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Massachusetts Institute of Technology

Dynamics of associative polymer networks

Abstract

dc:description.abstract

Associative polymer networks are a versatile class of materials as demonstrated by their use in a wide variety of applications including biomaterials, viscosity modifiers and underwater adhesives. The viscoelastic and transport properties displayed by these associative networks can be tuned by careful design of the polymers that make up the network, as well as the transient interactions between them. Thus, elucidating the relationship between the molecular level details and the observed macroscopic properties is of high importance to further advance our understanding of associative networks. However, the complex dynamics displayed by these materials over a wide range of length and time scales present a significant challenge in studying this relation. This thesis aims to provide insights into the molecular origin of the dynamics of associative polymer networks.

Degree

thesis:*
Name thesis:degree_name
Doctoral
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Mahmad Rasid, Irina.
Advisor dc:contributor.advisor
  • Niels Holten-Andersen and Bradley D. Olsen.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/130675
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/130675

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Mahmad Rasid, Irina.. Dynamics of associative polymer networks. Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130675