Thermophysical characterization and chemical stability of Ag2O-enhanced eutectic nano-PCMs for moderate-temperature applications
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Rahul Agrawal
Abstract
Thermal energy storage utilizing phase change materials (PCMs) is important for conserving energy because of its high latent heat capacity and ability to maintain a steady temperature during the phase change. However, no single organic PCM provides all of the required qualities, such as ideal melting temperature and latent heat, to perform effectively under specified environmental circumstances. To address this, binary eutectic PCMs are prepared by mixing two or more organic PCMs and optimizing their thermophysical properties. Despite these advancements, their low thermal conductivity remains an issue, limiting overall performance. This study focuses on developing nano-enhanced eutectic phase change material consisting of 64 wt% myristic acid and 36 wt% stearic acid, further improved by incorporating Ag2O nanoparticles at concentrations of 0.3–3.0 wt%. Dispersion of nanoparticles within composite phase change material was testified by FTIR and XRD analyses. However, thermal conductivity of the eutectic PCM was significantly enhanced up to 105 % at an equivalent loading of 3.0 wt% nanoparticles. Thermal aging tests also showed thermochemical stability for both pure and composite PCMs in the case of 1,000 cycles. This paper concludes that nano-embedded eutectic PCM may be a potentially well-suited material for medium- and low-temperature applications.
Funding source: King Saud University, Riyadh, Saudi Arabia
Award Identifier / Grant number: RSPD2025R999
Acknowledgement
The authors extend their appreciation to the Researchers Supporting Project number (RSPD2025R999), King Saud University, Riyadh, Saudi Arabia.
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Research ethics: The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: No Large Language Models, artificial intelligence, or machine learning tools were used in the preparation, analysis, or writing of this work. All content was created entirely by the authors.
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Conflict of interest: Authors state no conflict of interest.
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Research funding: Researchers Supporting Project number (RSPD2025R999), King Saud University, Riyadh, Saudi Arabia.
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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