Aerosol Direct Writing and Thermal Tuning of Copper Nanoparticle Patterns as Surface-Enhanced Raman Scattering Sensors

Journal Article (2020)
Author(s)

Saleh Aghajani (TU Delft - Micro and Nano Engineering)

A. Accardo (TU Delft - Micro and Nano Engineering)

Marcel Tichem (TU Delft - Micro and Nano Engineering)

Research Group
Micro and Nano Engineering
Copyright
© 2020 S. Aghajani, A. Accardo, M. Tichem
DOI related publication
https://doi.org/10.1021/acsanm.0c00887
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 S. Aghajani, A. Accardo, M. Tichem
Research Group
Micro and Nano Engineering
Issue number
6
Volume number
3
Pages (from-to)
5665-5675
Reuse Rights

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Abstract

Surface-enhanced Raman scattering (SERS) substrates are of great interest for detecting low-concentrated analytes. However, issues such as multistep processing, cost, and possible presence of hazardous substances in the fabrication still represent a significant drawback. In this paper, an innovative direct writing method is introduced for solvent-free and spatially selective deposition of fine metal copper nanoparticles (CuNPs), with size distribution below 20 nm, generated in-line through a spark ablation method (SAM). The deposited CuNPs' morphology and composition were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), and energy-dispersive X-ray spectroscopy (EDS). The resulting CuNP patterns feature porous 3D microdomains with nanometric structures serving as hot spots for Raman signal enhancement. Low-temperature post-treatment (below 200 °C) of the deposited CuNPs significantly evolves its morphology and leads to sintering of NPs into a semicrystalline structure with sharp geometric features, which resulted in a more than 10-fold increase of the enhancement factor (up to 2.1 × 105) compared to non-heat-treated samples. The proposed method allows creating SERS substrates constituted by sharp 3D metallic nanopatterns selectively deposited onto specific regions, which paves the way for new printed, highly sensitive SERS-based sensors.