An insight into thermal properties of BC3-graphene hetero-nanosheets: a molecular dynamics study

Simulation of thermal properties of graphene hetero-nanosheets is a key step in understanding their performance in nano-electronics where thermal loads and shocks are highly likely. Herein we combine graphene and boron-carbide nanosheets (BC3N) heterogeneous structures to obtain BC3N-graphene hetero-nanosheet (BC3GrHs) as a model semiconductor with tunable properties. Poor thermal properties of such heterostructures would curb their long-term practice. BC3GrHs may be imperfect with grain boundaries comprising non-hexagonal rings, heptagons, and pentagons as topological defects. Therefore, a realistic picture of the thermal properties of BC3GrHs necessitates consideration of grain boundaries of heptagon-pentagon defect pairs. Herein thermal properties of BC3GrHs with various defects were evaluated applying molecular dynamic (MD) simulation. First, temperature profiles along BC3GrHs interface with symmetric and asymmetric pentagon-heptagon pairs at 300 K, ΔT = 40 K, and zero strain were compared. Next, the effect of temperature, strain, and temperature gradient (ΔT) on Kaptiza resistance (interfacial thermal resistance at the grain boundary) was visualized. It was found that Kapitza resistance increases upon an increase of defect density in the grain boundary. Besides, among symmetric grain boundaries, 5–7–6–6 and 5–7–5–7 defect pairs showed the lowest (2 × 10–10 m2 K W−1) and highest (4.9 × 10–10 m2 K W−1) values of Kapitza resistance, respectively. Regarding parameters affecting Kapitza resistance, increased temperature and strain caused the rise and drop in Kaptiza thermal resistance, respectively. However, lengthier nanosheets had lower Kapitza thermal resistance. Moreover, changes in temperature gradient had a negligible effect on the Kapitza resistance.

D-GQDs Modified Epoxy Resin Enhances the Thermal Conductivity of AlN/Epoxy Resin Thermally Conductive Composites

This article proposes a method of increasing thermal conductivity (λ) by improving the λ value of a matrix and reducing the interfacial thermal resistance between such matrix and its thermally conductive fillers. D-GQDs (graphene quantum dots modified by polyetheramine D400) with a π–π-conjugated system in the center of their molecules, and polyether branched chains that are rich in amino groups at their edges, are designed and synthesized. AlN/DG-ER (AlN/D-GQDs-Epoxy resin) thermally conductive composites are obtained using AlN as a thermally conductive and insulating filler, using D-GQDs-modified epoxy resin as a matrix. All of the thermal conductivity, electrically insulating and physical–mechanical properties of AlN/DG-ER are investigated in detail. The results show that D-GQDs linked to an epoxy resin by chemical bonds can increase the value of λ of the epoxy–resin matrix and reduce the interfacial thermal resistance between AlN and DG-ER (D-GQDs–epoxy resin). The prepared AlN/DG-ER is shown to be a good thermally conductive and insulating packaging material.

Interfacial Modulation of Graphene by Polythiophene with Controlled Molecular Weight to Enhance Thermal Conductivity

With a trend of continuing improvement in the development of electronic devices, a problem of serious heat accumulation has emerged which has created the need for more efficient thermal management. Graphene sheets (GNS) have drawn much attention with regard to heat transfer because of their excellent in-plane thermal conductivity; however, the ultrahigh interfacial thermal resistance between graphene lamellae has seriously restricted its practical applications. Herein, we describe heat transfer membranes composed of graphene which have been modified by intrinsic thermally conductive polymers with different molecular weights. The presence of macromolecular surface modifiers not only constructed the graphene heat transfer interface by π–π interactions, but also significantly enhanced the membranes’ in-plane thermal conductivity by utilizing their intrinsic heat transfer properties. Such results indicated that the in-plane thermal conductivity of the fabricated membrane exhibits a high in-plane thermal conductivity of 4.17 W m−1 K−1, which, containing the GNS modified with 6000 g/mol (Mn) of poly(3-hexylthiophene) (P3HT), was 26 times higher that of poly (vinylidene fluoride) (PVDF). The P3HT molecular chain with specific molecular weight can form more matching structure π–π interactions, which promotes thermal conductivity. The investigation of different molecular weights has provided a new pathway for designing effective interfacial structures to relieve interface thermal resistance in thermally conductive membranes.

Enhanced Thermal Conductivity of Composite Materials by Filling Sheet and Fiber Under Effect of Filler Contact

The most common non-granular fillers are sheet and fiber. When they are distributed along the heat flux direction, the thermal conductivity of composite increases greatly. Meanwhile, the filler contact also has large effect on the thermal conductivity. However, the effect of filler contact on the thermal conductivity of composite with directional fillers has not been investigated. In this paper, the combined effects of filler contact, content and orientation are investigated. The results show that the effect of filler orientation on the thermal conductivity is greater than filler contact in low filler content, and exact opposite in high filler content. The effect of filler contact on fibrous and sheet fillers is far greater than cube and sphere fillers. This rule is affected by the filler contact. The filler content of 8% is the ideal percolation threshold of composite with fibrous and sheet filler. It is lower than cube filler and previous reports. The space for thermal conductivity growth of composite with directional filler is still very large. The effect of interfacial thermal resistance should be considered in predicting the thermal conductivity of composite under high Rc (>10-4).

Predicting Interfacial Thermal Resistance by Ensemble Learning

Interfacial thermal resistance (ITR) plays a critical role in the thermal properties of a variety of material systems. Accurate and reliable ITR prediction is vital in the structure design and thermal management of nanodevices, aircraft, buildings, etc. However, because ITR is affected by dozens of factors, traditional models have difficulty predicting it. To address this high-dimensional problem, we employ machine learning and deep learning algorithms in this work. First, exploratory data analysis and data visualization were performed on the raw data to obtain a comprehensive picture of the objects. Second, XGBoost was chosen to demonstrate the significance of various descriptors in ITR prediction. Following that, the top 20 descriptors with the highest importance scores were chosen except for fdensity, fmass, and smass, to build concise models based on XGBoost, Kernel Ridge Regression, and deep neural network algorithms. Finally, ensemble learning was used to combine all three models and predict high melting points, high ITR material systems for spacecraft, automotive, building insulation, etc. The predicted ITR of the Pb/diamond high melting point material system was consistent with the experimental value reported in the literature, while the other predicted material systems provide valuable guidelines for experimentalists and engineers searching for high melting point, high ITR material systems.