Thermal conductivity of nanoscale polycrystalline ZnO thin films

Thermal conductivities (TCs) of ZnO thin films of thickness 80-276 nm prepared by sol-gel method are measured by the transient thermoreflectance (TTR) system. The obtained TCs ranging from 1.4 to 6.5 W/m K decrease while the thickness decrease. The measured TCs are much smaller than those of bulk ZnO, which is about 100 W/m K. The possible reasons for the decrease are the grain boundary and defects. The latter is the dominating one from the analysis.     

Investigation of thermal transport in colloidal silica dispersions (nanofluids)

Thermal conductivity enhancement in colloidal silica dispersions (nanofluids) is investigated experimentally using a novel optical technique. The effects of nanoparticle size, concentration, and state of aggregation are examined. New data on well dispersed systems are compared to published data obtained using the more conventional transient hot-wire technique and good agreement was found. Experimental results are also compared with model predictions for relative thermal conductivity based on effective medium theory. For systems composed of larger diameter nanoparticles (~30 nm), good agreement was found between the measured thermal conductivity enhancement and that predicted by the classical Maxwell-Garnett model. For systems composed of smaller nanoparticles (∼10 and 20 nm), thermal conductivity enhancement was reduced by as much as 10%, presumably because interfacial thermal resistance effects become important. Measurements on two systems that were induced to form gels exhibited an increase in thermal conductivity of approximately 5% relative to the well-dispersed systems. The observed increase in thermal conductivity is larger than that predicted by a recently proposed model for aggregated nanofluids.     

Thermal conductivity of nanofluids and size distribution of nanoparticles by Monte Carlo simulations

Nanofluids, a class of solid–liquid suspensions, have received an increasing attention and studied intensively because of their anomalously high thermal conductivites at low nanoparticle concentration. Based on the fractal character of nanoparticles in nanofluids, the probability model for nanoparticle’s sizes and the effective thermal conductivity model are derived, in which the effect of the microconvection due to the Brownian motion of nanoparticles in the fluids is taken into account. The proposed model is expressed as a function of the thermal conductivities of the base fluid and the nanoparticles, the volume fraction, fractal dimension for particles, the size of nanoparticles, and the temperature, as well as random number. This model has the characters of both analytical and numerical solutions. The Monte Carlo simulations combined with the fractal geometry theory are performed. The predictions by the present Monte Carlo simulations are shown in good accord with the existing experimental data.     

Entropic control over nanoscale colloidal crystals

ABSTRACT

Here the collective results of a recent body of work, which reveal how polymer architecture determines the most thermodynamically stable colloidal crystal structure in binary nanoscale colloid–polymer mixtures, are reviewed. At the nanoscale, the dimensions of the colloids, polymer segments and overall polymer size begin to converge. This may be exploited to thermodynamically stabilise a single desired crystal polymorph from a suite of competitors by leveraging the size and shape of the polymer. When each polymorph has a unique void symmetry, the entropic cost of polymer confinement in each crystal becomes significantly different. Thus, when a sufficient amount of polymer partitions into the crystal phase, the system's total free energy difference between the competing structures is significantly amplified; in some cases by up to three orders of magnitude. The focus of this discussion is primarily on selectively stabilising one of the two close-packed polymorphs over the other; however, the heuristics presented here also lend themselves to applications in other crystals. This approach to polymorph selection requires no modification of the colloids, and is entirely based on entropy. Consequently, this technique is thermodynamically complementary to many ‘bottom-up’ self-assembly approaches, which rely on energetic interactions to stabilise a single crystal structure.     

Sedimentation of nanoparticles in nanoscale colloidal suspensions

A theoretical model is developed to study the sedimentation characteristics of nanoscale colloidal suspensions (nanofluids). The influences of various deterministic and stochastic forcing parameters in the transport characteristics of the suspended nanoparticles are investigated by employing a Langevin formalism of particle transport. The role of collective particle interaction phenomena in the sedimentation of nanoparticles is analyzed by invoking the fundamental considerations of agglomeration-deagglomeration kinetics of the particulate phases. The model demonstrates the effect of particle volume fraction, particle size, and aggregate structure on the sedimentation velocity of the suspended nanoparticles. Predictions from the present model agree well with the experimental results reported in the literature.     

Adjustable thermal conductivity in carbon nanotube nanofluids

Homogeneous and stable nanofluids have been produced by suspending well dispersible multi-walled carbon nanotubes (CNTs) into ethylene glycol base fluid. CNT nanofluids have enhanced thermal conductivity and the enhancement ratios increase with the nanotube loading and the temperature. Thermal conductivity enhancement was adjusted by ball milling and cutting the treated CNTs suspended in the nanofluids to relatively straight CNTs with an appropriate length distribution. Our findings indicate that the straightness ratio, aspect ratio, and aggregation have collective influence on the thermal conductivity of CNT nanofluids.     

