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Threads of Change: Using Smart Labels to Improve Textile Recycling

Threads of Change: Using Smart Labels to Improve Textile Recycling

Decorative: A large pile of clothing

Textile waste in the United States has surged by 80% since 2000, making it among the fastest-growing waste streams. A 2017 report from the Ellen MacArthur Foundation estimated that less than 1% of clothing material is recycled, resulting in an annual loss of over $100 billion. Inaccurate fiber content information, primarily caused by missing or erroneous labels, is one of the main obstacles to large-scale recycling.

As a remedy, and building on this research team’s prior work, the researchers propose an integrated textile labeling system that provides accurate data on fiber content, dyes, and chemicals to ensure seamless information transfer from yarn and fabric manufacture to an item’s end-of-use. A version of the label has already been developed and is in the patent process. With this project, the team will prepare the label for widespread adoption by incorporating direct input from over 150 industrial end-users and leveraging the insights of the team’s industrial partner, Patagonia.

With proposed outputs that include protocols, samples, machine learning tools, and white papers, the project addresses textile circularity challenges with an industry-aligned approach. Key objectives include modifying fibers to use industry-relevant and recyclable materials, integrating them into knitted fabrics, and demonstrating low-cost, handheld readers for efficient identification. The shift to chemically-recyclable materials eliminates the need for manual label removal, while the integration into knitted fabrics aligns with the preferences of many clothing manufacturers. Designing a user-friendly fiber identification system aims to reduce barriers to adoption.

Project team: Max Shtein, PI (Materials Science and Engineering); Sean Ahlquist, Co-I (Architecture); Alanson Sample, Co-I (Electrical Engineering and Computer Science); Brian Iezzi (Parkdale Mills, Inc.)

This project received a $10,000 catalyst grant in Fall 2023.