Effective Thermal Conductivity Of Nanocomposites Research Articles

Effect of graphene and carbon-nitride nanofillers on the thermal transport properties of polymer nanocomposites: A combined molecular dynamics and finite element study.

Low thermal conductivity of polymers, which is one of the considerable drawbacks of commonly used composite structures, has been the focus of many researchers aiming to achieve high-performance polymer-based nanocomposites through the inclusion of highly thermally conductive fillers inside the polymer matrices. Thus, in the present study, a multiscale scheme using nonequilibrium molecular dynamics and the finite element method is developed to explore the impact of different nanosized fillers (carbon-nitride and graphene) on the effective thermal conductivity of polyethylene-based nanocomposites. We show that the thermal conductivity of amorphous polyethylene at room temperature using the reactive bond order interatomic potential is nearly 0.36±0.05W/mK. Also, the atomistic results predict that, compared to the C_{3}N and graphene nanosheets, the C_{2}N nanofilm presents a much stronger interfacial thermal conductance with polyethylene. Furthermore, the results indicate that the effective thermal conductivity values of C_{2}N-polyethylene, C_{3}N-polyethylene, and graphene-polyethylene nanocomposite, at constant volume fractions of 1%, are about 0.47, 0.56, and 0.74W/mK, respectively. In other words, the results of our models reveal that the thermal conductivity of fillers is the dominant factor that defines the effective thermal conductivity of nanocomposites.

Multiscale modeling of thermal conductivity of carbon nanotube epoxy nanocomposites

Epoxy is among the most important polymers, which is extensively employed in various technologies and applications. Nevertheless, epoxy polymers present low thermal conductivities and thus the enhancement of their thermal conductivity is an important research topic. Carbon nanotubes (CNTs) owing to their excellent thermal conductivities have been widely considered for the enhancement of the thermal conduction of epoxy polymers. In this work, we developed a combined molecular dynamics finite element multiscale modelling to investigate the heat transfer along CNT/epoxy nanocomposites. To this aim, the heat transfer between the CNT and epoxy atoms at the nanoscale was explored using the atomistic classical molecular dynamics simulations. In this case, we particularly evaluated the interfacial thermal conductance between the polymer and fillers. We finally constructed the continuum models of polymer nanocomposites representative volume elements using the finite element method in order to evaluate the effective thermal conductivity. The developed multiscale modelling enabled us to systematically analyze the effects of CNT fillers geometry (aspect ratio), diameter and volume fraction on the effective thermal conductivity of nanocomposites. Our results suggest that the interfacial thermal conductance between the CNT additives and epoxy polymer dominate the heat transfer mechanism at the nanoscale. The obtained findings in this study provide good vision regarding the enhancement of thermal conductivity of polymeric materials using highly conductive nanofillers.

Investigation of Thermal Properties of High-Density Polyethylene/Aluminum Nanocomposites by Photothermal Infrared Radiometry

In this study, thermal properties of high-density polyethylene (HDPE) filled with nanosized Al particles (80 nm) were investigated. Samples were prepared using melt mixing method up to filler volume fraction of 29 %, followed by compression molding. By using modulated photothermal radiometry (PTR) technique, thermal diffusivity and thermal effusivity were obtained. The effective thermal conductivity of nanocomposites was calculated directly from PTR measurements and from the measurements of density, specific heat capacity (by differential scanning calorimetry) and thermal diffusivity (obtained from PTR signal amplitude and phase). It is concluded that the thermal conductivity of HDPE composites increases with increasing Al fraction and the highest effective thermal conductivity enhancement of 205 % is achieved at a filler volume fraction of 29 %. The obtained results were compared with the theoretical models and experimental data given in the literature. The results demonstrate that Agari and Uno, and Cheng and Vachon models can predict well the thermal conductivity of HDPE/Al nanocomposites in the whole range of Al fractions.

Influence of nanoparticle size distribution on the thermal conductivity of particulate nanocomposites

Conventional models for the thermal conductivity of nanocomposites neglect the particle size distribution by assuming all nanoparticles to be uniform in size, although in reality most particles in a nanocomposite are not uniform and the size distribution of the nanoparticles might strongly affect the thermal conductivity of nanocomposites as demonstrated in this letter. By including the lognormal distribution of the nanoparticle size into the thermal conductivity model of nanocomposites embedded with spherical particles, we show that the mean value and the standard deviation (SD) of the nanoparticle radius significantly affect the thermal conductivity of particulate nanocomposites. At the same mean radius of embedded particles, the thermal conductivity of the nanocomposite with lognormal size distribution at can be twice that with a uniform particle size. Our improved model on effective thermal conductivity of nanocomposites with nonuniform particles can provide a more accurate prediction on the thermal conductivity of realistic particulate nanocomposites.

Generalized Effective Medium Theory for Particulate Nanocomposite Materials.

