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<p>The stability and specific heat capacity (SHC) of hybrid nanofluids (HNF) play a decisive role in heat transfer and storage systems. A slight instability in HNF can deteriorate the efficacy of the thermal application. Thermal systems undergo repetitive heat-cool-heat cycles, which have not been rigorously studied in the literature. This study introduces and explores the application of a DSC heat–cool–heat cycling protocol to diamond/graphene (D-GNP) hybrid nanofluids in thermal oil using Differential Scanning Calorimetry (DSC) to evaluate reversibility and stability of the hybrid nanofluid. The D-GNP-based HNFs are prepared by a two-step method with 0.4, 0.8, 1.2, 1.6, and 2.0% mass loadings. D-GNP HNFs are subjected to heat–cool–heat cycles in the temperature range of 303.15–371.15&#xa0;K. The results illustrate the consistency of D-GNP-based HNFs with minimal variation in SHC values during heat-cool-heat cycles. The SHC increases with increasing temperature but decreases with increasing loading of D-GNP. Thermogravimetric Analysis (TGA) is also conducted, confirming the thermal stability of all HNFs samples and displaying no signs of degradation in the studied temperature range. This research also develops a machine learning algorithm (ANN model) to precisely predict SHC as a function of temperature and weight concentration. ANN model predicted the experimental results accurately to <i>R</i><sup>2</sup> = 0.9998 and MSE = 2.29E-05, which indicates the robustness of the ANN model for capturing complex trends between the datasets for thermal modeling. These findings contribute to a deeper understanding of the dynamic thermal cyclic stability and SHC behavior of D-GNP-based HNFs and support their integration in next-generation thermal energy systems.</p>

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DSC Evaluation of Heat–Cool–Heat Cycling on Diamond/Graphene Hybrid Thermal Oil Nanofluids

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • Suhaib Umer Ilyas,
  • Haris Naseer,
  • Patrice Estellé,
  • Mustafa Alsaady,
  • Anas Ahmed,
  • Noor A. Merdad,
  • Rashid Shamsuddin

摘要

The stability and specific heat capacity (SHC) of hybrid nanofluids (HNF) play a decisive role in heat transfer and storage systems. A slight instability in HNF can deteriorate the efficacy of the thermal application. Thermal systems undergo repetitive heat-cool-heat cycles, which have not been rigorously studied in the literature. This study introduces and explores the application of a DSC heat–cool–heat cycling protocol to diamond/graphene (D-GNP) hybrid nanofluids in thermal oil using Differential Scanning Calorimetry (DSC) to evaluate reversibility and stability of the hybrid nanofluid. The D-GNP-based HNFs are prepared by a two-step method with 0.4, 0.8, 1.2, 1.6, and 2.0% mass loadings. D-GNP HNFs are subjected to heat–cool–heat cycles in the temperature range of 303.15–371.15 K. The results illustrate the consistency of D-GNP-based HNFs with minimal variation in SHC values during heat-cool-heat cycles. The SHC increases with increasing temperature but decreases with increasing loading of D-GNP. Thermogravimetric Analysis (TGA) is also conducted, confirming the thermal stability of all HNFs samples and displaying no signs of degradation in the studied temperature range. This research also develops a machine learning algorithm (ANN model) to precisely predict SHC as a function of temperature and weight concentration. ANN model predicted the experimental results accurately to R2 = 0.9998 and MSE = 2.29E-05, which indicates the robustness of the ANN model for capturing complex trends between the datasets for thermal modeling. These findings contribute to a deeper understanding of the dynamic thermal cyclic stability and SHC behavior of D-GNP-based HNFs and support their integration in next-generation thermal energy systems.