Heat conduction mechanisms in nanofluids and suspensions

Wang, J.J.; Zheng, Ruiting; Gao, Jinwei; Chen, Gang

doi:10.1016/j.nantod.2012.02.007

Cited by 144 publications

(91 citation statements)

References 103 publications

(144 reference statements)

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“…%). 17,46 Our previous studies based on electrical conductivity, AC impedance spectroscopy, and thermal conductivity measurements have established that such nanofluids have a percolation threshold around ! ≈ 0.07% volume fraction for an ethylene glycol based dispersion.…”

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confidence: 99%

“…3d) observed for the electrical conductivity. 17,46 Here A and B are numerical constants and ! is the free-space volume fraction of the suspension which is close to unity in our case since the particle volume fraction is less than 1%.…”

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confidence: 99%

“…This is consistent with the conclusion that Brownian motion of the dispersed particles is not responsible for the observed thermal conductivity enhancement in the nanofluids. 46,[48][49][50] The effect of temperature on the viscosity of nanofluids and suspensions, however, is more complex than its effect on the thermal conductivity. To understand the thermo-rheological behavior of these nanofluids, we first consider the temperature-dependence of the suspending solvent.…”

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“…In particular the observation that the graphite flakes are closely aggregated into isolated clusters below the percolation threshold due to global surface energy minimization while above the percolation threshold, the contact between flakes weakens, as supported by our previous AC impedance studies. 17,46 The . 65 Note that both the translational and rotational Péclet numbers have similar functional forms and are proportional to…”

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Viscosity and Thermal Conductivity of Stable Graphite Suspensions Near Percolation

Wang

Marconnet

et al. 2014

Nano Lett.

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Abstract:Nanofluids have received much attention due, in part, to the range of properties possible with different combinations of nanoparticles and base fluids. In this work, we measure the viscosity of suspensions of graphite particles in ethylene glycol as a function of the volume fraction, shear rate, and temperature below and above the percolation threshold. We also measure and contrast the trends observed in the viscosity with increasing volume fraction to the thermal conductivity behavior of the same suspensions: above the percolation threshold, the slope that describes the rate of thermal conductivity enhancement with concentration reduces compared to below the percolation threshold, whereas that of the viscosity enhancement * These authors contributed equally to this work. † Corresponding author: gchen2@mit.edu 2 increases. While the thermal conductivity enhancement is independent of temperature, the viscosity changes show a strong dependence on temperature and exhibit different trends with respect to the temperature at different shear rates above the percolation threshold. Interpretation of the experimental observations is provided within the framework of Stokesian dynamics simulations of the suspension microstructure, and suggest that although diffusive contributions are not important for the observed thermal conductivity enhancement, they are important for understanding the variations in the viscosity with changes of temperature and shear rate above the percolation threshold. The experimental results can be collapsed to a single master curve through calculation of a single dimensionless parameter (a Péclet number based on the rotary diffusivity of the graphite particles). The viscosity of the graphite dispersion at room temperature is measured using a controlled stress rheometer (TA Instruments AR-G2) with a coneand-plate geometry. The viscosity results show good repeatability; during repeated measurements with the same suspension the viscosity curves coincide with each other with standard deviation less than 2%. Two key trends of viscosity with shear rate are evident in Fig. 2. First, the viscosity of the graphite suspension increases as the volume fraction increases. Second, the graphite suspension exhibits non-Newtonian behavior. Specifically, the 6 viscosity decreases with increasing shear rate (i.e. the dispersion is shear thinning), and the level of shear thinning increases for higher volume fractions. This is likely due to the graphite clusters and flakes preferentially realigning themselves along the flow direction under the application of an imposed shear stress. This structural reorganization reduces particle-particle interactions and, thus, reduces the viscosity.In a previous paper, 17 some of the present authors observed that the thermal conductivity of graphite suspensions increases more rapidly with concentration below the percolation threshold than above percolation. To further study this effect, we measured the thermal conductivity of the samples (following a similar preparation proto...

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Viscosity and Thermal Conductivity of Stable Graphite Suspensions Near Percolation

Wang

Marconnet

et al. 2014

Nano Lett.

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“…The different factors which potentially influence the heat transfer enhancement of nanofluids are Brownian motion of nanoparticles, clustering of nanoparticles, nanolayering of the liquid at the liquid/nanoparticle interface, ballistic transport and nonlocal effect, thermophoretic effect and near-field radiation [13,14]. Up to present most of the studies have been done on the Brownian motion of the nanoparticles, molecular-level layering of the liquid at the liquid/particle interface (nanolayer), nanoparticle clustering, and a combination of these factors together with other conditional parameters as temperature, nanoparticles size and volume fraction.…”

Section: Models For Thermal Conductivitymentioning

confidence: 99%

A Review of Thermal Conductivity Models for Nanofluids

Aybar

Sharifpur

Azizian

et al. 2015

Heat Transfer Engineering

203

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Preparation of Highly Thermally Conductive Hexagonal Boron Nitride‐Polyvinyl Alcohol/Polydimethylsiloxane Composite Using Combined Freeze‐Drying and Spatial Confining Forced Network Assembly Method

Li,

Qiu,

et al. 2024

Adv Eng Mater

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With the rapid advancement of electronic products, an increasing number of electronic components are being integrated onto chips, leading to a higher power consumption per unit area. Consequently, effective heat dissipation has become increasingly important. To address this heat dissipation challenge, this paper introduces and prepares a highly thermally conductive polymer composite (TCPC) based on freeze‐drying, coupled with the Spatial Confining Forced Network Assembly（SCFNA） method. The freeze‐drying process is used to create a three‐dimensional thermal conductive network. This network provides a well‐defined heat path. The SCFNA method enhances the regularity of the thermal conductive network within the matrix. Ultimately, This results in the significant thermal conductivity enhancement of the polymer composite. After freeze‐drying, when the mass ratio of hexagonal boron nitride (h‐BN) to polyvinyl alcohol (PVA) is 4:1, the sample prepared by SCFNA presents 72.6% higher thermal conductivity than the one obtained through direct infiltration. This achievement holds significant value for the advancement of polymer composites with high thermal conductivity and excellent mechanical properties. It can improve the low dissipation performance of polymer thermal conductive composite systems, meet the needs of high‐performance and low‐dissipation use of thermal management materials, and reduce the working temperature of electronic chips.This article is protected by copyright. All rights reserved.

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Heat conduction mechanisms in nanofluids and suspensions

Cited by 144 publications

References 103 publications

Viscosity and Thermal Conductivity of Stable Graphite Suspensions Near Percolation

Viscosity and Thermal Conductivity of Stable Graphite Suspensions Near Percolation

A Review of Thermal Conductivity Models for Nanofluids

Preparation of Highly Thermally Conductive Hexagonal Boron Nitride‐Polyvinyl Alcohol/Polydimethylsiloxane Composite Using Combined Freeze‐Drying and Spatial Confining Forced Network Assembly Method

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