The integration of layer-by-layer (LbL) and self-assembly methods has the potential to achieve precision assembly of nanocomposite materials. Knowledge of how nanoparticles move across and within stacked materials is critical for directing nanoparticle assembly. Here, we investigate nanoparticle self-assembly within two different LbL architectures: (1) a bilayer composed of two immiscible polymer thin-films, and (2) a bilayer composed of polymer and graphene that possesses a “hard-soft” interface. Polymer-grafted silver nanocubes (AgNCs) are employed as a model nanoparticle system for systematic experiments – characterizing both assembly rate and resulting morphologies – that examine how assembly is affected by the presence of an interface. We observe that polymer grafts can serve to anchor AgNCs at the bilayer interface and to decrease particle mobility, or can promote particle transfer between layers. We also find that polymer viscosity and polymer mixing parameters can be used as predictors of assembly rate and behavior. These results provide a pathway for designing more complex multilayered nanocomposites.
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Many-body potential for simulating the self-assembly of polymer-grafted nanoparticles in a polymer matrix
Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior.
more » « less- Award ID(s):
- 2011924
- PAR ID:
- 10506053
- Publisher / Repository:
- NPJ
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41524-023-01166-6
