V. Ravi Raj,
B. Chinna Ankanna,
T. Maruthavanan,
D.Raguraman,
G.Janani,
G.Nixon Samuel Vijayakumar,
Sam Stanley S G,
D.Murali,
M.Buvana,
- Associate Professor, Department of Mechanical Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, India
- Assistant Professor, Department of Mechanical Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh, India
- Associate Professor, Department of Chemistry, SONASTARCH, Sona College of Technology, Salem, Tamil Nadu, India
- Associate Professor, Department of Automobile Engineering, Bharath Institute of Science and Technology, Chennai, Tamil Nadu, India
- Assistant Professor, Department of Civil Engineering, Erode Sengunthar Engineering College, Erode, Tamil Nadu, India
- Professor, Department of Physics, R.M.K. Engineering College, Chennai, Tamil Nadu, India
- Associate Professor, Department of Mechanical Engineering, Park College of Engineering and Technology, Coimbatore, Tamil Nadu, India
- Assistant Professor, Department of Mechanical Engineering, St. Joseph’s Institute of Technology (Autonomous), Chennai, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu, India
Abstract
This study presents an AI-optimized nano-silica reinforced polymer composite phase change material (PCM) for predictive solar-thermal energy storage networks. The proposed composite combines paraffin wax, high-density polyethylene (HDPE), and uniformly dispersed nano-silica particles to improve thermal conductivity, structural stability, leakage resistance, and long-term cycling performance. The composite was fabricated through melt blending and ultrasonication-assisted nanoparticle dispersion, followed by comprehensive morphological, chemical, thermal, and thermophysical characterization using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and thermal conductivity measurements. Experimental results demonstrated homogeneous nano-silica distribution, strong polymer–particle interfacial adhesion, excellent chemical compatibility, enhanced latent heat storage, improved thermal stability, and significantly higher thermal conductivity compared with the unreinforced PCM. An artificial intelligence predictive optimization framework was further developed to model the nonlinear relationship between composite formulation and thermophysical performance, enabling accurate prediction of the optimum nano-silica loading while reducing experimental effort and material consumption. The synergy between polymer composite engineering and AI-driven optimization provides an efficient strategy for designing intelligent thermal energy storage materials with improved heat transfer, dimensional stability, and operational reliability. Overall, the developed nano-silica reinforced polymer composite PCM demonstrates considerable potential for next-generation solar-thermal energy storage systems, supporting sustainable renewable energy utilization, predictive thermal management, autonomous energy networks, and advanced smart materials for future clean energy applications under practical conditions.
Keywords: Nano-silica, thermogravimetric analysis, reinforced polymer composite, interfacial adhesion, nanoparticle dispersion
[This article belongs to Journal of Polymer & Composites ]
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Journal of Polymer & Composites
| Volume | 14 | |
| Issue | 05 | |
| Received | 07/09/2026 | |
| Accepted | 12/09/2026 | |
| Published | 16/09/2026 | |
| Publication Time | 9 Days |