Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics-Reference-Cited by-同舟云学术

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

Published:2023 Issue:3 Volume:5 Page:538-570
ISSN:2516-0230
Container-title:Nanoscale Advances
language:en
Short-container-title:Nanoscale Adv.

Author:

Zhou Hong¹²^ORCID,Xu Liangge¹²³^ORCID,Ren Zhihao¹²^ORCID,Zhu Jiaqi³^ORCID,Lee Chengkuo¹²⁴^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583

2. Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore 117608

3. National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology, Harbin, 150001, China

4. NUS Suzhou Research Institute (NUSRI), Suzhou 215123, China

Abstract

This review summarizes the integration of machine learning with surface-enhanced Raman scattering and infrared absorption in terms of concepts, processes, and applications, and provides an outlook on the future development of this technology.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Materials Science,General Chemistry,Atomic and Molecular Physics, and Optics,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2023/NA/D2NA00608A

Reference312 articles.

1. Present and Future of Surface-Enhanced Raman Scattering

2. Fano Resonance Resulting from a Tunable Interaction between Molecular Vibrational Modes and a Double Continuum of a Plasmonic Metamolecule

3. Surface enhanced fluorescence

4. Infrared metamaterial for surface-enhanced infrared absorption spectroscopy: pushing the frontier of ultrasensitive on-chip sensing

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