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Flame retardant polyurethane foam prepared from compatible blends of ammonium ligninsulfonate-based and zinc alginate

  • Xu Zhang EMAIL logo , Shuai Ding , Zhaoqian Wang , Dehe Yuan and Zhi Wang
Published/Copyright: September 18, 2024
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Abstract

Zinc alginate (ZnAlg) was prepared and subsequently employed to modify ammonium ligninsulfonate-based polyurethane foams (PUFs). A range of analytical techniques, including thermogravimetry, integral programmed decomposition temperature, activation energy, smoke density, cone calorimetry and scanning electron microscopy, were employed to characterise the modified PUFs. The results demonstrated that PUFA15Z5 (15 % ammonium lignin sulfonate and 5 g ZnAlg) with the lowest mass loss exhibited the second-highest sample decomposition temperature (T50 %), second-highest peak temperature (TMAX2), IPDT and activation energy. Furthermore, the PUFA15Z5 sample exhibited the smallest smoke density (23.11) and the highest light transmittance (67.11 %). The peak heat release rate, total heat release, smoke production rate, total smoke release and four fire risk assessment indicators of PUFA15Z5 were the lowest, while its carbon residue was the densest. The results demonstrated that PUFA15Z5 exhibited the most favourable thermal stability, flame retardant properties and the lowest smoke toxicity. These findings provide a valuable reference for subsequent biomass-based flame retardant modified polyurethane foams (PUFs).


Corresponding author: Xu Zhang, Liaoning Key Laboratory of Aircraft Fire Explosion Control and Reliability Airworthiness Technology, Shenyang Aerospace University, Shenyang 110136, China; and School of Safety Engineering, Shenyang Aerospace University, Shenyang 110136, China, E-mail:

  1. Research ethics: Not applicable.

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

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: The financial support from Scientific Research Fund of Liaoning Provincial Education Department (Grant No. JYTMS20230261) and the Fundamental Research Funds for the Universities of Liaoning Province are greatly acknowledged.

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

  6. Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2024-06-11
Accepted: 2024-08-30
Published Online: 2024-09-18
Published in Print: 2024-11-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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