Abstract

The phase change materials (PCMs) used in medium-temperature thermal energy storage can store and release a large amount of latent heat. However, their use in real-world applications is often limited by poor thermal conductivity and degradation stability during repeated heating cycles. Therefore, improving the thermal reliability and kinetic behaviour of PCMs is essential for advancing high-performance energy storage systems. This research assessed the thermal stability and degradation kinetics of pure D-mannitol and its graphene nanoplatelet (GNP)-reinforced composites. It introduced a new combination of model-free kinetic modelling and machine-learning-based predictions for evaluating the thermal reliability of nano-enhanced PCMs. Five different PCM compositions (containing 0, 0.25, 0.5, 0.75, and 1 wt% GNP) were prepared using ultrasonic-assisted dispersion. Non-isothermal thermogravimetric analysis (TGA) was performed at five different heating rates (10–25 °C·min⁻¹), and the activation energies (Eₐ) were determined using the Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (FWO), and Starink model-free methods. The Starink model yielded the lowest Eₐ for pure D-mannitol (61.99 kJ·mol⁻¹ at α ≈ 0.1), while the KAS and FWO models provided mean values of 137.62 kJ·mol⁻¹ and 141.48 kJ·mol⁻¹, respectively. Incorporating GNP improved thermal stability across all compositions, with Eₐ ranging from 123.67 to 149.08 kJ·mol⁻¹. The random forest regression model achieved the best predictive accuracy (R² = 0.99, RMSE = 0.0002), outperforming both linear and polynomial models. Overall, the addition of graphene nanoplatelets significantly enhances both chemical and thermal stability, confirming their role as effective nano-additives for PCM improvement and enabling the design of thermally stable “smart” energy storage systems for solar and sustainable applications. The thermal stability of Graphene Nanoplatelets (GNP)-enhanced D-Mannitol was determined through non-isothermal TGA analysis at heating rates between 10–25 °C min-1. The decomposition behaviour of the GNP-enhanced D-Man was characterized using Model-Free Kinetic Methods and Machine Learning Models to determine the accuracy of predicting the thermal stability of the D-Man using the most significant thermal parameters.

Keywords

D-Mannitol, Graphene Nano platelets, Thermal Decomposition, Activation Energy, Machine learning,

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