Insights into the specific heat capacity enhancement of ternary carbonate nanofluids with SiO<sub>2</sub>nanoparticles: the effect of change in the composition ratio

Sang, Lixia; Ai, Wenming; Wu, Yuting; Ma, Chongfang

doi:10.1039/c8ra10318f

Cited by 18 publications

(9 citation statements)

References 32 publications

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“…Specific heat represents the capacity of the fluid to exchange sensible heat given a temperature change, and it is a desirable property for HTF to be used in CSP. , A higher specific heat implies that a more thermal energy per unit volume can be directly stored by the fluid, without implicating a secondary medium for thermal energy storage, and higher rates for convection heat transfer from the collector, which improves its thermal and hydraulic performance. , Previous research papers have reported either an increase − or decrease − in isobaric specific heat by dispersing a small load of nanoparticles within the fluid, thereby generating a nanofluid . The ability to tune the specific heat of the fluids has suggested potential applications in the field of CSP. , Current numerical models do not provide an adequate description of the variation of isobaric specific heat in nanofluids with respect to the base fluid, as they always predict increases or decreases, regardless.…”

Section: Introductionmentioning

confidence: 99%

Understanding the Specific Heat Enhancement in Metal-Containing Nanofluids for Thermal Energy Storage: Experimental and Ab Initio Evidence for a Strong Interfacial Layering Effect

Carrillo‐Berdugo

Midgley

Grau‐Crespo

et al. 2020

ACS Appl. Energy Mater.

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Nanofluids have emerged as an addition for thermal management and energy conversion applications. The dispersion of a small amount of solid nanoparticles occasionally leads to an unexpected enhancement in the specific heat of the dispersant fluid. This effect has technical, economic, and social significance, and for that, it has received a lot of attention from applied research, but the associated physical and chemical phenomena explaining this phenomenon are yet to be described. We report here a combined experimental and theoretical investigation of nanofluids consisting of palladium nanoplates in a typical heat transfer oil used for concentrating solar power. Their specific heat per unit volume is found to be maximally enhanced at intermediate nanoparticle concentrations, at all temperatures. This is consistent with the phenomenological description provided by the mesolayer model. Density functional theory calculations of adsorption energies and diffusion/desorption activation barriers reveal a strong interaction between the base fluid molecules and palladium surfaces, leading to a nanofluid model where the metal particles are decorated by a static layer of organic molecules. Such layering is potentially responsible for the anomalous enhancement on the thermal properties of the nanofluid, such as the specific heat. Our contribution with this work is a first step toward a complete understanding on the structure and properties of nanofluids using ab initio molecular simulation techniques rather than phenomenological descriptions only.

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Section: Introductionmentioning

confidence: 99%

Understanding the Specific Heat Enhancement in Metal-Containing Nanofluids for Thermal Energy Storage: Experimental and Ab Initio Evidence for a Strong Interfacial Layering Effect

Carrillo‐Berdugo

Midgley

Grau‐Crespo

et al. 2020

ACS Appl. Energy Mater.

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“…4, there was an optimum concentration at which it reached a maximum value. In addition to binary base fluids, the specific heat capacity of a molten salt nanofluid with ternary base fluid was investigated by Sang et al [39]. The ternary base fluid was composed of K 2 CO 3 , Li 2 CO 3 and Na 2 CO 3 with various composition ratios.…”

Section: Specific Heat Capacity Of Nanofluidsmentioning

confidence: 99%

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Assad¹,

Mahariq²,

Ghandour³

et al. 2022

Computers, Materials &Amp; Continua

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Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance. Specific heat capacity of nanofluids, as one of the thermophysical properties, performs principal role in heat transfer of thermal mediums utilizing nanofluids. In this regard, different studies have been carried out to investigate the influential factors on nanofluids specific heat. Moreover, several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids. In the current review paper, influential parameters on the specific heat capacity of nanofluids are introduced. Afterwards, the proposed models for their forecasting and modeling are proposed. According to the reviewed works, concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the models. Moreover, by using other effective factors, the accuracy and comprehensive of the models can be modified. Finally, some suggestions are offered for the upcoming works in the relevant topics.

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“…A recognized mechanism of how chemical composition affects . However, it has not been fully established (Sang et al, 2019).…”

Section: Influence Of the Chemical Composition On The ( ) Curvesmentioning

confidence: 99%

Experimental data and predictive equation of the specific heat capacity of fruit juice model systems measured with differential scanning calorimetry

Sánchez-Romero

García-Coronado

Rivera-Bautista

et al. 2021

Journal of Food Science

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Specific heat capacity (CP) is regarded as a fundamental parameter for the design, operation, and optimization of the heat transfer equipment widely used in the food industry. Using the calorimetric ASTM E1269‐11 standard procedure, the CP–temperature (CPfalse(Tfalse)) curves of fruit juice model systems prepared at different mass fractions of fructose/glucose/sucrose/citric acid/pectin and water were measured. Thus, experimental data of CP for solid samples in crystalline and amorphous states from −80 °C up to the melting temperature range and for aqueous samples from −80 to 110 °C were generated. In the tested temperature interval, the CP of crystalline, amorphous, and aqueous samples were found to be in the ranges of 0.037 ± 0.020 to 5.61 ± 0.04; 0.061 ± 0.004 to 3.12 ± 0.19, and 0.363 ± 0.05 to 3.24 ± 0.14 kJ/kg °C, respectively. Also, a generalized empirical equation based on the type and concentration of components was developed to predict the CPfalse(Tfalse) curves of the studied samples. The proposed equation exhibited a low error sum of squares (SSE < 57.3) and a high coefficient of determination (R2 > 0.927). An analysis of variance (ANOVA) was performed with a confidence level of 95% (p < 0.05). The CPfalse(Tfalse) curves were influenced by temperature, thermal transitions, water, solid types, and compound interactions. Glucose was one of the solids that most significantly influenced the CP values of samples. Practical Application The experimental specific heat capacity data and empirical equation proposed in this study are relevant to the design, evaluation, and optimization of heat transfer equipment involved in many foods and biochemical industrial processes such as cryopreservation, frozen storage, freezing, chilling, drying, and the cooking of hard candies.

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Insights into the specific heat capacity enhancement of ternary carbonate nanofluids with SiO₂nanoparticles: the effect of change in the composition ratio

Cited by 18 publications

References 32 publications

Understanding the Specific Heat Enhancement in Metal-Containing Nanofluids for Thermal Energy Storage: Experimental and Ab Initio Evidence for a Strong Interfacial Layering Effect

Understanding the Specific Heat Enhancement in Metal-Containing Nanofluids for Thermal Energy Storage: Experimental and Ab Initio Evidence for a Strong Interfacial Layering Effect

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Experimental data and predictive equation of the specific heat capacity of fruit juice model systems measured with differential scanning calorimetry

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Insights into the specific heat capacity enhancement of ternary carbonate nanofluids with SiO2nanoparticles: the effect of change in the composition ratio

Cited by 18 publications

References 32 publications

Understanding the Specific Heat Enhancement in Metal-Containing Nanofluids for Thermal Energy Storage: Experimental and Ab Initio Evidence for a Strong Interfacial Layering Effect

Understanding the Specific Heat Enhancement in Metal-Containing Nanofluids for Thermal Energy Storage: Experimental and Ab Initio Evidence for a Strong Interfacial Layering Effect

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Experimental data and predictive equation of the specific heat capacity of fruit juice model systems measured with differential scanning calorimetry

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Insights into the specific heat capacity enhancement of ternary carbonate nanofluids with SiO₂nanoparticles: the effect of change in the composition ratio