Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning...

Area of Science:

• Plasmonics and Spectroscopy
• Biomedical Engineering
• Data Science

Background:

• Surface-enhanced Raman spectroscopy (SERS) is a label-free, non-destructive technique valuable in biology and medicine.
• Plasmonics and advanced instrumentation have significantly improved SERS for trace biomolecule detection and diagnostics.
• Existing biosensing methods often lack the sensitivity and specificity required for early disease detection.

Purpose of the Study:

• To review recent developments in SERS for biosensing applications.
• To highlight the integration of machine learning techniques with SERS for biological analysis.
• To provide an interdisciplinary audience with simplified insights into SERS advancements since 2010.

Main Methods:

• Review of scientific literature on SERS in biosensing from 2010 onwards.
• Focus on plasmonic sensors for disease diagnosis (cancer, respiratory illnesses, SARS-CoV-2).
• Exploration of SERS for microorganism detection and homeland security applications.
• Analysis of machine learning applications in SERS for identification, classification, and quantification.

Main Results:

• SERS is increasingly utilized for sensitive and specific detection of various biomarkers and pathogens.
• Machine learning significantly enhances the analytical capabilities of SERS, improving accuracy in disease diagnosis.
• SERS applications extend from clinical diagnostics to environmental monitoring and security.

Conclusions:

• SERS, augmented by machine learning, represents a transformative technology for biosensing and disease diagnostics.
• The technique shows great promise for early disease detection, pathogen identification, and security applications.
• Continued interdisciplinary research will further unlock the potential of SERS in medicine and biology.