jiang,Zihao Zhang,Mingshun Jiang
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摘要

The high toxicity and bioaccumulation of heavy metal ion pollutants pose severe threats to the environment and public health. Tilted fiber Bragg grating-based surface plasmon resonance (TFBG-SPR) sensors have emerged as effective tools for heavy metal ion sensing owing to their portability and rapid response. These sensors typically feature a single plasmonic structure. And they are constrained by hardware restrictions and complex spectral analyses. Thus, these conventional designs render multicomponent detection and precise analysis challenging. To address these limitations, this study introduces a lightweight multichannel sensing platform based on a composite plasmonic TFBG structure. A two-dimensional metal–organic framework (2D MOF Cu-TCPP(Pt)) and silver nanoparticles (AgNPs) are integrated onto a gold (Au)-coated TFBG surface. This configuration enables strong coupling between the SPR and localized SPR (LSPR). Hence, the signal sensitivity is enhanced. The molecular sieve effect of the Cu-TCPP(Pt) improves the target analyte enrichment and suppresses the background interference, thereby ensuring a stable signal output. Additionally, a DNA aptamer conformational switching strategy ensures selective target analyte recognition. This platform integrates an AgNPs@Cu-TCPP(Pt)/Au/TFBG sensor, optical channel array, and real-time signal processing module and enables simultaneous detection of Pb 2+ , Cd 2+ , and Hg 2+ within 6 min, with detection limits of 0.007, 0.012, and 0.005 nM, respectively. The proposed sensor effectively mitigates background interference in complex matrices, demonstrating excellent linearity and recovery. Compared with conventional TFBG-based methods, this platform offers high sensitivity, short detection times, and enhanced robustness, and presents a novel solution for real-time multicomponent detection in complex matrices.

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