journal article·2024-06-01Open access

Efficient concentration of trace analyte with ordered hotspot construction for a robust and sensitive SERS platform

Youdi Hu, Yanlei Hu, Zhenyu Wang, Jiale Yong, Wei Xiong, Dong Wu, Shixiang Xu

Abstract

Abstract Surface-enhanced Raman scattering (SERS) platform, which enables trace analyte detection, has important application prospects. By structuring/modifying the surface of the SERS substrate, analyte in highly diluted solutions can be concentrated into localized active areas for highly sensitive detection. However, subject to the difficulty of the fabrication process, it remains challenging to balance hot-spot construction and the concentration capacity of analyte simultaneously. Therefore, preparing SERS substrates with densely ordered hot spots and efficient concentration capacity is of great significance for highly sensitive detection. Herein, we propose an Ag and fluoroalkyl-modified hierarchical armour substrate (Ag/F-HA), which has a double-layer stacking design to combine analyte concentration with hotspot construction. The microarmour structure is fabricated by femtosecond-laser processing to serve as a superhydrophobic and low-adhesive surface to concentrate analyte, while the anodic aluminium oxide (AAO) template creates a nanopillar array serving as dense and ordered hot spots. Under the synergistic action of hot spots and analyte concentration, Ag/F-HA achieves a detection limit down to 10−7 M doxorubicin (DOX) molecules with a RSD of 7.69%. Additionally, Ag/F-HA exhibits excellent robustness to resist external disturbances such as liquid splash or abrasion. Based on our strategy, SERS substrates with directional analyte concentrations are further explored by patterning microcone arrays with defects. This work opens a way to the realistic implementation of SERS in diverse scenarios.
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Research context

OpenAlex topics: Spectroscopy Techniques in Biomedical and Chemical Research · Gold and Silver Nanoparticles Synthesis and Applications · Spectroscopy and Chemometric Analyses

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

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