JOURNAL ARTICLE

Exploring the mechanical and thermal characteristics of polypropylene composites with recycled polyester waste as sustainable reinforcement for enhanced protection in safety helmets head band.

  • Published In: Progress in Rubber, Plastics & Recycling Technology, 2026, v. 42, n. 2. P. 161 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Periasamy, Diwahar; Manoharan, Bharathi; Arockiasamy, Felix Sahayaraj; Karuppiah, Prakalathan; Periyasamy, Bhuvana K; Ranganathan, Nalini; Natarajan, Varagunapandiyan; Dhandapani, Aravind 3 of 3

Abstract

This article investigates the mechanical and thermal properties of polypropylene (PP) composites reinforced with recycled polyester waste (RPW) to assess their suitability for safety helmet components. Composites with varying RPW content (10–50 wt%) were fabricated and tested, revealing that up to 40% RPW incorporation improved tensile strength, flexural strength, impact resistance, hardness, density, and thermal stability, as evidenced by thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and dynamic mechanical analysis (DMA). Scanning electron microscopy (SEM) showed good dispersion and interfacial adhesion between RPW and PP, contributing to enhanced composite performance. The study concludes that PP/RPW composites offer a sustainable, eco-friendly alternative for industrial applications such as safety helmets, promoting waste valorization and circular economy principles.

Additional Information

  • Source:Progress in Rubber, Plastics & Recycling Technology. 2026/05, Vol. 42, Issue 2, p161
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2026
  • ISSN:1477-7606
  • DOI:10.1177/14777606241313076
  • Accession Number:193059384
  • Copyright Statement:Copyright of Progress in Rubber, Plastics & Recycling Technology 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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