High-throughput multiplex analysis method based on Fluorescence–SERS quantum Dot-Embedded silver bumpy nanoprobes

• Published in: Applied surface science 2021-08, Vol.558, p.149787, Article 149787
• Main Authors: Cha, Myeong Geun, Son, Won Ki, Choi, Yun-Sik, Kim, Hyung-Mo, Hahm, Eunil, Jun, Bong-Hyun, Jeong, Dae Hong
• Format: Article
• Language: English
• Subjects: Dual-modal nanoprobe; High-throughput analysis; Q-SERS barcode; Quantum dot fluorescence; Surface-enhanced Raman scattering
• ISSN: 0169-4332; 1873-5584
• DOI: 10.1016/j.apsusc.2021.149787

[Display omitted] •Dual-modal using fluorescence and SERS for high-throughput multiplex analysis.•45 different FQDRSERS signals obtained from three fluorescent and 15 SERS signals.•A barcode-based machine learning identification algorithm.•A multiplex detection platform: nanoprobes and high-throughput analysis algorithm. Biological encoding, multiplex biomarker imaging, and immunoassays require a multiplex spectroscopic detection and analysis technique that uses surface-enhanced Raman scattering (SERS)-based dual-modal nanoprobes. In the present study, dual-modal fluorescence–SERS quantum dot (QD)-embedded silver bumpy nanoparticles are developed for high-throughput multiplex analysis. Forty-five different dual-modal (FRGBRSERS) nanoprobes are prepared from silica-coated silver bumpy nanoshells (AgNS@SiO2) with 15 different types of Raman label compounds and 3 types of QDs (red, green, and blue). Each FRRSERS nanoprobe produces strong SERS and fluorescence signals for multiplex analysis. Based on this dual-modality, a barcode-based machine learning algorithm that transforms spectra into barcodes and identifies chemical information is created. As a proof-of-concept experiment, single- and triplex-label spectra are identified from a library comprising 15 types of Raman labels. The multiplex detection platform comprising the FRRSERS nanoprobes and the high-throughput analysis algorithm will be extremely useful for analyzing and encoding biological targets.