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Recognition-mediated assembly of random nanoscale copolymers into ordered microscale aggregates
Abstract
The non-covalent interactions that embody molecular self-assembly have developed, and continue to evolve, into a valuable instrument in the design, fabrication, and control of novel polymeric materials. To this end, we have developed a modular, ‘Plug and Play’ approach to reversible polymer side chain modification and have demonstrated this methodology to be useful for numerous applications such as solution-state control over polymer nanostructure, guest sensing, reversible surface modification, and what will be discussed throughout this thesis in particular, the formation of complex functional microstructures. ^ Polystyrene and polynorbornene copolymers randomly dispersed with complementary thymine and diamidopyridine functionalities spontaneously assemble, in non-competitive media, into giant vesicles, or Recognition-Induced Polymersomes (RIPs) due to specific interstrand three-point hydrogen bonding. The formation of vesicular architectures from random copolymers lacking well-defined headgroups is unprecedented, providing a new method for the creation of supramolecular systems. This mode of assembly affords a new tool for the control of vesicle structure; complementary monovalent and multivalent guests distort or disrupt vesicle structure through competitive binding to the polymer recognition sites. ^ An alternative motif to supramolecular engineering has led to the concept of noncovalent polymer crosslinking. Bis-thymine molecules were used to non-covalently crosslink a complementary diamidopyridine-functionalized copolymer. Upon combination in non-competitive solvents, discrete micron-scale spherical aggregates were formed, the size of which is controlled by crosslinker preorganization. ^
Subject Area
Chemistry, Organic|Chemistry, Polymer
Recommended Citation
Raymond J. Thibault,
"Recognition-mediated assembly of random nanoscale copolymers into ordered microscale aggregates"
(January 1, 2004).
Electronic Doctoral Dissertations for UMass Amherst.
Paper AAI3152752.
http://scholarworks.umass.edu/dissertations/AAI3152752