253263 Quantification of Layered Silicates Dispersion in Polymer Nanocomposites
Over the past few decades, polymer nanocomoposites have drawn increasing interest from both academia and industry because their properties (e.g., mechanical, permeability, and gas barrier properties) are superior to those of pristine polymers and conventional modified composites. These property enhancements arise mainly as a result of excellent dispersion of the nanofillers (clays such as layered silicates; nanotubes; etc.) in the polymer matrix; if the fillers are poorly dispersed, the advantage is lost. For example, in polymer nanocomposites employing clays as nanofillers, poor dispersion, which manifests as agglomerates of clays, leads to unreinforced composites, resulting in little or no property improvements. Therefore, the key to producing high performance nanocomposites is to ensure excellent dispersion of clay particles in the polymer matrix, which in turn requires an effective method for quantifying the degree of dispersion in the manufactured product. Unfortunately, most currently available methods, which employ techniques such as X-ray diffraction (XRD), transmission election microscopy (TEM), rheological measurements, atomic force microscopy (AFM), and nuclear magnetic resonance (NMR), only provide qualitative assessments of clay dispersion, which is inadequate for quality assurance and for on-line control of dispersion. Recently proposed methods employ TEM image analysis to quantify clay dispersion in the form of particle size distribution , or particle density . However, what these techniques provide, even though quantitative, are surrogate metrics with no clear and direct indication of how the measured values can be used to distinguish good dispersions from poor ones.
To address this problem, we have developed a quantitative method involving a probability model-based analysis of TEM images, which introduces an explicit dispersion parameter δ for quantitatively distinguishing among various possible degrees of dispersion. In this presentation, we will discuss the details of our approach (which is based on using the gamma probability model to describe particle length distribution in polymer nanocomposites), show how the probability model parameters combine to produce the dispersion parameter, δ, and establish from literature data, an explicit empirical expression linking δ directly to physical dispersion states such as exfoliated dispersion (good), less exfoliated dispersion with several stacked platelets (average), and a combination of exfoliated particles and intercalated stacks (poor).
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