Nanocomposites property prediction

The modeling and simulation methods at molecular level usually employ atoms, molecules or their clusters as the basic units considered. The most popular methods include molecular mechanics (MM), MD, and MC simulation. Modeling of polymer nanocomposites at this scale is predominantly directed toward the thermodynamics and kinetics of the formation, molecular stracture, and interactions. The diagram in Figure 1 describes the equation of motion for each method and the typical properties predicted from each of them [17-22]. We introduce here the two widely used molecular scale methods— MD and MC. 

Bilotti E, Zhang R, Deng H, Baxendale M and Peijs T (2010) Fabrication and property prediction of conductive and strain sensing TPU/CNT nanocomposite fibres, J Mater Chem 20 9449-9455. 

It is well knovm that the behavior of polymer chains around the partide surface is different from that of bulk polymer chains. Depending on the nanopartide-polymer interactions, polymer in the vicinity of the nanopartide may be developed into crystallized, amorphized, or heterogeneously nudeated structures. The crystallization of polymer will dramatically affect the overall properties of polymer nanocomposites. As a result, when predicting nanocomposite properties, the morphological features and properties of the interfadal polymer must be taken into account in addition to the properties of individual nanoparticles and bulk polymer. 

The efficient design of new materials with specific properties and applications requires the property prediction of candidate materials and the use of these predictions to evaluate, screen, and guide the material synthesis. To meet this challenge, we need to extend the existing theoretical methods and develop new prediction approaches for nanocomposites. Such prediction requires some important information such as the properties of individual components (i.e., nanoparticle and polymer), processing methods and conditions, structure and morphology of the nanocomposites, and, more importantly, the nature of the interfadal region. 

In the following sections, we will discuss briefly some analytical and numerical techniques, followed by their apphcations to the property prediction of nanocomposites with a focus on nanopartide-reinforced polymer nanocomposites. 

Mechanical properties of polymer nanocomposites can be predicted by using analytical models and numerical simulations at a wide range of time- and length scales, for example, from molecular scale (e.g., MD) to microscale (e.g., Halpin-Tsai), to macroscale (e.g., FEM), and their combinations. MD simulations can study the local load transfers, interface properties, or failure modes at the nanoscale. Micromechanical models and continuum models may provide a simple and rapid way to predict the global mechanical properties of nanocomposites and correlate them with the key factors (e.g., particle volume fraction, particle geometry and orientation, and property ratio between particle and matrix). Recently, some of these models have been applied to polymer nanocomposites to predict their thermal-mechanical properties. Young s modulus, and reinforcement efficiency and to examine the effects of the nature of individual nanopartides (e.g., aspect ratio, shape, orientation, clustering, and the modulus ratio of nanopartide to polymer matrix). 

The development of polymer nanocomposites needs a comprehensive understanding of the phenomena and an accurate prediction of the material properties and behaviors at different time- and length scales. In the past, this need has significantly stimulated the theoretical and simulation efforts in nanoparticle-polymer nanocomposites. In this connection, many analytical and numerical techniques are employed to predict nanocomposite properties. 

Eor example, the effective elastic properties of silica nanopartides-reinforced polymer nanocomposites were predicted by means of various FEM-based computational models [70], induding an interphase layer around partides as a third constituent material in the prediction of the mechanical properties. Boutaleb et al. [30] studied the influence of structural characteristics on the overall behavior of silica spherical nanoparticles-polymer nanocomposites by means of analytical method and FEM. They assumed that the interphase between silica partide and polymer matrix presents a graded modulus, ranging from that of the silica to that of the polymer matrix, for example, a gradual transition from the properties of the silica to the properties of the polymer matrix (Figure 5.6). The change in elastic modulus in the interphase was described by a power law introducing a parameter linked to interfacial features. 

Various models for composite permeability as they relate to nanocomposites have been reviewed and different models have been proposed [41—44]. The simplest way to model any composite property is to use a rule of mixtures approach. Polymer nanocomposite properties, however, do not generally follow this rule. Instead, fillers with high aspect ratio particles will influence the permeability of gases through the matrix more than filler particles with lower aspect ratios. Alignment/orientation of the filler particles (with respect to the axis of gas permeation) also plays a significant role in bulk permeability. Five models are briefly described in Sections 8.5.1-8.5.5. Predictions from these models are later compared to experimental mass loss rates. 

The effect of polymer-filler interaction on solvent swelling and dynamic mechanical properties of the sol-gel-derived acrylic rubber (ACM)/silica, epoxi-dized natural rubber (ENR)/silica, and polyvinyl alcohol (PVA)/silica hybrid nanocomposites was described by Bandyopadhyay et al. [27]. Theoretical delineation of the reinforcing mechanism of polymer-layered silicate nanocomposites has been attempted by some authors while studying the micromechanics of the intercalated or exfoliated PNCs [28-31]. Wu et al. [32] verified the modulus reinforcement of rubber/clay nanocomposites using composite theories based on Guth, Halpin-Tsai, and the modified Halpin-Tsai equations. On introduction of a modulus reduction factor (MRF) for the platelet-like fillers, the predicted moduli were found to be closer to the experimental measurements. 

Morphological structures and properties of a series of poly(ethyl acrylate)/clay nanocomposites prepared by the two distinctively different techniques of in situ ATRP and solution blending were studied by Datta et al. [79]. Tailor-made PNCs with predictable molecular weights and narrow polydispersity indices were prepared at different clay loadings. WAXD and studies revealed that the in situ approach is the better option because it provided an exfoliated morphology. By contrast, conventional solution blending led only to interlayer expansion of the clay gallery. 

FEM has also been used to predict the mechanical properties of PNCs. These studies reasonably reproduced the nanocomposite microstructure (i.e., particle size, amount, and spatial arrangement) and underlined the importance of including an... 

Ergungor describes the application of on-line Raman spectroscopy and neural networks to the simultaneous prediction of temperature and crystallinity of nylon-6 nanocomposites as a function of cooling rate. The authors prefer their neural network approach because they make use of information in the entire spectrum rather than from a few bands as most studies have done.84 Van Wijk etal. of Akzo Nobel obtained a patent on the use of a Raman spectrum of a polymeric fiber to determine dye uptake and other structural or mechanical properties based on previously developed models.85... 

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