Enhancing Predictive Models for High-Fraction Nanocomposites Using Particle Contact Mechanics and Piezospectroscopy

Understanding stress distribution and load transfer mechanisms in nanocomposites with high particle volume fractions remains a critical challenge, particularly when classical analytical models neglect inter-particle interactions. This research introduces an improved framework for predicting the mechanical behavior of nanocomposites by integrating particle contact mechanics into conventional models, guided by experimental observations from piezospectroscopy.

Piezospectroscopic (PS) techniques provide a unique advantage by enabling direct, in situ measurement of stress within embedded nanoparticles. These measurements have consistently revealed that stress transfer to the particles is significantly greater than predicted by traditional models that assume non-interacting inclusions. To reconcile this gap, we incorporate an inter-particle contact term into existing predictive models. This term is further refined by accounting for the statistical probability of particle contact, thereby improving the realism and accuracy of mechanical property predictions at elevated filler loadings.

To validate the analytical approach, comparative finite element simulations were conducted, demonstrating strong agreement and reinforcing the physical significance of the proposed enhancements. The integration of contact probability and particle interactions into the predictive framework represents a key advancement in the mechanics of nanocomposites.

This work has important implications for the design and optimization of functional nanocomposites in high-performance applications. It improves the understanding of particle-to-matrix stress

transfer, supports more accurate mechanical property estimation, and offers a pathway toward the rational design of next-generation composite materials.