Jessica Bonsu
BioE PhD Proposal Presentation
Time and Date:12PM, February 14, 2025
Location: Ford ES&T 1387
Meeting ID: 257 171 612 72
Passcode: MU3qC2oo
Advisors:
Dr. Martha Grover, Chemical and Biomolecular Engineering, Georgia Institute of Technology
Dr. Carson Meredith, Chemical and Biomolecular Engineering, Georgia Institute of Technology
Thesis Committee:
Dr. Hang Lu, Chemical and Biomolecular Engineering, Georgia Institute of Technology
Dr. Scott Danielsen, Materials Science and Engineering, Georgia Institute of Technology
Dr. Victor Fung, Computational Science and Engineering, Georgia Institute of Technology
DATA-DRIVEN DESIGN OF POLYSACCHARIDE-BASED BARRIER FILMS
The widespread use of non-degradable petroleum-based plastics in food packaging, favored for their cost-effectiveness and excellent mechanical and barrier properties, has led to severe environmental issues. Cellulose and chitin, the most abundant polysaccharides in nature, offer a promising sustainable alternative to conventional plastics. Their nanomaterials, characterized by high crystallinity and strong hydrogen bonding, enable excellent mechanical and barrier properties, making them ideal candidates for developing barrier films to substitute petroleum-based plastics. To create high-performance barrier films, it is essential to minimize the transport of moisture and oxygen through the film, ensuring the preservation of packaged goods' quality, while maintaining mechanical stability. Achieving this requires a deep understanding of the process-structure-property (PSP) relationships for these films to optimize their performance. Traditionally, the discovery and development of new materials has been a time-consuming and resource-intensive process, often relying on trial-and-error, also known as the Edisonian approach, to discover and design materials within a span of decades. However, as a growing field, materials informatics offers great potential for accelerating the process of developing high-performing sustainable materials. Therefore, this thesis proposal aims to leverage data-driven materials informatics approaches to model PSP relationships in polysaccharide-based barrier films. Machine learning and data science techniques will be applied on curated experimental data to develop PSP models that guide future experiments aimed at enhancing barrier film performance. To evaluate the practical applicability of the developed models, predicted high-performing barrier films will be fabricated and tested after exposure to environmental conditions commonly faced by packaging materials.