JOURNAL ARTICLE

Entropy-TOPSIS-based material selection for sustainable polymer composite: An MCDM framework promoting circular economy.

  • Published In: Journal of Elastomers & Plastics, 2025, v. 57, n. 5. P. 703 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Dasgupta, Archisman; Sen, Binayak; Dutta, Prasenjit; Rachchh, Nikunj; Patil, Nagaraj; Mahapatro, Abinash; Karthikeyan, A 3 of 3

Abstract

This article focuses on the development and evaluation of sustainable polymer composites made from recycled polyethylene terephthalate (PET) and ground tire rubber (GTR) to address environmental challenges associated with plastic and rubber waste. Using an entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making (MCDM) framework, the study identifies the 70:30 PET-GTR ratio as optimal, balancing physical, mechanical, and thermal properties for lightweight, durable, and eco-friendly applications. Characterization techniques including Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and Fourier Transform Infrared Spectroscopy (FTIR) confirm good compatibility and bonding between PET and GTR. The composites show increased tensile strength and flexural modulus with higher GTR content but decreased density, impact strength, hardness, and melting temperature, indicating trade-offs depending on application needs. The research highlights potential uses in automotive, construction, and prosthetic industries while emphasizing the importance of policy support and further studies on long-term durability and expanded material formulations.

Additional Information

  • Source:Journal of Elastomers & Plastics. 2025/08, Vol. 57, Issue 5, p703
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2025
  • ISSN:0095-2443
  • DOI:10.1177/00952443251331171
  • Accession Number:186417956
  • Copyright Statement:Copyright of Journal of Elastomers & Plastics 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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