Evaluating analytical performance and characterization of silver-based SERS substrate fabricated by 3D printed microfluidic droplet generation

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Beilstein Arch. 2023, 202316. https://doi.org/10.3762/bxiv.2023.16.v1

Published 20 Apr 2023

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This preprint has not been peer-reviewed. When a peer-reviewed version is available, this information will be updated.


The detection of harmful chemicals in the environment and food safety is a crucial requirement. While traditional techniques such as GC-MS and HPLC have provided high sensitivity, they are expensive, time-consuming, and require skilled labor. On the other hand, Surface-enhanced Raman spectroscopy is a powerful analytical tool for detecting ultra-low concentrations of chemical compounds and biomolecules. We present a reproducible method for producing uniform-sized Ag nanoparticles, which can be used to create highly sensitive SERS substrates. A microfluidic device was employed to minimize the precursor reagents within the droplets, resulting in uniform shape and size Ag nanoparticles. The study investigates the effects of various synthesis conditions on the size distribution, dispersity, and localized surface plasmon resonance wavelength of the Ag nanoparticles. To create the SERS substrate, the as-synthesized Ag nanoparticles were assembled into a monolayer on a liquid/air interface and deposited onto a porous silicon array prepared through a metal-assisted chemical etching approach. By using the developed microfluidic, the enhancement factor of the Raman signal for rhodamine B (at 10-9 M) and melamine (at 10-7 M) was calculated to be 8.59 ´ 106 and 8.21 ´ 103, respectively. The detection limits for rhodamine B and melamine were estimated to be 1.94 × 10-10 M and 2.8 × 10-8 M with an RSD ~ 3.4% and 4.6%, respectively. The developed SERS substrate exhibits exceptional analytical performance and has the potential to be a valuable analytical tool for monitoring environmental contaminants.

Keywords: silver nanoparticle; 3D printing; microfluidic droplet; SERS Substrate; smartphone detection.

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When a peer-reviewed version of this preprint is available, this information will be updated in the information box above. If no peer-reviewed version is available, please cite this preprint using the following information:

Lee, Y.-I.; Sonexai, P.; Nguyen, M. V.; Huy, B. T. Beilstein Arch. 2023, 202316. doi:10.3762/bxiv.2023.16.v1

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