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

Ilia L. Rasskazov, Nicolas Spegazzini, P. Scott Carney, Rohit Bhargava

Research output: Contribution to journalArticle

Abstract

(Graph Presented) Understanding the infrared (IR) spectral response of materials as a function of their morphology is not only of fundamental importance but also of contemporary practical need in the analysis of biological and synthetic materials. While significant work has recently been reported in understanding the spectra of particles with well-defined geometries, we report here on samples that consist of collections of particles. First, we theoretically model the importance of multiple scattering effects and computationally predict the impact of local particles' environment on the recorded IR spectra. Both monodisperse and polydisperse particles are considered in clusters with various degrees of packing. We show that recorded spectra are highly dependent on the cluster morphology and size of particles but the origin of this dependence is largely due to the scattering that depends on morphology and not absorbance that largely depends on the volume of material. The effect of polydispersity is to reduce the fine scattering features in the spectrum, resulting in a closer resemblance to bulk spectra. Fourier transform-IR (FT-IR) spectra of clusters of electromagnetically coupled poly(methyl methacrylate) (PMMA) spheres with wavelength-scale diameters were recorded and compared to simulated results. Measured spectra agreed well with those predicted. Of note, when PMMA spheres occupy a volume greater than 18% of the focal volume, the recorded IR spectrum becomes almost independent of the cluster's morphological changes. This threshold, where absorbance starts to dominate the signal, exactly matches the percolation threshold for hard spheres and quantifies the transition between the single particle and bulk behavior. Our finding enables an understanding of the spectral response of structured samples and points to appropriate models for recovering accurate chemical information from in IR microspectroscopy data.

Original languageEnglish (US)
Pages (from-to)10813-10818
Number of pages6
JournalAnalytical chemistry
Volume89
Issue number20
DOIs
StatePublished - Oct 17 2017

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Infrared radiation
Imaging techniques
Polymethyl Methacrylate
Scattering
Multiple scattering
Polydispersity
Fourier transforms
Wavelength
Geometry

ASJC Scopus subject areas

  • Analytical Chemistry

Cite this

Dielectric Sphere Clusters as a Model to Understand Infrared Spectroscopic Imaging Data Recorded from Complex Samples. / Rasskazov, Ilia L.; Spegazzini, Nicolas; Carney, P. Scott; Bhargava, Rohit.

In: Analytical chemistry, Vol. 89, No. 20, 17.10.2017, p. 10813-10818.

Research output: Contribution to journalArticle

Rasskazov, Ilia L. ; Spegazzini, Nicolas ; Carney, P. Scott ; Bhargava, Rohit. / Dielectric Sphere Clusters as a Model to Understand Infrared Spectroscopic Imaging Data Recorded from Complex Samples. In: Analytical chemistry. 2017 ; Vol. 89, No. 20. pp. 10813-10818.
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