Molecularly imprinted core-shell Au nanoparticles for 2,4-dichlorophenoxyacetic acid detection in milk using surface-enhanced Raman spectroscopy

化学 检出限 分析物 胶体金 表面增强拉曼光谱 拉曼散射 分子印迹聚合物 纳米颗粒 拉曼光谱 纳米技术 分析化学(期刊) 色谱法 选择性 材料科学 有机化学 物理 光学 催化作用
作者
Shaolong Feng,Yaxi Hu,Lei Chen,Xiaonan Lu
出处
期刊:Analytica Chimica Acta [Elsevier BV]
卷期号:1227: 340333-340333 被引量:17
标识
DOI:10.1016/j.aca.2022.340333
摘要

Surface-enhanced Raman spectroscopy (SERS) has been extensively investigated for rapid and sensitive detection of trace level chemical contaminants in foods. Lack of selectivity to the targeted molecules in food matrices and fairly poor spectral reproducibility remain the main challenges for practical SERS applications. Herein, an ingenious strategy was proposed to hybridize molecularly imprinted polymers (MIPs) with gold nanoparticles as the functional SERS substrate for selective separation and detection of 2,4-dichlorophenoxyacetic acid (2,4-D), a systemic herbicide that has acute toxicity and potential cancer risk. The core-shell AuNPs@MIPs nanoparticles were finely tailored by wrapping an ultrathin layer of MIPs shell on the surface of AuNPs, which allowed selectively separating and enriching 2,4-D to the near surface of AuNPs and ensured the enhancement of Raman scattering signal of the analyte. Embedding an internal standard (i.e., 4-aminothiophenol) inside AuNPs@MIPs for SERS spectral calibration improved the quantification accuracy for 2,4-D. Three-dimensional finite difference time domain (3D-FDTD) simulation demonstrated the maximal electric field enhancement presented in the gap between adjacent AuNPs@MIPs with the theoretical enhancement factor (EF) as high as 5.85 × 106. Chemometric models established using SERS spectra showed accurate differentiation and quantification results for 2,4-D in milk at various contamination levels with a limit of detection (LOD) of 0.011 μg/mL. Our approach to integrate MIPs with noble metallic nanoparticles has great potential for selective and quantitative detection of analytes using SERS for practical agri-food analysis.
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