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Abstract Spherical crystals are ubiquitous in nature and the necessary breaks in translational symmetry not seen in flat crystals render them structurally unique. Polymer crystals have been shown to exhibit nonflat morphologies, but control over their formation is difficult to achieve. One strategy is directing the crystallization by spatially and/or temporally tuning chain segmental mobility. This has been studied early on using polymer blends or polymer/solvent systems where coupling liquid–liquid phase separation with crystallization could provide morphological control. In this Trend article, a recent trend in using miniemulsion systems to act as nanoscale confinement on chain segmental mobility is reviewed. The confinement at this length scale causes unique features to arise in ordering processes such as liquid–liquid phase separation and crystallization that are not observed at the macroscale. The generality of this approach makes it a good candidate to direct the formation of new and unique hierarchical polymer nanostructures that could be utilized in numerous applications.
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Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning
Abstract The process of crystallization is often understood in terms of the fundamental microstructural elements of the crystallite being formed, such as surface orientation or the presence of defects. Considerably less is known about the role of the liquid structure on the kinetics of crystal growth. Here atomistic simulations and machine learning methods are employed together to demonstrate that the liquid adjacent to solid-liquid interfaces presents significant structural ordering, which effectively reduces the mobility of atoms and slows down the crystallization kinetics. Through detailed studies of silicon and copper we discover that the extent to which liquid mobility is affected by interface-induced ordering (IIO) varies greatly with the degree of ordering and nature of the adjacent interface. Physical mechanisms behind the IIO anisotropy are explained and it is demonstrated that incorporation of this effect on a physically-motivated crystal growth model enables the quantitative prediction of the growth rate temperature dependence.
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- Award ID(s):
- 1922312
- PAR ID:
- 10165155
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Communications
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2041-1723
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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