Excellence in Research: Fundamental Understanding of Interfacial Adhesion of Biopolymers for Advanced Composite Materials

Renewable biomass has great potential to replace petroleum-based plastics in products ranging from packaging and consumer goods to automotive and biomedical materials. However, natural biomass varies significantly in its composition and structure, making it difficult to manufacture sustainable materials with consistent quality and performance. This project seeks to establish the scientific knowledge needed to understand how biomass interacts with polymers and how these interactions influence composite manufacturing and performance. The resulting knowledge will enable more reliable approaches for designing sustainable composite materials from renewable resources. In addition to advancing fundamental materials science, the project will strengthen research capacity at Howard University by providing interdisciplinary research and training opportunities for undergraduate and graduate students in sustainable manufacturing, materials science, and data-driven engineering. The outcomes will contribute to U.S. leadership in advanced manufacturing, support the growing circular bioeconomy, and help strengthen the STEM workforce. The goal of this project is to establish a fundamental understanding of how biomass characteristics impact interfacial adhesion with polymer matrices and how these interactions influence composite processability and performance. The research will integrate systematic biomass chemical modification, multiscale characterization, and composite manufacturing to generate comprehensive datasets linking biomass structure, interfacial interactions, processing behavior, and material properties. These experimental data will be complemented by quantum chemical calculations and molecular dynamics simulations to reveal the molecular mechanisms impacting the biomass-polymer interface. Building upon these experimental and computational datasets, the project will develop preliminary physics-informed machine learning models to establish predictive relationships among biomass characteristics, interfacial adhesion, manufacturability, and composite performance. The resulting framework will provide a scientific foundation for the predictive design and manufacturing of renewable composite materials used in advanced manufacturing applications, including lightweight structural components, compostable packaging, and biomedical products, while contributing broadly to the fields of sustainable materials science, advanced manufacturing, and the circular bioeconomy. 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.