(611f) Nanoparticle Incorporation and Aggregation In Block Copolymer Worm-Like Micelles | AIChE

(611f) Nanoparticle Incorporation and Aggregation In Block Copolymer Worm-Like Micelles

Authors 

Christian, D. A. - Presenter, University of Pennsylvania
Rajagopal, K. - Presenter, University of Pennsylvania
Srinivas, G. - Presenter, University of Pennsylvania
Klein, M. L. - Presenter, University of Pennsylvania
Discher, D. E. - Presenter, University of Pennsylvania


Top-down lithographic approaches have significant limitations in either feature size (optical lithography) or cost (electron beam lithography), whereas self-assembling polymer systems can in principle provide robust scaffolds for positioning. Block copolymers are composed of chemically distinct polymer chains joined by a covalent bond at the interface, and the distinct chemistries not only drive self-assembly in the bulk phase to form periodic nanostructures over supramolecular length scales but they might also be used to precisely order nanoparticles embedded in the polymer. By changing the surface chemistry of the particles, for example, their location can be predictably changed to different domains in the bulk diblock copolymer assembly. Factors such as nanoparticle composition, volume fraction, and concentration play crucial roles in determining the final morphology and the properties of such nanoparticle-copolymer composite materials. How these nanoparticles interact and aggregate within these block copolymer assemblies is therefore important to understand.

Amphiphilic diblock copolymers are particularly well known to self assemble in dilute solutions, forming supramolecular aggregates with a hydrophobic core surrounded by a dense, hydrophilic brush. By changing the lengths of the different polymer chains, the morphology of these aggregates can be tuned to form worm-like micelles that are nanometers in diameter and microns in length. Recent advances in assembly of worm-like micelles have demonstrated their potential to serve as linear nano-templates for ordering nanoparticles over several microns. Here, we investigate ? by experiment and by coarse-grain simulation ? the nanoparticle-copolymer (N-C) composite self-assembly and aggregation.

Coarse grain modeling of the self-assembly of hydrophobic nanoparticles and diblock copolymers dispersed randomly in an aqueous solution shows that nanoparticle-copolymer interactions drive the self-assembly process to produce worm-like micelles faster than when the copolymer alone is in solution. Coarse grain modeling was also used to study the aggregation of nanoparticles integrated within the core of pre-assembled worm-like micelles. In the model, nanoparticles were placed 3 nm apart in the core of a worm-like micelle. The kinetics for nanoparticle aggregation was an order of magnitude slower compared to the case with a random initial configuration. No change in the distance between the nanoparticles was observed until the density of copolymer molecules around the nanoparticles changed. It can therefore be concluded that the local density of the copolymers in the core of the worm-like micelle controls the aggregation of nanoparticles.

Integration and aggregation of hydrophobic nanoparticles ? core-shell quantum dots (CSQD) ? in the cores of pre-assembled worm-like micelles was investigated experimentally. Worm-like micelles composed of the amphiphilic diblock poly(ethylene oxide)-polybutadiene (PEO-PBD) were assembled by the solvent evaporation in water. CSQDs dissolved in chloroform were then dispersed into the worm-like micelle solution and were observed by fluorescence microscopy to incorporate into the hydrophobic core of worm-like micelles. Worm-like micelles were homogeneously labeled with a fluorescent hydrophobic dye, and CSQDs could be imaged as punctates along the worm-like micelle contour length. Image analysis of CSQD fluorescence intensity indicated a distribution of CSQD aggregate number. Ongoing experiments aim to incorporate CSQDs more efficiently into the worm-like micelles and also to quantify the number of CSQDs per aggregate.