A Robust, Uniform, and Stable Glycerol Liquid Film Substrate for In Situ SERS Quantitative Investigation of Contaminant Distribution on Rough Surfaces

Dandan Peng, Xinyue He, Zhangmei Hu, Jacson Weber de Menezes, Chiara Valsecchi, Meikun Fan
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Abstract

Rapid, in situ, and real‐time molecule mapping on rough surfaces with high accuracy has long been one of the paramount challenges in many fields. Here, an effortless Ag NPs doped glycerol liquid film SERS substrate (g‐LFS) is developed to investigate the distribution of contaminants on different types of rough surfaces. After substrate optimization, the g‐LFS is characterized in terms of uniformity, reproducibility, and stability with time. The substrate showed an excellent signal stability, even after 96.5 h of usage or 19 days of storage, together with great uniformity (7.4% RSD) and reproducibility (7.1% RSD). As a proof of concept, the distribution of Rhodamine 6G(R6G) dye on rough fabric and the R6G migration in different plant types and tissues have been explored. The g‐LFS substrate demonstrated great accuracy, detecting R6G even in deep fabric grooves, recovering 82.4% of the initial concentration. Moreover, the g‐LFS SERS substrate detected significantly different concentrations in root, stem and leave tissues of bean sprouts, as well as between xylem and phloem in vascular plant branches. Overall, the g‐LFS substrate is proven to be well‐suited for in situ detection on rough surfaces with great versatility and robustness, aggregating new opportunities for contaminant investigation on food and plants using SERS.

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用于原位 SERS 定量研究粗糙表面污染物分布的坚固、均匀、稳定的甘油液膜基底
长期以来,在粗糙表面上快速、原位、实时、高精度地绘制分子图谱一直是许多领域面临的重大挑战之一。在这里,我们开发了一种毫不费力的掺杂 Ag NPs 的甘油液膜 SERS 基底(g-LFS),用于研究污染物在不同类型粗糙表面上的分布。经过基底优化后,g-LFS 在均匀性、可重复性和随时间变化的稳定性等方面都得到了表征。即使在使用 96.5 小时或储存 19 天后,基底仍显示出极佳的信号稳定性,同时具有极高的均匀性(7.4% RSD)和可重复性(7.1% RSD)。作为概念验证,研究了罗丹明 6G(R6G)染料在粗糙织物上的分布以及 R6G 在不同植物类型和组织中的迁移。g-LFS 底物表现出极高的准确性,即使在织物的深槽中也能检测到 R6G,并能回收 82.4% 的初始浓度。此外,g-LFS SERS 底物在豆芽的根、茎和叶组织中,以及维管束植物枝条的木质部和韧皮部中检测到的浓度也有显著差异。总之,g-LFS 底物被证明非常适合在粗糙表面进行原位检测,具有很强的通用性和稳健性,为利用 SERS 调查食品和植物中的污染物提供了新的机会。
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