A study on a hybrid SERS substrates based on arrayed gold nanoparticle/graphene/copper cone cavities fabricated by a conical tip indentation

Zhang, Jingran and Lu, Siwei and Shi, Guangfeng and Xie, Wenkun and Geng, Yanquan and Wang, Zuobin (2023) A study on a hybrid SERS substrates based on arrayed gold nanoparticle/graphene/copper cone cavities fabricated by a conical tip indentation. Journal of Materials Research and Technology, 22. pp. 1558-1571. ISSN 2238-7854 (https://doi.org/10.1016/j.jmrt.2022.12.001)

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Abstract

The pesticide residues with low concentration are important to detect in food safety. Although a lot of methods, such as spectrophotometry, colorimetry and liquid chromatography, are used to detect pesticide residues, they often suffer from low efficiency and long detection time. Here, a hybrid Surface Enhanced Raman Scattering (SERS) substrate, namely, Au nanoparticle/copper-based graphene/arrayed cone cavities (Au@GR@Cu cone cavities), is fabricated for pesticide residues detection through sputtering Au nanoparticle and generating periodical cone cavity structures on the copper-based graphene surface. By actively controlling the interspace between adjacent cone cavities, both surface topographies and the number of graphene layers on the SERS substrate could be modulated, creating an effective control on the functional performance of the SERS substrate. Both sensitivity and reliability of the hybrid SERS substrate were evaluated by Raman Spectroscopy detection. The experimental results indicated that both 10-9 mol/L malachite green and 10-7 mol/L paraquat could be detected, respectively. Moreover, the detected Raman intensity of malachite green only dropped by 11.89% in a month and 29.8% in two months, respectively. The hotspot distribution was also characterized by 2-dimensional Raman mapping. The research finding is expected to provide a new insight in development of innovative SERS substrates.

ORCID iDs

Zhang, Jingran, Lu, Siwei, Shi, Guangfeng, Xie, Wenkun ORCID logoORCID: https://orcid.org/0000-0002-5305-7356, Geng, Yanquan and Wang, Zuobin;