Particle Packing Theory Guided Thermal Conductive Polymer Preparation and Related Properties

Quantitative Study on Reinforcing Mechanism of Nanofiller Network in Silicone Elastomer Based on Fluorescence Labeling Technology

Although there have been many theoretical studies on the enhancement effect of nanofiller networks and their interaction with elastomer molecular chains on the mechanical properties of elastomers, its mechanism description is still not completely clear. One of the main obstacles is the lack of quantitative characterization techniques and corresponding theoretical models for the three-dimensional morphology of complex nanofiller networks. In this paper, the precipitated silica-filled silicone rubber was studied by fluorescence labeling combined with laser scanning confocal microscopy, and the real three-dimensional images of dispersion and aggregation structure of filled rubber systems were obtained. The microstructure evolution of nano-particle aggregates caused by the increase in the filler volume fraction was quantitatively described, and the reinforcement mechanism of elastomers with a distribution of aggregates and filler networks composed of nanoparticles was studied. Furthermore, a nano-composite reinforcement model based on volume fraction, particle shape, interaction, and filler dispersion has been proposed.

Investigation of the Fabrication of Diamond/SiC Composites Using α-Si3N4/Si Infiltration

Diamond/SiC (Dia/SiC) composites possess excellent properties, such as high thermal conductivity and low thermal expansion coefficient. In addition, they are suitable as electronic packaging materials. This study mainly optimized the diamond particle size packing and liquid-phase silicon infiltration processes and investigated a method to prevent the adhesion of the product to molten silicon. Based on the Dinger-Funk particle stacking theory, a multiscale diamond ratio optimization model was established, and the volume ratio of diamond particles with sizes of D20, D50, and D90 was optimized as 1:3:6. The method of pressureless silicon infiltration and the formulas of the composites were investigated. The influences of bedding powder on phase composition and microstructure were studied using X-ray diffraction and scanning electron microscopy, and the optimal parameters were obtained. The porosity of the preform was controlled by regulating the feeding amount through constant volume molding. Dia/SiC-8 exhibited the highest density of 2.73 g/cm3 and the lowest porosity of 0.6%. To avoid adhesion between the sample and buried powder with the bedding silicon powder, a mixed powder of α-Si3N4 and silicon was used as the buried powder and the related mechanisms of action were discussed.

Exploring Lipoic Acid-Mediated Dynamic Bottlebrush Elastomers as a New Platform for the Design of High-Performance Thermally Conductive Materials

The development of high-performance thermally conductive interface materials is the key to unlocking the serious bottleneck of modern microelectronic technology through enhanced heat dispersion. Existing methods that utilize silicone composites rely either on loading large doses of randomly distributed thermal conductive fillers or on filling prealigned thermal conductive scaffolds with liquid silicone precursors. Both approaches suffer from several limitations in terms of physical traits and processability. We describe an alternative approach in which malleable silicone matrices, based on the dynamic cyclic disulfide nature cross-linker (α-lipoic acid), are readily prepared using ring-opening polymerization. The mechanical properties of the resultant dynamic silicone matrix are readily tunable. Stress-dependent depolymerization of the disulfide network demonstrates the ability to reprocess the silicone elastomer matrix, which allows for the fabrication of highly efficient thermal conductive composites with a 3D interconnecting, thermally conductive network (3D-graphite/MxBy composites) via in situ methods. Applications of the composites as thermal dispersion interface materials are demonstrated by LEDs and CPUs, suggesting great potential in advanced electronics.

Multi-scale hybrid spherical graphite composites: a light weight thermal interface material with high thermal conductivity and simple processing technology

In consideration of low density and high intrinsic thermal conductivity, spherical graphite powders can act as promising fillers for light weight thermal interface materials. Herein, spherical artificial graphite derived composites exhibit a similar thermal conductivity and significantly reduced bulk density compared with traditional Al₂O₃-derived composites. Further, based on the particle packing theory, an innovatively optimized calculation method has been proposed by introducing the quadratic programming method into the traditional calculation method to acquire the optimum formulation of multi-scale spherical graphite particles. The thermal conductivity of the optimum formulation-derived composites attains 1.994 W m⁻¹ K⁻¹, which is 1.72 times higher than that of the single particle size-derived composites (1.156 W m⁻¹ K⁻¹), accompanied by a low density of 1.812 g cm⁻³vs. the 2.31 g cm⁻³ of the traditional Al₂O₃-derived composites. Besides, the relationships between the tap density of the graphite powders, thermal conductivity and maximum filling content of the composites are creatively established, which are available for predicting the thermal conductivities of composites by simply testing the tap density of the fillers. This present work provides an instructional strategy to optimize spherical filler particles for thermal management of electronic devices.

A new carbon-based thermal conductive filler, a new calculation method of multi-scale spherical particle mixing and a model for predicting thermal conductivity by tap density.

