Dielectric Sphere Clusters as a Model to Understand Infrared Spectroscopic Imaging Data Recorded from Complex Samples

Area of Science:

• Materials Science
• Spectroscopy
• Computational Modeling

Background:

• Understanding infrared (IR) spectral responses of materials is crucial for analyzing biological and synthetic samples.
• Previous research focused on particles with defined geometries, leaving gaps in understanding collections of particles.
• The influence of morphology on IR spectra of aggregated particles requires further investigation.

Purpose of the Study:

• To theoretically model and computationally predict the impact of particle morphology and environment on IR spectra.
• To investigate the effects of multiple scattering and particle packing on spectral responses.
• To quantify the transition between single-particle and bulk behavior in particle clusters.

Main Methods:

• Theoretical modeling of multiple scattering effects in particle clusters.
• Computational prediction of IR spectra for monodisperse and polydisperse particles.
• Experimental measurement of Fourier transform-infrared (FT-IR) spectra of poly(methyl methacrylate) (PMMA) sphere clusters.
• Comparison of experimental data with simulation results.

Main Results:

• IR spectra are highly dependent on cluster morphology and particle size, primarily due to scattering, not absorbance.
• Polydispersity reduces spectral scattering features, making spectra resemble bulk material.
• At a volume fraction >18% (percolation threshold), IR spectra become independent of morphological changes, indicating a shift to bulk behavior.

Conclusions:

• The study quantifies the transition from single-particle to bulk spectral behavior based on particle packing.
• Findings provide a framework for understanding spectral responses of structured samples.
• The results guide the selection of appropriate models for accurate chemical information retrieval in IR microspectroscopy.