Feasibility Study: Statistical Modelling of Microstructural Variables in Particulate Filled Composite Materials

随着纳米技术的发展，颗粒增强聚合物正成为越来越重要的一类材料。现代材料可能是在许多尺度上既含有纤维又含有颗粒的复合材料。颗粒相的微观结构可以变化很大。这些变化包括颗粒大小和分布的变化。这项可行性研究的总体目标是探索统计建模在定义颗粒复合材料力学性能的微观结构依赖中的作用。在可行性研究中将包括的微观结构参数是颗粒大小和颗粒分布。这项建议是受到目前正在进行的工作的启发。为了获得颗粒改性聚合物的最佳性能，我们认为颗粒应该分散得很好，每个颗粒都应该被聚合物润湿。如果不是这种情况，那么聚集将作为缺陷导致性能降低而不是任何增强。受分散性差影响的材料性能包括弹性性能（如刚度）；力量;裂缝行为、渗透率和导电性。制备配方时，实现良好的分散是一个主要挑战。一般来说，颗粒的分散，特别是纳米颗粒，由于其高表面积和与基体聚合物的不相容性，是困难的，通常需要表面处理或相容剂。然而，这并不能保证即使使用超声或高剪切混合也能获得良好稳定的分散。一旦结块发生，就很难使其破碎。评估分散程度是非常主观的，并且分散可能在长度尺度上变化。例如，一个样品在宏观尺度上看起来可能是均匀的，但电子显微镜可能表明这些颗粒在微观或纳米尺度上是聚集的。消除这种主观性所需要的是一个量化分散程度的参数的数值。然而，据研究者所知，还没有定量的方法来评估颗粒的分散。目前使用的定性方法通常依赖于操作员的意见，因此一个人的“好”分散对其他人来说是“差”。提供数值来描述分散程度的能力将允许更快地筛选颗粒的表面处理并改进过程控制。它将为学术界和工业界提供评估和跟踪集聚演变的能力，而不需要当前方法所附带的主观性。

Particulate reinforced polymers are becoming an increasingly important class of materials particularly with the development of nanotechnology. Modern materials may be composites on many scales containing both fibres and particles. The microstructure of the particulate phase can be very varied. These variations include both changing particle size and distribution. The overall aim of this feasibility study is to explore the role of statistical modelling in defining the microstructural dependence of the mechanical properties of particulate composite materials. The microstructural parameters which will be included in this feasibility study will be particle size and particle distribution. This proposal has been inspired by work currently being undertaken. To obtain the best properties from a particle-modified polymer, it is considered that the particles should be well-dispersed, and each particle should be wetted by the polymer. If this is not the case, then the agglomerates will act as defects resulting in a reduction in performance rather than any enhancement. The material properties affected by poor dispersion include elastic properties (such as stiffness); strength; fracture behaviour, permeability and conductivity. Achieving a good dispersion is a major challenge when preparing formulations. The dispersion of particles in general, and especially nanoparticles, is difficult due to the high surface area and incompatability with the matrix polymer, and generally a surface treatment or compatabiliser is required. However, this does not guarantee that a good stable dispersion will be achieved even when ultrasonication or high-shear mixing is used. Once agglomeration occurs it is very difficult to break up the agglomerates.Assessing the degree of dispersion is very subjective, and the dispersion may vary across the length scales. For example a sample may look homogeneous at the macroscale, but electron microscopy may indicate that the particles are agglomerated at the micro- or nanoscale. What is required to remove this subjectivity is a numerical value of a parameter that quantifies the degree of dispersion. However, to the investigators' knowledge, there are no quantitative methods to assess the dispersion of particles. The qualitative methods currently used generally rely on an operator's opinion, and hence one person's 'good' dispersion is 'poor' to someone else. The ability to provide a numerical value to describe the degree of dispersion will allow faster screening of surface treatments for particles and improved process control. It will provide both academia and industry with the ability to assess and follow the evolution of agglomeration, without the subjectivity attached to current methods.