journal article·2021-01

Tuning plasmonic nanostructures in graphene-based nano-sandwiches using ultraviolet/ozone functionalization

Dai Zhang, Ying Du, Cheng Yang, Pan Zeng, Yan Yu, Yujun Xie, Rongqing Liang, Qiongrong Ou, Shuyu Zhang

Journal of Materials Science· Volume 56· Issue 2
Publisher page

Abstract

Graphene-based plasmonic sandwiches have received considerable research interest because of their intriguing optical, electronic and catalytic properties. The capability of tuning the distribution of plasmonic nanostructures is essential for exploring their intrinsic properties and potential applications. Herein, we report an ultraviolet (UV)/ozone-assisted approach to synthesizing gold nanoparticles/reduced graphene oxide/gold thin film (AuNPs/RGO/Au) plasmonic nano-sandwiches with easy and fine tunability of AuNPs at room temperature. The UV/ozone functionalization controls the activation of anchor sites for AuNPs on the GO scaffold without creating new defects on the basal plane. By simply varying the functionalization time, the loading density of AuNPs with an average size of ~ 17 nm is able to reach a value of over 500 μm−2, which is higher than previously reported values using other room-temperature methods. The AuNPs/RGO/Au plasmonic nano-sandwiches offer remarkable near-field enhancement with an enhancement factor of over 300 in the Raman signal of graphene, which is comparable with the highest reported values. The as-prepared AuNPs/RGO/Au nano-sandwiches can be used as sensitive surface-enhanced Raman spectroscopy substrates. This work demonstrates that oxygen-containing functional groups, which were commonly considered to have negative effects on graphene-based nanocomposites, can contribute to the successful synthesis of high-loading plasmonic nano-sandwiches.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Graphene research and applications · Plasmonic and Surface Plasmon Research

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

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