JOURNAL ARTICLE

Performance evaluation of boron-based particles as filler for thermoplastic polyester elastomer.

  • Published In: Journal of Thermoplastic Composite Materials, 2025, v. 38, n. 10. P. 3718 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gul, Okan; Karsli, N Gamze; Gul, Cihat; Durmus, Ali; Yilmaz, Taner 3 of 3

Abstract

This article investigates the enhancement of thermoplastic polyester elastomer (TPEE) properties through the incorporation of two boron-based reinforcing particles: boric acid (BA) and hexagonal boron nitride (hBN). Using extrusion and injection molding, composites with varying weight ratios of BA and hBN were produced and evaluated for wear resistance, mechanical strength, and thermal stability. Results showed that both BA and hBN reduced the coefficient of friction and improved tensile strength, elastic modulus, and flexural stress at yield, with hBN generally outperforming BA due to its smaller particle size and more homogeneous dispersion. Differential scanning calorimetry revealed that BA increased TPEE crystallinity by acting as a nucleating agent, whereas hBN decreased crystallinity by impeding polymer chain crystallization. Thermogravimetric analysis indicated that both particles enhanced the thermal stability of TPEE by raising decomposition temperatures. The study concludes that both BA and hBN can effectively improve TPEE performance, with hBN recommended for applications demanding higher mechanical and tribological properties, such as automotive and aerospace sectors, and BA suited for fields like biomedical engineering and electronics where flammability and tribological performance are critical.

Additional Information

  • Source:Journal of Thermoplastic Composite Materials. 2025/10, Vol. 38, Issue 10, p3718
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2025
  • ISSN:0892-7057
  • DOI:10.1177/08927057251318711
  • Accession Number:187998216
  • Copyright Statement:Copyright of Journal of Thermoplastic Composite Materials is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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