<p>The improvement of Directional Thermal Conductivity (DTC) within the polymer-based composites is important for high-performance heat management, particularly in power storage devices and technologies. The combination of electrical insulation, low density, and strong in-plane Thermal Conductivity (TC) has made hexagonal boron nitride (h-BN) a desirable filler among other materials in use. To fully utilize h-BN’s potential, challenges pertaining to its orientation, dispersion, and interfacial compatibility within the polymer matrix must be resolved. An approach to enhancing DTC in composites based on h-BN is described. To enhance filler–matrix compatibility and orientation, this review discusses surface modification procedures such as hydroxylation (acid treatment, plasma exposure, or thermal oxidation) and Silanization (silane coupling agents like KH560 and APTES). Key factors influencing DTC, including filler size, aspect ratio, orientation angle, h-BN conductivity, matrix conductivity, and interface thermal resistance, are investigated. The review also addresses isotopic engineering of boron as ¹¹B enrichment to enhance intrinsic TC while reducing phonon scattering. Fabrication methods like vacuum-assisted filtering, shear-induced methods, and magnetic/electric field alignment are discussed. This review is the first that analytically contemplates advanced characterization techniques encompassing optothermal Raman spectroscopy, MTPS, and Transient Plane Source for DTC characteristics of h-BN/polymer composites. In conclusion, the review offers a thorough framework for improving h-BN/polymer composites using alignment, isotopic engineering, and surface modification methods. It lays the groundwork for scalable, directionally conductive materials appropriate for new applications in temperature control, energy retention, and semiconductors by focusing on both production techniques and sophisticated characterization tools.</p>

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Hexagonal boron nitride for enhancing directional thermal conductivity in polymer matrices

  • Anuj Dahiwal,
  • Balasubramanian Kandasubramanian,
  • Manisha Kulthe,
  • Sajal Umak

摘要

The improvement of Directional Thermal Conductivity (DTC) within the polymer-based composites is important for high-performance heat management, particularly in power storage devices and technologies. The combination of electrical insulation, low density, and strong in-plane Thermal Conductivity (TC) has made hexagonal boron nitride (h-BN) a desirable filler among other materials in use. To fully utilize h-BN’s potential, challenges pertaining to its orientation, dispersion, and interfacial compatibility within the polymer matrix must be resolved. An approach to enhancing DTC in composites based on h-BN is described. To enhance filler–matrix compatibility and orientation, this review discusses surface modification procedures such as hydroxylation (acid treatment, plasma exposure, or thermal oxidation) and Silanization (silane coupling agents like KH560 and APTES). Key factors influencing DTC, including filler size, aspect ratio, orientation angle, h-BN conductivity, matrix conductivity, and interface thermal resistance, are investigated. The review also addresses isotopic engineering of boron as ¹¹B enrichment to enhance intrinsic TC while reducing phonon scattering. Fabrication methods like vacuum-assisted filtering, shear-induced methods, and magnetic/electric field alignment are discussed. This review is the first that analytically contemplates advanced characterization techniques encompassing optothermal Raman spectroscopy, MTPS, and Transient Plane Source for DTC characteristics of h-BN/polymer composites. In conclusion, the review offers a thorough framework for improving h-BN/polymer composites using alignment, isotopic engineering, and surface modification methods. It lays the groundwork for scalable, directionally conductive materials appropriate for new applications in temperature control, energy retention, and semiconductors by focusing on both production techniques and sophisticated characterization tools.