Predicting Chiral Nanostructures, Lattices and Superlattices in Complex Multicomponent Nanoparticle Self-Assembly
KAUST Grant NumberKUS-C1-018-02
Permanent link to this recordhttp://hdl.handle.net/10754/599381
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Abstract"Bottom up" type nanoparticle (NP) self-assembly is expected to provide facile routes to nanostructured materials for various, for example, energy related, applications. Despite progress in simulations and theories, structure prediction of self-assembled materials beyond simple model systems remains challenging. Here we utilize a field theory approach for predicting nanostructure of complex and multicomponent hybrid systems with multiple types of short- and long-range interactions. We propose design criteria for controlling a range of NP based nanomaterial structures. In good agreement with recent experiments, the theory predicts that ABC triblock terpolymer directed assemblies with ligand-stabilized NPs can lead to chiral NP network structures. Furthermore, we predict that long-range Coulomb interactions between NPs leading to simple NP lattices, when applied to NP/block copolymer (BCP) assemblies, induce NP superlattice formation within the phase separated BCP nanostructure, a strategy not yet realized experimentally. We expect such superlattices to be of increasing interest to communities involved in research on, for example, energy generation and storage, metamaterials, as well as microelectronics and information storage. © 2012 American Chemical Society.
CitationHur K, Hennig RG, Escobedo FA, Wiesner U (2012) Predicting Chiral Nanostructures, Lattices and Superlattices in Complex Multicomponent Nanoparticle Self-Assembly. Nano Lett 12: 3218–3223. Available: http://dx.doi.org/10.1021/nl301209c.
SponsorsThis publication is based on work supported in part by Award No. KUS-C1-018-02, made by King Abdullah University of Science and Technology (KAUST). This work was further supported by a single investigator award of the National Science Foundation (DMR-1104773). The calculations were performed using computational resources of the Computational Center for Nanotechnology Innovation (CCNI) at Rensselaer Polytechnic Institute.
PublisherAmerican Chemical Society (ACS)
CollectionsPublications Acknowledging KAUST Support
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