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  4. CONNECTING THE STUDY OF COLLOIDAL AND BLOCK-COPOLYMER SYSTEMS; INSIGHTS FROM SELF ASSEMBLY

CONNECTING THE STUDY OF COLLOIDAL AND BLOCK-COPOLYMER SYSTEMS; INSIGHTS FROM SELF ASSEMBLY

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File(s)
NievesRosado_cornellgrad_0058F_14856.pdf (4.21 MB)
No Access Until
2027-06-18
Permanent Link(s)
https://doi.org/10.7298/0y80-wg67
https://hdl.handle.net/1813/117608
Collections
Cornell Theses and Dissertations
Author
Nieves-Rosado, Luis
Abstract

The study of self-assembly processes in soft matter science is one of the most important endeavors of our field. These processes, where carefully designed building blocks come together to form larger structures with targeted properties, are key to the discovery and manufacture of new materials. Many component families exist, from polymers to colloids andliquid crystals, each with specific building blocks to tune and with different potential assemblies to reach. The work presented on this thesis hinges upon the hypothesis that it is possible to create connections between the mechanisms of these self-assembly processes. We focus on two systems, block-copolymers that undergo multiscale self-assembly that on macroscales may be harnessed to create filtration membranes and colloidal systems with specific interactions that lead to a wide range of mesophases. The first class of materials are polymeric but undergo an assembly process in which they first form micelles that then organize into larger structures. By treating these micelles as individual colloidal units, we develop a well-calibrated computational model of the self-assembly process that can provide insight into the mechanisms that control the structure of the membranes. The second class of materials are colloids that interact through local non-additivity to reach self-assembled structures traditionally observed in block copolymer systems. We develop an experimentally relevant design that allows for the manufacture of this particle family. To do this, we take advantage of DNA functionalized nanoparticles as a platform for interaction control, with the key insight that multiple interaction layers can be manipulated to induce non-additivity. On the basis of a machine learning analysis, we identify specific designs for these particles that achieve a desired assembly. Results generated from our studies of the two systems lead to connections between the theory of selfassembly of block copolymers and colloids. On one side, we have a polymer building block that can be modeled as a colloid, and on the other we have a colloidal building block that with appropriate interactions can achieve block copolymer like assembly. Extending the study of these systems, and finding further connections between them, can lead to generalized rules for self-assembly in other material families.

Description
130 pages
Date Issued
2025-05
Keywords
Machine Learning
•
Materials Design
•
Molecular Dynamics
•
Self-Assembly
Committee Chair
Escobedo, Fernando
Committee Member
Wiesner, Ulrich
Archer, Lynden
Degree Discipline
Chemical Engineering
Degree Name
Ph. D., Chemical Engineering
Degree Level
Doctor of Philosophy
Rights
Attribution-NonCommercial-ShareAlike 4.0 International
Rights URI
https://creativecommons.org/licenses/by-nc-sa/4.0/
Type
dissertation or thesis
Link(s) to Catalog Record
https://newcatalog.library.cornell.edu/catalog/16938195

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