Optimization of Polymer Filler Materials Page: 4 of 9
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Introduction
In practice, most polymers that are used in engineering applications are highly loaded
with filler particles. These particles serve to control many critical physical properties
including strength, modulus, thermal expansion, thermal conductivity, and density.
Although materials scientists have studied filled polymer systems for many years,
surprisingly little basic understanding exists regarding how the size, shape, distribution,
surface chemical nature, and concentration of the filler particles affect the mechanical
and rheological behavior of polymers during the curing process. Thanks to recent
developments in computational modeling methods, and the revolution in raw computer
power, it is now possible to gain an understanding of filled polymer systems that would
have been impossible just a few years ago. The goal of this research is to apply state-of-
the-art computational modeling methods to the important problem of filled polymers.
There are a number of important Defense Program (DP) applications of highly filled
polymers of importance to Sandia. Most of the encapsulants used in packaging of
electronic subsystems involve epoxies, silicones, and urethanes filled with particulates
such as A12O3, glass microballoons, and beta eucryptite. These fillers are used primarily
to lower the thermal expansion coefficient of the polymer to closely match the glass and
ceramic components and circuit boards in the electronic package. This is required in
order to minimize thermal stresses that can ultimately lead to failure in the component.
An important limiting factor that controls the amount of filler that can be introduced into
the polymer is the resulting viscosity of the suspension; encapsulant processing is
normally a "potting" process requiring a relativel + v viscosity. Another crucial
application of filled polymers is in elastomeric 0-rings and seals. These systems involve
crosslinked polymers of low glass transition that are normally filled with carbon black or
silica particles to improve toughness. Indirect evidence suggests that the spatial
distribution of the filler particles changes with time leading to changes in physical
properties of O-rings in the stockpile.
Classical molecular modeling tools consist primarily of explicit 'exact' molecular
simulation (Molecular Dynamics (MD) and Monte Carlo (MC) methods) and a number of
approximate techniques ranging from crude mean field theory or perturbation theory (PT)
to sophisticated integral equation and density functional theory. Each starts with defining
a molecular entity and an intermolecular potential. Structural and thermodynamic
properties are then obtained either via (time or ensemble) averaging (MD and MC
respectively) or via (typically iterative) numerical solutions of (mostly integral) equations
derived by approximate theories.
The composite material of interest here, filler particles in a polymer fluid, is particularly
difficult. The primary reason is the disparity in size between the micron sized filler
particles on the one hand and the molecular size of the polymers (100 nm) on the other. A
brute force method involving an explicit MD simulation of both the polymer molecules
and the filler particles is doomed because the small polymer molecules relax on a much
shorter time scale than the large filler particles. As a consequence, small time steps are
needed resulting in all the computing resources being spent moving only the polymer4
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EMERSON, JOHN A.; CURRO, JOHN G. & VAN SWOL, FRANK B. Optimization of Polymer Filler Materials, report, April 1, 2001; Albuquerque, New Mexico. (https://digital.library.unt.edu/ark:/67531/metadc723956/m1/4/: accessed April 24, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.