Effective thermal conductivity of condensed polymeric nanofluids (nanosolids) controlled by diffusion and interfacial scattering

Thermal properties of polymeric nanosolids, obtained by condensing the corresponding nanofluids, are investigated using photothermal techniques. The heat transport properties of two sets of polyvinyl alcohol (PVA) based nanosolids, TiO₂/PVA and Cu/PVA, prepared by condensing the respective nanofluids, which are prepared by dispersing nanoparticles of TiO₂ and metallic copper in liquid PVA, are reported. Two photothermal techniques, the photoacoustic and the photopyroelectric techniques, have been employed for measuring thermal diffusivity, thermal conductivity and specific heat capacity of these nanosolids. The experimental results indicate that thermal conduction in these polymer composites is controlled by heat diffusion through the embedded particles and interfacial scattering at matrix–particle boundaries. These two mechanisms are combined to arrive at an expression for their effective thermal conductivity. Analysis of the results reveals the possibility to tune the thermal conductivity of such nanosolids over a wide range using the right types of nanoparticles and right concentration.     

A new thermal conductivity model for nanofluids

In a quiescent suspension, nanoparticles move randomly and thereby carry relatively large volumes of surrounding liquid with them. This micro-scale interaction may occur between hot and cold regions, resulting in a lower local temperature gradient for a given heat flux compared with the pure liquid case. Thus, as a result of Brownian motion, the effective thermal conductivity, k_eff, which is composed of the particles conventional static part and the Brownian motion part, increases to result in a lower temperature gradient for a given heat flux. To capture these transport phenomena, a new thermal conductivity model for nanofluids has been developed, which takes the effects of particle size, particle volume fraction and temperature dependence as well as properties of base liquid and particle phase into consideration by considering surrounding liquid traveling with randomly moving nanoparticles.The strong dependence of the effective thermal conductivity on temperature and material properties of both particle and carrier fluid was attributed to the long impact range of the interparticle potential, which influences the particle motion. In the new model, the impact of Brownian motion is more effective at higher temperatures, as also observed experimentally. Specifically, the new model was tested with simple thermal conduction cases, and demonstrated that for a given heat flux, the temperature gradient changes significantly due to a variable thermal conductivity which mainly depends on particle volume fraction, particle size, particle material and temperature. To improve the accuracy and versatility of the k_effmodel, more experimental data sets are needed.     

A micro-convection model for thermal conductivity of nanofluids

Increase in the specific surface area as well as Brownian motion are supposed to be the most significant reasons for the anomalous enhancement in thermal conductivity of nanofluids. This work presents a semi-empirical approach for the same by emphasizing the above two effects through micro-convection. A new way of modeling thermal conductivity of nanofluids has been explored which is found to agree excellently with a wide range of experimental data obtained by the present authors as well as the data published in literature     

Theory for anisotropic thermal conductivity of magnetic nanofluids

Effective thermal conductivity tensor for magnetic nanofluids containing magnetizable nanoparticles suspended in a base liquid is theoretically investigated with a two-step homogenization method. First, we adopt differential effective medium theory to determine the equivalent thermal conductivity of magnetizable nanoparticle chains. Second, we generalize self-consistent anisotropic effective medium theory to study the effective thermal conductivity tensors of magnetic nanofluids. Numerical results show that the aspect ratio of chain-like aggregated clusters plays an important role in enhancement of anisotropic thermal conductivity. In addition, our theoretical results on the elements of thermal conductivity parallel to the fields Kez and perpendicular to the fields Kex are in good agreement with experimental data. Furthermore, we predict the nonmonotonic dependence of effective thermal conductivity on magnetic field strength, in accordance with experimental reports.     

Thermal conductivity of aqueous solutions of salts for high parameters of state

New generalized formulas for calculation of thermal conductivity of aqueous solutions of binary and multicomponent inorganic substances under high values of state parameters were derived. New values of thermal conductivity were calculated for aqueous solutions of salts within the ranges of temperatures of 293–473 K. concentrations of 0–25 mass % and pressures P _s of 100 MPa.     

