Thermal Feature of Sn۳N۲-LiNO۳-NaCl/Monolayer BN High Thermal Conductivity Phase Change Materials for Energy Storage

سال انتشار: 1404
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 152

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شناسه ملی سند علمی:

JR_JREE-12-1_002

تاریخ نمایه سازی: 10 اسفند 1403

چکیده مقاله:

Thermal energy storage has attracted significant attention, with a wide range of phase change materials (PCMs) being used due to their beneficial physical and chemical properties. While nitride-based salt PCMs are commonly applied for thermal heat storage, their latent heat storage capacity remains limited. This study enhances the performance of nitride-based salts for thermal energy storage by incorporating monolayer boron nitride, improving both thermal conductivity and latent heat storage capacity. The novel blend of Sn₃N₂-LiNO₃-NaCl/Monolayer boron nitride is characterized by high specific heat capacity, a high latent heat value, and a low phase change temperature, making it an excellent candidate for thermal energy storage. The addition of monolayer boron nitride to the PCMs significantly enhances the thermal conductivity, increasing it from ۱.۴۶۸ W/m·K to ۵.۵۴۳ W/m·K. Of note, these nitride-based ternary salts do not chemically react with one another; their interaction occurs purely through mixing to improve thermal properties. The novel blend also demonstrates remarkable thermal stability, with a decomposition rate of just ۰.۵% at ۶۰۰°C, a melting temperature of ۱۵۰°C, and a solidification temperature of ۱۳۰°C. The specific heat capacity of the ternary salt reaches a maximum of ۳.۵ J/g·°C, indicating a higher thermal heat flow rate, as well as increased charging and discharging rates. The heat storage capacity of the composite PCM (CPCM) is ۶۰۰ kJ/kg at ۶۰۰°C, and the combination of these PCMs prolongs thermal energy storage duration. The ternary salt demonstrates outstanding thermal stability, maintaining performance over ۱۰۰ cycles without significant mass reduction. In addition, the diffusion of the ternary salt into the monolayer's pores further enhances its effectiveness. Simulation analyses are performed using Anaconda-based Jupyter Notebook with Python.

نویسندگان

Muhammad Shehram

School of Electrical and Electronic Engineering, Universiti Sains Malaysia Engineering Campus, P.O. Box: ۱۴۳۰۰ Nibong Tebal, Pulau Pinan, Malaysia.

Muhammad Najwan Hamidi

School of Electrical and Electronic Engineering, Universiti Sains Malaysia Engineering Campus, P.O. Box: ۱۴۳۰۰ Nibong Tebal, Pulau Pinan, Malaysia.

Aeizaal Azman Abdul Wahab

School of Electrical and Electronic Engineering, Universiti Sains Malaysia Engineering Campus, P.O. Box: ۱۴۳۰۰ Nibong Tebal, Pulau Pinan, Malaysia.

Mohd Khairunaz Mat Desa

School of Electrical and Electronic Engineering, Universiti Sains Malaysia Engineering Campus, P.O. Box: ۱۴۳۰۰ Nibong Tebal, Pulau Pinan, Malaysia.

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