QM/Classical Modeling of Surface Enhanced Raman Scattering Based on Atomistic Electromagnetic Models

Area of Science:

• Computational Chemistry
• Materials Science
• Spectroscopy

Background:

• Surface-enhanced Raman scattering (SERS) is a powerful technique for analyzing molecules on plasmonic nanostructures.
• Accurate theoretical modeling of SERS requires multiscale approaches that bridge quantum mechanics and classical electrodynamics.
• Existing methods may not fully capture the complex interactions between adsorbed molecules and plasmonic substrates.

Purpose of the Study:

• To develop and present novel multiscale computational methods for simulating SERS spectra.
• To model molecular systems adsorbed on plasmonic nanostructures using quantum mechanics (QM)/frequency dependent fluctuating charge (QM/ωFQ) and fluctuating dipoles (QM/ωFQFμ) approaches.
• To validate the accuracy and reliability of the proposed QM/ωFQ and QM/ωFQFμ methods against experimental data.

Main Methods:

• A QM/classical partitioning scheme is employed, treating the plasmonic substrate with atomistic electromagnetic models (ωFQ and ωFQFμ).
• These models incorporate Drude conduction theory, classical electrodynamics, and atomistic polarizability for interband transitions.
• A phenomenological correction is included to account for quantum tunneling effects.

Main Results:

• The QM/ωFQ and QM/ωFQFμ methods successfully model the plasmonic properties of noble metal and graphene-based nanostructures.
• Calculated SERS spectra using the new methods show excellent agreement with experimental results for selected test cases.
• The approaches demonstrate robustness and reliability in predicting SERS phenomena.

Conclusions:

• The developed QM/ωFQ and QM/ωFQFμ multiscale approaches provide accurate and reliable simulations of SERS spectra.
• These methods offer a unified framework for describing plasmonic properties and SERS activity of various nanostructured materials.
• The study highlights the potential of these computational tools for advancing the understanding and design of SERS-based applications.