PFI-RP: Artificial colors made sustainable

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

The broader impact/commercial potential of this Partnerships for Innovation – Research Partnerships (PFI-RP) project explores a sustainable way to color the world around us. This technology has both commercial and sustainability potential as an application for vehicle body paint, structural and non-structural trim (including trim with complex geometries such as static sealing, emblems, etc.), as well as chrome replacements. The proposed technology is applicable to exterior and interior trim surfaces for transportation, infrastructure, and many other applications. Currently, humans use about 30,000 tons of chemical colorant pigments every day, creating environment impact. Some colorant products or processes are hazardous. Natural colorant pigments cannot satisfy all the requirements for certain applications. Artificially-made structural color may address some of the issues associated with natural pigment including long-term sustainability. A proposed partnership with industry will provide the opportunity to conduct extensive customer discovery, offering much needed information and guidance to translate the technology to real world applications, especially in selecting the best market-fit and most probable path toward commercialization. The proposed project explores light interaction with a man-made physical structure that produces strong color reflection or transmission. Such structural colors have received interest due to their various advantages over the natural colorants and can complement traditional pigments with enhanced visual effects and increased durability. Such artificial “color” can be designed at virtually any spectrum band, providing design freedom for the targetted products and applications. Layered thin film stack structures can create a wide-range of colors with high brightness and high chroma. The fabrication procedures only require thin film deposition, which offers scalability. Machine learning-based inverse optical design facilitates the generation of the “digital code” that can be reproduced with layered structures to generate the desired color. Most of the colors can be produced this way, including those with a highly desirable chrome look used in a vast number of applications. To lower the manufacturing cost, cost-effective, solution-based methods will be developed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.