Related Experiment Video
Updated: May 26, 2025
11:44
Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates
Published on: March 20, 2015
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AI in SERS sensing moving from discriminative to generative
Steven M Quarin1, Der Vang1, Ruxandra I Dima1
1Department of Chemistry, University of Cincinnati, Cincinnati, OH USA.
Npj Biosensing
|February 24, 2025
Summary
Artificial intelligence (AI) and machine learning (ML) are advancing surface-enhanced Raman scattering (SERS) sensing. Future applications will leverage AI for designing novel SERS materials and biomaterials.
Area of Science:
- Analytical Chemistry
- Materials Science
- Biomedical Engineering
Background:
- Surface-Enhanced Raman Scattering (SERS) is a powerful technique for chemical detection.
- Current SERS applications often rely on discriminative methods for analysis.
- Developing advanced SERS capabilities requires innovative material design and enhanced diagnostic tools.
Purpose of the Study:
- To review the current and future roles of AI and ML in SERS sensing.
- To highlight AI's potential in improving SERS diagnostic accuracy.
- To explore generative AI approaches for novel SERS material discovery.
Main Methods:
- Literature review of AI/ML applications in SERS.
- Analysis of discriminative AI/ML approaches for SERS diagnostics.
- Discussion of generative AI/ML for SERS material design.
Main Results:
- AI/ML are currently used for discriminative analysis in SERS, enhancing diagnostic capabilities.
- Future directions involve generative AI for designing new SERS substrates and biomaterials.
- This integration promises more sensitive and specific SERS sensing platforms.
Conclusions:
- AI and ML are transformative for SERS sensing, moving beyond analysis to material design.
- Generative AI offers a pathway to create bespoke materials for advanced SERS applications.
- The synergy between AI and SERS will drive innovation in diagnostics and beyond.
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