Plasmonic cellulose microfilament assisted SERS detection in microfluidics

Kaibin Yao; Xin Xie; Jiawei Jiao; Anping Liu; Yingzhou Huang

doi:10.1016/j.saa.2023.123631

Plasmonic cellulose microfilament assisted SERS detection in microfluidics

Spectrochim Acta A Mol Biomol Spectrosc. 2024 Mar 5:308:123631. doi: 10.1016/j.saa.2023.123631. Epub 2023 Nov 8.

Authors

Kaibin Yao¹, Xin Xie¹, Jiawei Jiao², Anping Liu³, Yingzhou Huang⁴

Affiliations

¹ College of Physics, Chongqing University, Chongqing 400044, China; Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, Chongqing University, Chongqing 400044, China.
² College of Physics, Chongqing University, Chongqing 400044, China.
³ College of Physics, Chongqing University, Chongqing 400044, China. Electronic address: liuanping@cqu.edu.cn.
⁴ Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, Chongqing University, Chongqing 400044, China. Electronic address: yzhuang@cqu.edu.cn.

PMID: 37995409
DOI: 10.1016/j.saa.2023.123631

Abstract

Limited by the narrow enhanced area of nanoscale on the metal surface, the sensitivity of surface-enhanced Raman spectroscopy (SERS) detection in solution is usually much lower than the detection in a solid substrate, which is dramatic in microfluidics for online detection. In this work, a cellulose microfilament embraced by Ag nanoparticles, called plasmonic cellulose microfilament, is located in a microchannel for SERS detection in microfluidics. Benefiting from the congestion caused by the plasmonic cellulose microfilament in a microchannel, the trace molecule in the solution is much easier to gather in Ag nanoparticles for Raman enhancement. To obtain high sensitivity, the structure of plasmonic cellulose microfilament is optimized. The SERS spectra collected in this novel microfluidics demonstrate that the plasmonic cellulose microfilament presents a high sensitivity at 10^-13 M and good reproducibility in SERS detection. In addition, automatic identification of urea presence or absence was achieved based on deep learning (DL) here. The results show excellent diagnostic accuracy (99 %), which suggests that a fast, label-free urea screening tool can be developed. These results point out this SERS microfluidics with plasmonic cellulose microfilament has a great application prospective in online SERS detection with high sensitivity.

Keywords: Deep learning; Online detection; Plasmonic cellulose microfilament; SERS microfluidics.