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Lychee-inspired Au@Ag/Au nanoarray chip with hierarchical enhancement-protection for robust SERS detection of additives in fruits

Journal 2025 · IF 7.7 · 发表于 2025 年 11 月 20 日
作者: Yi Yuan,Junlin Chen,Limin Cao,Jianxin Sui,Xiudan Wang,Hong Lin,Kaiqiang Wang
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摘要

Surface-enhanced Raman scattering (SERS) substrates hold great potential for rapid food monitoring, but the inability to reconcile high enhancement, uniformity, and stability presents a critical trilemma for substrate design and practical applications. Here, inspired by the hierarchical structure of lychee, we developed an innovative "integrated balanced enhancement-protection" strategy, fabricating a lychee-like Au@Ag/Au nanoarray chip via a simple, uniform oil/water/oil (O/W/O) self-assembly technique. This design mimics the architecture of lychee: the outer Au layer, modeled after lychee’s peel, provides chemical protection; the inner Au@Ag layer, mirroring lychee’s seed and flesh, delivers strong plasmonic enhancement. The unique bilayer nanoarchitecture, featuring dense and vertically coupled plasmonic hotspots, ensures highly efficient electromagnetic (EM) field by Au-Ag synergistic effects. It demonstrates an analytical enhancement factor (AEF) of 2.30 × 10 6 , uniformity with a relative standard deviation (RSD) of 6.55 %, and stability with 93.26 % signal retention over 60 days. When coupled with a universal pretreatment protocol to minimize matrix interference, the platform enables sensitive and reliable quantitative detection of three representative food additives, including potassium sorbate (PS), sodium benzoate (SB), and sodium saccharin (SS) in complex fruit matrices. The limit of detection (LOD) for all analytes was below 75 mg/kg, substantially surpassing the national regulatory standards of China. Importantly, the chip demonstrated high recoveries (86.62–102.49 %) and low RSD (< 9 %) across multiple fruit types. Beyond food safety, this scalable, low-cost approach not only overcomes SERS substrate bottlenecks but also provides a viable field-deployable tool with broader implications for SERS-based analysis.

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