Investigation on characteristics of thermal conductivity enhancement of nanofluids

It has been shown that a nanofluid consisting of nanoparticles dispersed in base fluid has much higher effective thermal conductivity than pure fluid. In this study, four kinds of nanofluids such as multiwalled carbon nanotube (MWCNT) in water, CuO in water, SiO₂ in water, and CuO in ethylene glycol, are produced. Their thermal conductivities are measured by a transient hot-wire method. The thermal conductivity enhancement of water-based MWCNT nanofluid is increased up to 11.3% at a volume fraction of 0.01. The measured thermal conductivities of MWCNT nanofluids are higher than those calculated with Hamilton–Crosser model due to neglecting solid–liquid interaction at the interface. The results show that the thermal conductivity enhancement of nanofluids depends on the thermal conductivities of both particles and the base fluid.     

Computational analysis of factors influencing thermal conductivity of nanofluids

Numerical investigations are conducted to study the effect of factors such as particle clustering and interfacial layer thickness on thermal conductivity of nanofluids. Based on this, parameters including Kapitza radius and fractal and chemical dimension which have received little attention by previous research are rigorously investigated. The degree of thermal enhancement is analyzed for increasing aggregate size, particle concentration, interfacial thermal resistance, and fractal and chemical dimensions. This analysis is conducted for water-based nanofluids of Alumina (Al₂O₃), CuO, and Titania (TiO₂) nanoparticles where the particle concentrations are varied up to 4 vol%. Results from the numerical work are validated using available experimental data. For the case of aggregate size, particle concentration, and interfacial thermal resistance, the aspect ratio (ratio of radius of gyration of aggregate to radius of primary particle, R _g/a) is varied from 2 to 60. It was found that the enhancement decreases with interfacial layer thickness. Also the rate of decrease is more significant after a given aggregate size. For a given interfacial resistance, the enhancement is mostly sensitive to R _g/a < 20 indicated by the steep gradients of data plots. Predicted and experimental data for thermal conductivity enhancement are in good agreement. On the influence of fractal and chemical dimensions (d _l and d _f) of Alumina–water nanofluid, the R _g/a was varied from 2 to 8, d _l from 1.2 to 1.8, and d _f from 1.75 to 2.5. For a given concentration, the enhancement increased with the reduction of d _l or d _f. It appears a distinctive sensitivity of the enhancement to d _f, in particular, in the range 2–2.25, for all values of R _g/a. However, the sensitivity of d _l was largely depended on the value of R _g/a. The information gathered from this study on the sensitivity of thermal conductivity enhancement to aggregate size, particle concentration, interfacial resistance, and fractal and chemical dimensions will be useful in manufacturing highly thermally conductive nanofluids. Further research on the refine cluster evolution dynamics as a function of particle-scale properties is underway.     

Dispersion stability and thermal conductivity of propylene glycol-based nanofluids

The dispersion stability and thermal conductivity of propylene glycol-based nanofluids containing Al₂O₃ and TiO₂ nanoparticles were studied in the temperature range of 20–80 °C. Nanofluids with different concentrations of nanoparticles were formulated by the two-step method and no dispersant was used. In contrast to the common belief, the average particle size of nanofluids was observed to decrease with increasing temperature, and nanofluids showed an excellent stability over the temperature range of interest. Thermal conductivity enhancement for both studied nanofluids was a nonlinear function of concentration and was temperature independent. Theoretical analyses were also performed using existing models compared with experimental results. The model based on the aggregation theory appears to be the best.     

A cell model approach for thermal conductivity of nanofluids

This work presents a cell model for predicting the thermal conductivity of nanofluids. Effects due to the high specific surface area of the mono-dispersed nanoparticles and the micro-convective heat transfer enhancement associated with the Brownian motion of particles are addressed in detail. Novelty of the paper lies in its prediction of the non-linear dependence of thermal conductivity of nanofluids on particle volume fraction at low particle concentrations. The model is found to correctly predict the trends observed in experimental data over a wide range of particle sizes, temperatures and particle concentrations.     

Significant thermal conductivity enhancement for nanofluids containing graphene nanosheets

We developed a facile technique to produce ethylene glycol based nanofluids containing graphene nanosheets. The thermal conductivity of the base fluid was increased significantly by the dispersed graphene: up to 86% increase for 5.0 vol % graphene dispersion. The 2D structure and stiffness of graphene and graphene oxide help to increase the thermal conductivity of the nanofluid. The thermal conductivity of graphene oxide and graphene in the fluid were estimated to be ∼4.9 and 6.8 W/m K, respectively.     

Dispersibility dependence of thermal conductivity of carbon nanotube based nanofluids

A differential effective medium theory together with Brownian motion is used to predict Effective Thermal Conductivity (ETC) of CNT nanofluids. ETC was influenced significantly by Brownian motion and enhancement was higher in dilute nanofluids. A theoretical model employing an effective volume fraction with dispersibility factor agrees well with experimental data.