The thermal conductivity of particulate nanocomposites is strongly dependent on the size, shape, orientation and dispersion uniformity of the inclusions. To correctly estimate the effective thermal conductivity of the nanocomposite, all these factors should be included in the prediction model. In this paper, the formulation of a generalized effective medium theory for the determination of the effective thermal conductivity of particulate nanocomposites with multiple inclusions is presented. The formulated methodology takes into account all the factors mentioned above and can be used to model nanocomposites with multiple inclusions that are randomly oriented or aligned in a particular direction. The effect of inclusion dispersion non-uniformity is modeled using a two-scale approach. The applications of the formulated effective medium theory are demonstrated using previously published experimental and numerical results for several particulate nanocomposites.

Size effect on the thermal intensification of alumina-filled nanocomposites

This work comprises an experimental investigation of thermal intensification in polymeric nanocomposites. Polyester and epoxy resins were employed as continuous phases and alumina spherical nanoparticles with different diameters (30–40, 27–43, 150, and 200 nm) were used as fillers, in volumetric concentrations up to 10%. The thermal conductivities of the fabricated samples were measured using a guarded heat flow meter and the influence of filler size was investigated. Density measurements were performed for verifying the volume concentrations of particles in batches containing distinct sorts of nanoparticles. In addition, scanning electron micrographs of all samples were obtained. Results show that larger particles are responsible for larger agglomerates and greater thermal conductivity augmentation of the polymeric matrices, indicating that interfacial properties play a significant role in the effective thermal conductivity of nanocomposites.

An extended irreversible thermodynamic modelling of size-dependent thermal conductivity of spherical nanoparticles dispersed in homogeneous media

The effective thermal conductivity of nanocomposites constituted by nanoparticles and homogeneous host media is discussed from the point of view of extended irreversible thermodynamics. This formalism is particularly well adapted to the description of small length scales. As illustrations, dispersion of Si nanoparticles in Ge (respectively, SiO2in epoxy resin) homogeneous matrices is investigated, the nanoparticles are assumed to be spherical with a wide dispersion. Four specific problems are studied: the dependence of the effective thermal conductivity on the volume fraction of particles, the type of phonon scattering at the interface particle–matrix, the radius of the nanoparticles and the temperature.

Numerical Calculation of the Effective Thermal Conductivity of Nanocomposites

The effective thermal conductivity (K eff) of carbon nanotube (CNT) composites is affected by the thermal boundary resistance (TBR), the dispersion pattern, and geometry distribution of the CNTs. Traditional effective medium theories assume that CNTs are perfectly dispersed without considering TBR. In this work, we report the development of a new algorithm using CNTs with 3-D worm-like geometry and different persistence lengths. We describe how to obtain K eff using simulations of these realistic CNT configurations with off-lattice Monte Carlo simulation. The results are compared with straight cylinder models without effects of persistence length.

A molecular dynamics study of effective thermal conductivity in nanocomposites

In this study, non-equilibrium molecular dynamics (NEMD) simulations are performed to determine the effective thermal conductivity of nanocomposites embedded with a variety of nanoparticles. The effects of orientation and arrangement of asymmetric nanoparticles, thermal property mismatch at the interface, interface density per unit volume of nanocomposite, and polydispersity of nanoparticles on the effective thermal conductivity of nanocomposites are investigated. Simulation results are compared with existing model predictions based on the effective medium approach. Results indicate that, with the same particle volume fraction, the nanocomposites that have a larger interfacial area perpendicular to heat flow or a larger interface density yield a smaller effective thermal conductivity. In addition, a larger mismatch at the interface between the host material and nanoparticle inclusions leads to a smaller effective thermal conductivity. Finally, it is found that the effective thermal conductivity of nanocomposites decreases with increasing polydispersity of embedded nanoparticles.

Using SAXS approach to estimate thermal conductivity of polystyrene/zirconia nanocomposite by exploiting strong contrast technique

In this study, the effective thermal conductivity of a polymer/ZrO 2 nanocomposite material has been calculated based on small-angle X-ray scattering (SAXS) data. These SAXS results have been used to calculate statistical two-point correlation functions of the nanostructure. A statistical continuum approach based on higher-order correlation functions has been utilized to estimate the effective thermal conductivity of this zirconia-filled polymer. A strong-contrast formulation was used to predict the effective conductivity of the nanocomposite by means of two- and three-point statistical functions with an approximation of the three-point probability functions for a two-phase media. The thermal conductivity of ZrO 2 particles is estimated using a semi-inverse application of the strong contrast technique for a buckypaper material sample. Comparison of the simulated results with our experimental results for polystyrene/ZrO 2 nanocomposite samples with different volume fractions shows satisfactory agreement. This supports the capability of our proposed methodology to predict effective thermal conductivity of nanocomposites.