JOURNAL ARTICLE

Optimization of carbon nanotubes in carbon black‐filled natural rubber: Constitutive modeling and verification using finite element analysis.

  • Published In: Polymer Engineering & Science, 2025, v. 65, n. 2. P. 783 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Zhang, Jiaqi; Wang, Baokai; Yong, Zhanfu 3 of 3

Abstract

Carbon nanotubes (CNTs) are novel one‐dimensional nanomaterials with a large aspect ratio and specific surface area, exhibiting excellent electrical and thermal properties that have led to their extensive utilization in rubber nanocomposites. In this paper, the effect of CNTs and carbon black juxtaposition on the properties of natural rubber is investigated using two different structures of CNTs, namely GC‐30 and GT‐210. The experimental results show that GT‐210 CNTs exhibit superior performance when incorporated into natural rubber. In addition, finite elements are employed for constitutive modeling to facilitate meaningful explorations in mechanical calculation and characterization of rubber filled with CNTs. Simulating the mechanical behavior of rubber materials under different working conditions (tensile and compressive) offers a cost‐effective and time‐efficient approach to predicting and optimizing material performance. Highlights: Characterization of filler dispersion by Mooney–Rivlin curves.Fitting material stress–strain behavior using constitutive models.Offers an approach to predicting and optimizing material performance. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Polymer Engineering & Science. 2025/02, Vol. 65, Issue 2, p783
  • Document Type:Article
  • Subject Area:Engineering
  • Publication Date:2025
  • ISSN:00323888
  • DOI:10.1002/pen.27042
  • Accession Number:183923554
  • Copyright Statement:Copyright of Polymer Engineering & Science is the property of Wiley-Blackwell 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.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.