NIST Database Can Help Increase Recycling of Textiles and Clothing
However, according to the Environmental Protection Agency (EPA), in 2018 around 85% of used clothes and textiles headed to landfills and incinerators, wasting precious resources and polluting our environment. One reason is that recycling can be more expensive than landfilling, so companies have little incentive to recycle.
To help solve this problem, researchers at the National Institute of Standards and Technology (NIST) have developed a database that contains the molecular “fingerprints” of different kinds of textile fibers and that can enable more rapid, efficient sorting of fabrics at recycling centers.
“This reference data will help improve sorting algorithms and unlock the potential for high-throughput sorting, which requires less manual labor,” said Amanda Forster, a NIST materials research engineer. Forster leads the NIST project focused on keeping end-of-life textiles in the economy, a process called textile circularity. “That should reduce costs and increase efficiency, making textile recycling more economically viable.”
The problem of textile waste has been growing in recent years. One reason is fast fashion, a business model that has companies churning out large volumes of inexpensive, trendy clothes that are often quickly discarded. New types of textiles, blended textiles, and incomplete or inaccurate labeling also pose significant challenges when it comes to sorting textiles at recycling centers.
At those centers, workers sort clothing using handheld devices that shine near-infrared light. Those devices measure how much of the light passes through or scatters off the fabric, producing a unique pattern — a sort of fingerprint that can identify the type of fibers in the clothing. This technique, called near-infrared (NIR) spectroscopy, can also be used in automated conveyor belt systems. However, current techniques still require a lot of manual labor.
In recent years, recycling equipment manufacturers have increasingly used machine learning and artificial intelligence to improve their sorting algorithms. To train these algorithms, they need high-quality reference data.
That’s where NIST’s database comes in. Called the Near-Infrared Spectra of Origin-defined and Real-world Textiles, or NIR-SORT, it contains 64 different fabric types along with the NIR fingerprints they produce. The database includes pure fiber types, such as cotton and polyester; blended fiber types, such as spandex blends; and real-world fabrics taken from thrift stores.
Manufacturers of NIR scanner systems can use this database to train and test their sorting algorithms and improve the performance of their products.