CAREER: Aqueous Click Functionalization of Nanocellulose for Scalable Production of Property Programmable Plastics

About This Grant

NON-TECHNICAL SUMMARY Plastic waste is a growing environmental and societal challenge, with millions of tons accumulating in landfills and oceans each year. These plastics persist for centuries, harming ecosystems and public health. This research addresses this problem by creating sustainable plastics that can naturally decompose, using plant-based materials called cellulose nanofibers. These nanoscale fibers are abundant, break down safely, and derived from the non-edible parts of plants, making them a promising alternative to current commercial plastics. However, due to limited methods to modify plant nanofibers at scale, plastics made from these materials have constrained properties and functionality, preventing their widespread use. This research tackles these limitations combining eco-friendly chemistry with artificial intelligence and machine learning (AI/ML) to develop plant nanofiber-based plastics with “programmable” properties, meaning their resistance to breakage, water barrier abilities, and adhesiveness to other surfaces, etc., can be customized for applications in electronics, packaging, and biomedical devices. The approach uses water-based chemical processes instead of harmful solvents, while AI/ML accelerates innovation by predicting how chemical changes affect material performance. This research supports national interests by reducing plastic pollution, advancing U.S. leadership in sustainable manufacturing building on domestic feedstocks, and promoting biotechnology for a circular bioeconomy. Beyond environmental benefits, it invests in education and workforce development. Students will gain hands-on experience in sustainable materials and data-driven design in relevant courses, participate in industry internships, and create short educational videos for platforms like TikTok and YouTube to engage K–12 learners and the public. These efforts will enhance STEM education and prepare future leaders in biotechnology and advanced materials. TECHNICAL SUMMARY Global plastic production exceeds 400 million tons annually, with less than 10% recycled, creating severe environmental challenges. Cellulose nanofibers (CNFs), derived from non-edible plant residues, are abundant, biodegradable, and mechanically robust, making them promising candidates for sustainable plastics. However, their adoption is constrained by limited surface chemistry and the absence of scalable functionalization strategies, limiting performance tuning for diverse applications. This research develops a scalable, aqueous phase “click” chemistry platform for modular CNFs functionalization, enabling bioplastics with programmable properties. The research integrates green chemistry, polymer engineering, and machine learning through three objectives: (1) Establish catalyst-free reaction in water for selective attachment of diverse functional groups to CNFs, (2) Fabricate and characterize CNF-based bioplastics with tunable mechanical, interfacial, and physical properties, mapping structure–property relationships. (3) Implement an invertible machine learning (AI/ML) framework for bidirectional design—predicting material properties from functionalization parameters and generating functionalization “codecs” for target performance. The experimental framework includes systematic variation of functional group type and density, combined with advanced characterization to build a comprehensive dataset for predictive modeling. Educational components include a research-integrated undergraduate course, student internships, and datasets generated by student projects that feed into the machine learning model, creating a closed-loop system linking education and research. The results of this research are aimed at establishing fundamental design rules for property-programmable biotechnology-based polymers and enabling multifunctional bioplastics for advanced applications such as flexible electronics, soft robotics, and smart packaging. These outcomes are expected to advance predictive materials engineering, biotechnology and sustainable manufacturing. 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.