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Preparation of ER/NR foamed composites with adjustable shape memory and oil adsorption properties

  • Mingyue Pang , Haibiao Wang , Zheng Yuan , Yaotu Han , Hui Zhang and Jieting Geng EMAIL logo
Published/Copyright: June 6, 2025
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Abstract

A novel class of eco-friendly natural Eucommia rubber (ER)/natural rubber (NR) foam composites with tunable shape memory and oil adsorption capabilities has been successfully developed through optimized vulcanization-foaming integration technology. This study utilized comprehensive characterization techniques such as universal testing machine measurements, scanning electron microscopy, and complementary analytical methods to systematically evaluate the foamed composites’ mechanical properties, shape memory behavior, and oil absorption characteristics. Through these advanced characterization approaches, we elucidated the precise influence of foaming agent H concentration on both the macroscopic performance and microstructural evolution of the foam composites. When increasing the foaming agent H content, the cellular morphology exhibited substantial expansion. This structural transformation was accompanied by a marked density reduction from 0.52 g/cm3 to 0.18 g/cm3, concurrently driving a corresponding decline in tensile strength from 11.1 MPa to 4.3 MPa. Beyond these fundamental property variations, the composites manifested distinctive functional characteristics including notable shape memory effects and temperature-responsive oil absorption capabilities. Particularly noteworthy was the precisely tunable oil adsorption performance, which could be strategically modulated through controlled physical state transitions of the composites under thermal stimuli. This thermal responsiveness establishes promising potential for intelligent material applications requiring adaptive oil–water separation functionality.


Corresponding author: Jieting Geng, Key Laboratory of Rubber-Plastics, Ministry of Education/Shandong Provincial Key Laboratory of Rubber-Plastics, School of Polymer Science and Engineering, Qingdao University of Science and Technology, Qingdao, 266042, China, E-mail:

Acknowledgments

Financial support from National Natural Science Foundation of China (grant No. 22075298), National Key R&D Program of China (2022YFD2301204) is gratefully acknowledged.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

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

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: Financial support from National Natural Science Foundation of China (grant no. 22075298), National Key R&D Program of China (2022YFD2301204).

  7. Data availability: Not applicable.

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Received: 2025-03-08
Accepted: 2025-05-11
Published Online: 2025-06-06
Published in Print: 2025-08-26

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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