Toward Quantitative Surface-Enhanced Raman Scattering with Plasmonic Nanoparticles: Multiscale View on...

Area of Science:

• Nanotechnology and Spectroscopy
• Materials Science
• Analytical Chemistry

Background:

• Surface-enhanced Raman scattering (SERS) utilizes plasmonic nanostructures to amplify Raman signals for molecular fingerprinting.
• Plasmonic nanoparticle systems offer significant signal enhancement (>10^8-10^10) due to tunable plasmonic couplings.
• Current limitations in nanoparticle uniformity, scalability, and analytical protocols hinder reliable quantitative SERS measurements.

Purpose of the Study:

• To provide a multiscale perspective on the design principles and challenges in preparing SERS nanoparticle substrates.
• To review recent advancements in uniform and controllable synthesis of plasmonic nanoparticles for SERS.
• To introduce and scrutinize emerging machine learning and AI approaches for quantitative SERS analysis.

Main Methods:

• Discussion of design principles for SERS nanoparticle substrate preparation.
• Review of recent studies on uniform and controllable synthesis of plasmonic nanoparticles.
• Exploration of machine learning and artificial intelligence for quantitative SERS data analysis.

Main Results:

• Identified limitations in nanoparticle controllability, uniformity, scalability, and molecular modification chemistry as key barriers to quantitative SERS.
• Highlighted the importance of a multiscale approach, from atomic to assembled nanostructure levels, for addressing SERS challenges.
• Demonstrated the potential of statistical and AI methods to overcome limitations in analyzing complex SERS data and estimating enhancement factors.

Conclusions:

• Addressing multiscale issues and improving synthetic control are crucial for advancing quantitative SERS.
• Machine learning and AI offer promising avenues for robust quantitative analysis of complex SERS systems.
• Standardized protocols and accurate enhancement factor estimation are needed for wider adoption of SERS in molecular quantification.