Maqola

Investigation of thermal conductivity for nano-improved polyethylene glycol composites

Xu TaoKuala Lumpur University of Science and Technology (KLUST), Faculty of Engineering Science and Technology Jalan Ikram-Uniten, 43000, Kajang, Selangor Darul Ehsan, Malaysia. [email protected]Farag M. A. AltalbawyDepartment of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi ArabiaKrunal VaghelaChemical Engineering Department, Indian Institute of Technology, Delhi, IndiaK N Raja PraveenDepartment of Computer Science and Engineering, School of Engineering and Technology, JAIN (Deemed to Be University), Bangalore, Karnataka, IndiaAditya KashyapCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, IndiaKshamta ChauhanDepartment of CSE, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, Mohali, Punjab, 140307, IndiaBarno AbdullaevaDepartment of mathematics, National Pedagogical University of Uzbekistan, Tashkent, UzbekistanD Hima BinduKing George HospitalPrabhat Kumar SahuDepartment of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to Be University), Bhubaneswar, Odisha, 751030, IndiaNargiza KamolovaCollege of Chemical and Biological Engineering, Zhejiang University, Hangzhou, ChinaRaed H. C. AlfilhDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq. [email protected]Mehrdad MottaghiFaculty of Chemistry, Kabul University, Kabul, Afghanistan. [email protected]

2026en

ABI

Annotatsiya

Accurate prediction of thermal conductivity in (nano-PEG) composites is essential for accelerating thermal management material design. This study develops a hybrid Random Forest (RF) framework optimized using eight evolutionary algorithms, including (PSO), (GA), (WOA), (GWO), (CSA), (FPA), (FA), and (BA). A dataset of 229 experimental observations was used to model thermal conductivity as a function of temperature, PEG molecular weight, nanoparticle concentration, and nanoparticle form. Among evaluated models, the Bat Algorithm-optimized RF (RF-BA) achieved highest predictive efficiency with R 2 = 0.995406, MSE = 0.000196, and AARE = 1.291%, while the PSO-optimized model (RF-PSO) demonstrated the fastest optimization runtime (96.9 s) with competitive accuracy. Correlation and SHAP analyses revealed nanoparticle concentration as the dominant factor governing thermal conductivity (correlation coefficient = 0.75), followed by PEG molecular weight (0.56), temperature (0.33), and nanoparticle form (0.24). The results demonstrate that evolutionarily optimized ensemble learning provides a reliable and computationally efficient strategy for thermophysical property prediction in nano-PEG composites, offering a practical alternative to extensive experimental characterization.

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DOI: 10.1186/s11671-026-04677-5

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