Hexagonal boron nitride (h-BN) is a two-dimensional material with a structure similar to graphite. Because of its white color, it is often referred to as “white graphite.” Like graphite, h-BN has a high thermal conductivity. However, it also possesses electrical insulation properties that graphite lacks, making it suitable for applications that require both thermal dissipation and electrical insulation. Additionally, h-BN is highly thermally and chemically stable, making it a promising material for thermal management in electronic components.
However, the preparation and application technologies for high-performance h-BN powders are still immature, resulting in high costs. When using traditional methods to randomly disperse h-BN in polymers, a large amount of filler is needed to achieve high thermal conductivity. This not only increases costs significantly but may also negatively impact other properties of the composite material. Therefore, the key to reducing costs is to achieve high thermal conductivity with minimal h-BN content.
To this end, researchers have proposed several methods:
1. Self-Assembled Three-Dimensional Thermal Conductive Networks
This method involves allowing the thermal conductive fillers (such as h-BN) to self-assemble into a three-dimensional network, reaching a “percolation state,” before combining it with a polymer matrix. Compared to randomly distributed fillers, this continuous network reduces the contact area between the fillers and the polymer, lowering thermal resistance and enabling smoother heat transfer. Several processes have been developed to create such networks, but the complex procedures make large-scale production challenging.
2. Orientation of Thermal Conductive Fillers
h-BN has a layered structure with a high in-plane thermal conductivity (300 W/m·K) but a much lower through-plane thermal conductivity (30 W/m·K). To fully leverage its excellent in-plane thermal conductivity, fillers can be oriented within the composite material in the following ways:
- Shear Force Orientation: h-BN is added to a flowing polymer matrix and aligned in the direction of flow using shear or tensile forces (common methods include injection molding, doctor blading, and electrospinning). This method is simple but may be affected by inconsistent flow speeds.
- Electric/Magnetic Field-Assisted Orientation: A sensitive layer is applied to h-BN particles, enabling them to respond to external electric or magnetic fields and align in the desired direction. This approach yields more uniform alignment but is more costly due to the need for external fields.
3. Mixed Thermal Conductive Filler Blends
Using a single type of filler can create gaps within the polymer matrix, reducing thermal conductivity. By incorporating fillers of different types, shapes, and sizes, more efficient thermal pathways can be created. Examples include:
- Size-Graded Filler Blends: Larger h-BN particles form the primary thermal conductive pathways, while smaller particles fill the gaps between them, enhancing thermal conductivity.
- Multi-Dimensional Filler Blends: Combining h-BN with other fillers (such as silicon carbide, aluminum oxide, or carbon nanotubes) leverages synergistic effects to increase filler density and improve thermal pathways, ultimately reducing the cost of h-BN-filled polymer composites.
4. Dual-Percolation Structures
“Percolation” refers to the phenomenon where filler particles form interconnected networks when their concentration exceeds a critical threshold, transforming the material from a thermal insulator to a conductor. “Dual-percolation” involves distributing fillers within a multi-phase polymer system, where they selectively form micro-conductive networks within a specific phase or at phase interfaces. These micro-networks then create a macroscopic conductive network throughout the matrix. This approach significantly lowers the percolation threshold, enabling high thermal conductivity with minimal filler content.
These methods aim to optimize the use of h-BN’s thermal conductivity while minimizing costs, making it more viable for practical applications.
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