3D tracking of solute dynamics in heterogeneous polymer networks

Understanding molecular transport in structurally complex polymer networks is essential for advancing technologies in separation, drug delivery, and soft materials engineering. While theoretical models and ensemble measurements offer macroscopic insights, they fall short in capturing nanoscale heterogeneity and molecular-level dynamics—factors that are critical when these materials are implemented in practical applications. The development of single-molecule microscopy opens new opportunities to revisit this longstanding problem with greater resolution and precision. Recent advances in phase-engineered point spread functions (PSFs) have further extended these capabilities to three dimensions, enabling the direct visualization of solute motion in heterogeneous environments. In this thesis, I first implement three-dimensional (3D) single-molecule tracking (SMT) to investigate solute dynamics within a charged polyelectrolyte brush (PEB). Through high-throughput trajectory analysis, I uncover spatial heterogeneity and identify two distinct transport subpopulations linked to polymer structure. To address the limitations in analyzing fast-diffusing molecules, I then develop D-Blur, a deep learning model that extracts diffusion coefficients directly from motion-blurred PSFs without trajectory linking. Validated on both simulated and experimental datasets, D-Blur enables high-throughput, localization-free diffusion mapping. Together, these experimental and computational developments establish a framework for probing transport phenomena in complex polymer networks at the single-molecule level, opening pathways toward the rational design of functional soft materials.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemical Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Fan, Dongyu

Contributors dc:contributor

• Landes, Christy F.
• Sing, Charles E
• Su, Xiao
• Kuenstler, Alexa S.

• single molecule tracking
• single-molecule microscopy
• polymer networks
• transport
• deep learning

Statement dc:rights

• Copyright 2025 Dongyu Fan

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129407

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129407