journal article·2021-03

A SERS substrate with remarkable reproducibility: Adsorbing and detecting both hydrophobic and hydrophilic molecules using rGO/PEI/PAA/CD-AgNP nanocomposites

Junbin Fang, Junjia Zhu, Meng Fu, Yu Gu, Guanghuan Li, Honghao Hou, Zihua Lin, Xingyuan Chen, Xiangming Li

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Abstract

Abstract Surface-enhanced Raman scattering (SERS) spectroscopy is a highly sensitive and nondestructive method to detect a wide range of probe molecules in the field of agriculture and industry. However, to prepare a homogeneous and reproducible SERS substrate is still a great challenge. Herein, we propose a facile strategy to prepare a homogeneous SERS substrate that presents remarkable adsorption and reproducibility by using electrostatic assembly and in-situ reduction technology. The reduced graphene oxide assembled films modified with polyelectrolytes and heptakis (6-deoxy-6-amino)-β-cyclodextrin are used as the template and reducing agent in the preparation process. By means of simply mixing with AgNO3 solution, composites containing Ag nanoparticles were obtained. The as-prepared composites present great stability and remarkable adsorption for both hydrophobic and hydrophilic molecules, such as thiram and methylene blue. Combined the functions of reduced graphene, polyelectrolytes and β-cyclodextrin components, the composites exhibit highly sensitive SERS signals with inhibiting the influence of fluorescence. The detection limit of thiram and methylene blue are 10−9 M and 10−15 M, respectively. Furthermore, the composites serve as a reproducible SERS substrate to overcome randomness of SERS results and improve reproducibility. We expect this study to provide a novel method to fabricate more effective SERS substrates.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Nanocluster Synthesis and Applications · Advanced Nanomaterials in Catalysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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