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Updated: Jun 13, 2025

Author Spotlight: An Efficient Methodology to Confidently Differentiate and Characterize Fentanyl Analogs
Published on: November 8, 2024
On-site SERS analysis and intelligent multi-identification of fentanyl class substances by deep machine learning
Qiao-Yan Jiang1, Yuan Zhang2, Yang Sun3
1Shanghai Key Lab of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, China (Academy of Forensic Science), Shanghai 200063, China; Department of Pathology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou 317000, Zhejiang, China.
Abstract:
As the types of fentanyl class substances continue to grow, a universal SERS sensor is essential for the application of discriminant detection of fentanyl substances. A new nanomaterial SERS sensor-Ag@Au NPs-paper was developed. The SERS sensitivity and stability of Ag@Au NPs-paper were investigated by using R6G molecule, and the results showed that Ag@Au NPs-paper has excellent performance. In combination with visual analysis and machine learning methods, Ag@Au NPs-paper has been successfully applied to the analysis of fentanyl class substances and the component identification of binary fentanyl mixtures, and thus it can be effectively used in food safety, environmental toxicants and other fields.
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