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Thermally conductive, mechanically robust alumina-incorporated polyurethane films prepared by ultraviolet light curing

  • Ming-Liao Tsai , Yong-Ming Dai , Rou-Xuan Wu and An-Ya Lo EMAIL logo
Published/Copyright: October 29, 2024
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Abstract

The development of heat dissipating composite materials for electronic systems can be expedited using alumina particles as modifiable polymer-matrix fillers. However, these composites are difficult to synthesize given the tradeoffs between thermal conductivity, insulating characteristics, and conduciveness to processing. To that end, the present study is focused on synthesizing environmentally friendly polyurethane films that could be cured by ultraviolet light within minutes, without requiring high-temperature heat treatment. Through the optimization of alumina modification, hybridization of thermally conductive fillers, filler content, and stirring time on the thermal conductivity of the polyurethane composites, this study improves the process for sustainability, low-cost, and convenient preparation. The experimental results confirmed that the use of surface modification and hybrid fillers is effective for enhancing the thermal conductivity, with the value of the polyurethane integrated with 50 wt% Al2O3 and 5 wt% Al2O3–poly (catechol/polyamine) being 76.40 wt% higher than that of pure PU (0.44 W/mK). Additionally, the use of hybrid fillers resulted in superior mechanical and thermal properties (such as tensile strain, tensile strength, thermal expansion coefficient, and temperature resistance) compared with those of pure PU and films incorporated with only a single filler.


Corresponding author: An-Ya Lo, Institute of Electro-Optical Engineering, National Taiwan Normal University, Taipei 11677, Taiwan, ROC E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Yes, all authors agree.

  3. Author contributions: Ming-Liao Tsai: idea, discussion, writing, equipments; An-Ya Lo: discussion, writing, editing; Yong-Ming Dai: discussion, conduct experiments, analysis; Rou-Xuan Wu: conduct experiments, analysis.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: All the authors state no conflict of interest.

  6. Research funding: NSTC Taiwan (Grant Nos. 111-2221-E-003-035-MY3; 111-2221-E-003-034-MY3).

  7. Data availability: Available.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/ipp-2024-0047).


Received: 2024-03-22
Accepted: 2024-08-30
Published Online: 2024-10-29
Published in Print: 2024-11-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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