Research Data Leeds Repository

Citation

Goodchild, James and Walsh, Danielle and Connell, Simon D. (2019) Dataset associated with “Nanoscale Substrate Roughness Hinders Domain Formation in Supported Lipid Bilayers”. University of Leeds. [Dataset] https://doi.org/10.5518/721

Dataset description

Supported Lipid Bilayers (SLBs) are model membranes formed at solid substrate surfaces. This architecture renders the membrane experimentally accessible to surface sensitive techniques used to study their properties, including Atomic Force Microscopy (AFM), optical fluorescence microscopy, Quartz Crystal Microbalance (QCM) and X-Ray/Neutron Reflectometry, and allows integration with technology for potential biotechnological applications such as drug screening devices. The experimental technique often dictates substrate choice or treatment, and it is anecdotally recognised that certain substrates are suitable for the particular experiment, but the exact influence of the substrate has not been comprehensively investigated. Here, we study the behavior of a simple model bilayer, phase separating on a variety of commonly used substrates, including glass, mica, silicon and quartz, with drastically different results. The distinct micron scale domains observed on mica, identical to those seen in free-floating Giant Unilamellar Vesicles (GUVs), are reduced to nanometer scale domains on glass and quartz. The mechanism for the arrest of domain formation is investigated, and the most likely candidate is nanoscale surface roughness, acting as a drag on the hydrodynamic motion of small domains during phase separation. Evidence was found that the physico-chemical properties of the surface have a mediating effect, most likely due to changes in the lubricating interstitial water layer between surface and bilayer.

Subjects:	C000 - Biological sciences > C700 - Molecular biology, biophysics & biochemistry > C770 - Biophysical science
Divisions:	Faculty of Engineering and Physical Sciences > School of Physics and Astronomy
Related resources:	Location Type https://doi.org/10.1021/acs.langmuir.9b01990 Publication https://eprints.whiterose.ac.uk/156787/ Publication
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	18 Dec 2020 12:07
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/796
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Goodchild, James https://orcid.org/0000-0001-6950-7676 University of Leeds https://ror.org/024mrxd33 ror Walsh, Danielle University of Leeds https://ror.org/024mrxd33 ror Connell, Simon D. https://orcid.org/0000-0003-2500-5724 University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: EPSRC Grant numbers: EP/J017566/1 Publication date: 2019 Resource language: English Metadata language: English Publisher: University of Leeds Contact email: s.d.a.connell@leeds.ac.uk Last Modified: 30 Oct 2024 13:03

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