Deterministic Manipulation of Heat Flow via Three-Dimensional-Printed Thermal Meta-Materials for Multiple Protection of Critical Components

Heat dissipation is necessary for the safer operation of high-power electronic devices and high-capacity batteries. Thermal meta-materials can efficiently manipulate heat flow by molding natural materials into specific structures. In this study, we construct a three-dimensional-printed meta-material structure with efficient and deterministic heat conduction through combining the 2D boron nitride (BN) with nano-diamond (DM) bridging. A research of thermal conductivity and dielectric properties exhibits that the nanosized diamond-bridged and oriented 2D boron nitride endows efficient heat transfer and maintains low dielectric loss with low filler loading. The composites loaded with 19 wt% BN platelets and 1 wt% DM have the highest thermal conductivity of 3.687 W/(m·K) in the heat flow orientation, while the thermal conductivity is only 0.632 W/(m·K) in the vertical heading of heat flow. The thermal conductive networks with thermal meta-materials based on the structural characteristics have been designed to secure critical device components from the heat source and dissipate heat flow in a definite way. The infrared images show that the temperature difference of monitoring points in different directions on the BN-oriented composite substrate is 9 °C, which realizes the protection of the heat source and key components. This study shows the latent capacity of 3D-printed structured materials for critical device component protection and heat administration applications in electronic devices and electric equipment.

Thermally Conductive and Electrically Insulated Silicone Rubber Composites Incorporated with Boron Nitride−Multilayer Graphene Hybrid Nanofiller

Thermally conductive and electrically insulating composites are important for the thermal management of new generation integrated and miniaturized electronic devices. A practical and eco−friendly electrostatic self−assembly method was developed to prepare boron nitride−multilayer graphene (BN−MG) hybrid nanosheets. Then, BN−MG was filled into silicone rubber (SR) to fabricate BN−MG/SR composites. Compared with MG/SR composites with the same filler loadings, BN−MG/SR composites exhibit dramatically enhanced electrical insulation properties while still maintaining excellent thermal conductivity. The BN−MG/SR with 10 wt.% filler loading shows a thermal conductivity of 0.69 W·m⁻¹·K⁻¹, which is 475% higher than that of SR (0.12 W·m⁻¹·K⁻¹) and only 9.2% lower than that of MG/SR (0.76 W·m⁻¹·K⁻¹). More importantly, owing to the electron blocking effect of BN, the electron transport among MG sheets is greatly decreased, thus contributing to the high−volume resistivity of 4 × 10¹¹ Ω cm for BN−MG/SR (10 wt.%), which is fourorders higher than that of MG/SR (2 × 10⁷ Ω·cm). The development of BN−MG/SR composites with synergetic properties of high thermal conductivity and satisfactory electrical insulation is supposed to be a promising candidate for practical application in the electronic packaging field.

Optimization of Effective Thermal Conductivity of Thermal Interface Materials Based on the Genetic Algorithm-Driven Random Thermal Network Model

Polymer-based thermal interface materials (TIMs) are indispensable for reducing the thermal contact resistance of high-power electronic devices. Owing to the low thermal conductivity of polymers, adding multiscale dispersed particles with high thermal conductivity is a common approach to enhance the effective thermal conductivity. However, optimizing multiscale particle matching, including particle size distribution and volume fraction, for improving the effective thermal conductivity has not been achieved. In this study, three kinds of filler-loaded samples were prepared, and the effective thermal conductivity and average particle size of the samples were tested. The finite element model (FEM) and the random thermal network model (RTNM) were applied to predict the effective thermal conductivity of TIMs. Compared with the FEM, the RTNM achieves higher accuracy with an error less than 5% and higher computational efficiency in predicting the effective thermal conductivity of TIMs. Combining the abovementioned advantages, we designed a set of procedures for an RTNM driven by the genetic algorithm (GA). The procedure can find multiscale particle-matching ways to achieve the maximum effective thermal conductivity under a given filler load. The results show that the samples with 40 vol %, 50 vol %, and 60 vol % filler loading have similar particle size distribution and volume fractions when the effective thermal conductivity reaches the highest. It should be emphasized that the optimized effective thermal conductivity can be improved obviously with the increase in the volume fraction of the filler loading. The high efficiency and accuracy of the procedure show great potential for the future design of high-efficiency TIMs.

Al2O3 Dispersion-Induced Micropapillae in an Epoxy Composite Coating and Implications in Thermal Conductivity

Al₂O₃ particles with different sizes were dispersed into an epoxy precursor to improve the thermal conductivity (TC) of the epoxy coating. Al₂O₃ particles tend to aggregate in epoxy, and the aggregation becomes more apparent (formation of micropapillae when the particle size is larger than 1 μm) with the increase of particle size. The calculated fast aggregation rates of various-size Al₂O₃ particles in epoxy showed that the fast aggregation rate increased to a maximum rate of 6.37 × 10^-20 m³·s^-1 at a particle size of 200 nm and then decreased to a plateau value with the increase of particle size. The high fast aggregation rate caused the aggregation and the formation of nano- and micropapillae, causing the heterogeneous distribution of Al₂O₃ particles. These micropapillae were separated by epoxy, which made formation of continuous pathways fail, causing the reduction of TC and heterogeneous heat distribution. The highest thermal conductivity of 2.52 W/m·K and uniform heat distribution were observed at the optimum filler size of 30 nm. The research findings provide the knowledge of optimizing particle size on constructing a thermally conductive polymer composite.