Enabling Data-Driven Experimentation for High-Performance Polymer Thin Film Formulations

Thumbnail Image
Files
LIU-DISSERTATION-2023.pdf (8.42 MB)
Author(s)
Liu, Aaron Li
Advisor(s)
Meredith, Carson
Editor(s)
Associated Organization(s)
Organizational Unit
Organizational Unit
School of Chemical and Biomolecular Engineering
School established in 1901 as the School of Chemical Engineering; in 2003, renamed School of Chemical and Biomolecular Engineering
Series
Series
Collections
Theses and Dissertations
Supplementary to:
Permanent Link
https://hdl.handle.net/1853/75129
Abstract
Polymer thin films are a ubiquitous class of materials, as they demonstrate unprecedented performance in countless modern applications spanning electronics, coatings, composites, clean energy, packaging, and more. However, their final formulations are time-consuming to optimize through trial-and-error, as they are generated from a myriad of component choices and processing histories. While the advent of data science approaches has uncovered the promise of polymer informatics to accelerate new developments in polymer research, several challenges exist in adopting data-driven approaches effectively for the experimentation of polymer thin films. A foremost challenge is the low availability of experimental data, pertaining to the relevant process-structure-property relationships, that would yield the requisite knowledge necessary to construct precise models. “Small data” is an inherent problem for polymer thin film formulations; because their figures of merit are performance-based and often application-specific, parameter spaces are large, and reported data is inconsistent and sparse. To bridge the small data gaps that preclude the broader adoption of polymer informatics, this body of work details a series of case studies, spanning polymer stabilizers, composite blends, and organic electronics, addressing "small data" analytics, high-throughput experimentation, and database management as key objectives for tackling the broad challenges associated with accelerated development of polymer formulation technologies.
Sponsor
Date
2023-04-26
Extent
Resource Type
Text
Resource Subtype
Dissertation
Rights Statement
Rights URI