Title : Effect of agglomeration on the elastic properties of PLA/CNT nanocomposites: Numerical approach
Abstract:
In recent years, there has been a notable increase in interest in nanocomposites with polymer matrices, driven by their potential to enhance material properties with minimal inclusion volumes. This improvement is facilitated by the reinforcement effect at nanoscale. Furthermore, employing a biobased and biodegradable matrix, such as polylactic acid (PLA), emerges as a key strategy for reducing environmental impact by replacing petroleum-based polymers.
To enable the effective application of such materials in structures subjected to mechanical loads, a thorough investigation of their behavior is essential. Numerical and analytical modeling plays pivotal role in this context, offering significant time savings and ensuring cost-effectiveness in the adoption of these materials. However, the precision of these models hinges on their ability to account for the full nanostructure of the material. Factors such as the shape, phase stiffness, and orientation of reinforcements have been extensively investigated and incorporated into well-established models, including Mori-Tanaka approach, the double inclusion method, and finite element methods. Among these, the most robust analytical model addressing the interphase is a two-step incremental approach utilizing the Mori Tanaka scheme at each stage, referred to as MT2 in this research work.
Despite these advancements, an important aspect that remains underexplored is the effect of particle agglomeration. This phenomenon has been shown to significantly degrade mechanical properties, particularly at reinforcement fractions exceeding a percolation threshold. The deterioration is attributed to compromised interphase quality, caused by insufficient polymer infiltration when reinforcements are closely spaced within an agglomerate. While existing models provide reliable predictions, they lack a directly measurable parameter for agglomeration.
This study addresses this gap by leveraging experimental data obtained via atomic force microscopy (AFM) to quantify agglomeration and incorporate it into predictive models. The investigated samples comprise varying volume fractions of carbon nanotubes dispersed in a PLA matrix. The integration of this novel agglomeration parameter into numerical and analytical models yields results with strong alignment to experimental observations. As a result, an efficient methodology for accounting for agglomeration effects in existing prediction models has been developed, offering improved accuracy in mechanical property estimation.
