Rheological characterization and optimized predictive modeling of hexagonal boron nitride-enhanced silica-based shear-thickening fluids

Silica-based shear thickening fluid (STF) reinforced with hexagonal boron nitride (h-BN) nanoparticles was synthesized and systematically investigated over a wide range of temperatures, applied stresses, and shear rates. Experimental results revealed a pronounced, stress-dependent, nonlinear shear-thickening response with an optimal h-BN concentration range. The synergistic interaction between SiO 2 and h-BN nanoparticles promoted the formation of mechanically robust hydrocluster networks, thereby intensifying the resistance to flow under increasing shear conditions. To accurately capture and predict this complex rheological behavior, advanced machine learning (ML) models, namely Random Forest (RF) and CatBoost, were developed and further optimized using Particle Swarm Optimization (PSO). Among the developed models, the optimized CatBoost algorithm demonstrated superior predictive accuracy and generalization. Moreover, Shapley additive explanation (SHAP) analysis was employed to improve model interpretability, identifying applied stress as the most dominant parameter governing the shear-thickening response, followed by strong interactive contributions from temperature and shear rate. The h-BN concentration was found to positively contribute within an optimal range, supporting microstructural reinforcement. The proposed ML-based framework provides a reliable, generalizable approach to predicting STF rheology, offering valuable insights for the intelligent design and optimization of high-performance STFs for advanced impact protection and vibration control applications across diverse environmental conditions.