Description
- Abstract:
- There is great interest in exploiting van der Waals gaps in layered materials as nanofluidic channels. Graphene oxide (GO) nanosheets are known to spontaneously assemble into stacked planar membranes with transport properties that are highly selective to molecular structure. Use of conventional GO membranes in liquid-phase applications is often limited by low flux values, due to intersheet nanochannel alignment perpendicular to the desired Z-directional transport, which leads to circuitous fluid pathways that are orders of magnitude longer than the membrane thickness. Here we demonstrate an approach that uses compressive instability in Zr-doped GO thin films to create wrinkle patterns that rotate nanosheets to high angles. Capturing this structure in polymer matrices and thin sectioning produces fully dense membranes with arrays of near-vertically aligned nanochannels. These robust nanofluidic devices offer pronounced reduction in fluid path-length, while retaining the high selectivity for water over non-polar molecules characteristic of GO interlayer nanochannels.
- Notes:
- This research is supported by the National Institute of Environmental Health Sciences (NIEHS) Superfund Research Program P42ES013660
Access Conditions
- Use and Reproduction
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- Attribution 4.0 International (CC BY 4.0)
Citation
Liu, Muchun, Weston, Paula J., and Hurt, Robert H.,
"Controlling Nanochannel Orientation and Dimensions in Graphene-Based Nanofluidic Membranes"
Brown University Open Data Collection, Brown Superfund Data Products.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/r4sv-a551
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Brown University Open Data Collection
This collection contains open and public access data sets created by Brown University faculty and student researchers. Increasingly, publishers and funders are requiring that protocols, data sets, documentation and code underlying publications be retained and preserved in repositories, their locations …... -
Brown Superfund Data Products
This collection contains data sets, code, and documentation files associated with the Brown University Superfund Research Program's investigators and research projects....