Brian Iezzi leads the development of Fibarcode, a unique and indelible textile labeling method designed for authentication, traceability, and end-of-use management. With over ten years of experience in textile manufacturing and materials research and development, Brian has a rich background that includes developing novel yarns as an intern at the Gaston College Textile Technology Center and currently serving as an R&D engineer at Parkdale Mills, the largest yarn manufacturer in the western hemisphere.
Brian earned his bachelor’s degree in textile engineering from North Carolina State University and a doctorate in materials science and engineering from the University of Michigan. During an internship with the Defense Fabric Discovery Center at MIT’s Lincoln Laboratory, he co-invented the Fibarcode technology. His efforts have secured $175,000 in state and federal grants to support Fibarcode’s development, including funding from the Michigan Translational Research and Commercialization program, UM Graham Sustainability Institute Catalyst grant, NextCycle Michigan Accelerator, and the National Science Foundation I-Corps program.
In his spare time, Brian has co-organized life cycle assessment educational workshops in Kenya, Tanzania, and Senegal for the African Materials Research Society. He also enjoys cooking, climbing, and mountain biking.
Represented Organization
Fibarcode
Key Innovation
This project introduces the Fibarcode, an engineered photonic “barcode” integrated directly into textile fibers. The photonic fiber barcode can be interlaced into fabrics and apparel using standard manufacturing processes such as weaving, knitting, or stitching. It provides a durable, scannable identifier that enables continuous data transfer from manufacturing through the end of a product’s life.
The Fibarcode can be read using handheld, portable spectroscopic systems, offering an indelible and accurate method for communicating critical item-level information such as material composition, care instructions, and provenance. By enabling automated identification and sorting of post-consumer textiles into appropriate feedstock categories, the technology directly addresses the lack of reliable labeling that currently limits textile recycling and circularity.