Coupling annealed silver nanoparticles with a porous silicon Bragg mirror SERS substrate and machine learning for rapid non-invasive disease diagnosis

Shibin Han; Cheng Chen; Chen Chen; Lijun Wu; Xue Wu; Chen Lu; Xueqin Zhang; Peng Chao; Xiaoyi Lv; Zhenhong Jia; Junwei Hou

doi:10.1016/j.aca.2023.341116

Coupling annealed silver nanoparticles with a porous silicon Bragg mirror SERS substrate and machine learning for rapid non-invasive disease diagnosis

Anal Chim Acta. 2023 May 8:1254:341116. doi: 10.1016/j.aca.2023.341116. Epub 2023 Mar 17.

Authors

Shibin Han¹, Cheng Chen², Chen Chen³, Lijun Wu⁴, Xue Wu⁴, Chen Lu⁵, Xueqin Zhang⁴, Peng Chao⁴, Xiaoyi Lv⁶, Zhenhong Jia⁷, Junwei Hou⁸

Affiliations

¹ School of Physical Science and Technology, Xinjiang University, Urumqi, 830046, China.
² College of Software, Xinjiang University, Urumqi, 830046, China.
³ College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China.
⁴ The People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China.
⁵ The First Affiliated Hospital of Xinjiang Medical University, 830011, Urumqi, China.
⁶ College of Software, Xinjiang University, Urumqi, 830046, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, 840046, China. Electronic address: xjuwawj01@163.com.
⁷ College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China; The Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, 840046, China. Electronic address: jzhh@xju.edu.cn.
⁸ State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing at Karamay, Karamay, 834000, China. Electronic address: Junweihou@cupk.edu.cn.

PMID: 37005026
DOI: 10.1016/j.aca.2023.341116

Abstract

Ag₂O-Ag-porous silicon Bragg mirror (PSB) composite SERS substrates were successfully synthesized by using a combination of electrochemical and thermochemical methods. Test results showed that the SERS signal increased and decreased as the annealing temperature used for the substrate increased, where the most intense SERS signal was obtained using a substrate annealed at 300 °C. Stability test results showed substantial enhancement of the SERS signal intensity of the Ag₂O-Ag-PSB composite one month after preparation compared with that of conventional Ag-PSB. We conclude that Ag₂O nanoshells play an essential role in SERS signal enhancement. Ag₂O prevents natural oxidation of Ag nanoparticles (AgNPs) and has a solid localized surface plasmon resonance (LSPR). SERS signal enhancement was tested using this substrate for serum from patients with Sjögren's syndrome (SS) and Diabetic nephropathy (DN), as well as from healthy controls (HC). SERS feature extraction was performed using principal component analysis (PCA). The extracted features were analyzed by a support vector machine (SVM) algorithm. Finally, a rapid screening model for SS and HC, as well as DN and HC, was developed and used to perform controlled experiments. The results showed that the diagnostic accuracy, sensitivity and selectivity for SERS technology combined with machine learning algorithms reached 90.7%, 93.4% and 86.7% for SS/HC and 89.3%, 95.6% and 80% for DN/HC, respectively. The results of this study show that the composite substrate has excellent potential to be developed into a commercially available SERS chip for medical testing.

Keywords: Autoimmune diseases; Diabetic nephropathy; Machine learning; Porous silicon Bragg mirror; SERS.

MeSH terms

Humans
Metal Nanoparticles*
Porosity
Silicon*
Silver
Spectrum Analysis, Raman / methods

Substances

Silicon
Silver