Supported lipid bilayers are often used as model systems for studying interactions of biological membranes with protein or nanoparticles. A supported lipid bilayer is a phospholipid bilayer built on a solid substrate. The latter is typically made of silica or a metal oxide due to the ease of its formation and range of compatible measurement techniques. Recently, a solvent-assisted method involving supported lipid bilayer formation has allowed the extension of compatible substrate materials to include noble metals such as gold. Here, we examine the influence of substrate composition (SiO2 vs Au) on the interactions between anionic ligand-coated Au nanoparticles or cytochrome c and zwitterionic supported lipid bilayers using quartz crystal microbalance with dissipation monitoring. We find that anionic nanoparticles and cytochrome c have higher adsorption to bilayers formed on Au relative to those on SiO2 substrates. We examine the substrate-dependence of nanoparticle adsorption with DLVO theory and all-atom simulations, and find that the stronger attractive van der Waals and weaker repulsive electrostatic forces between anionic nanoparticles and Au substrates vs anionic nanoparticles and SiO2 substrates could be responsible for the change in adsorption observed. Our results also indicate that the underlying substrate material influences the degree to which nanoscale analytes interact with supported lipid bilayers; therefore, interpretation of the supported lipid bilayer model system should be conducted with understanding of support properties.
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Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis
Metal nanoparticles have received substantial attention in the past decades for their applications in numerous areas, including medicine, catalysis, energy, and the environment. Despite these applications, the fundamentals of adsorption on nanoparticle surfaces as a function of nanoparticle size, shape, metal composition, and type of adsorbate are yet to be found. Herein, we introduce the first universal adsorption model that accounts for detailed nanoparticle structural characteristics, metal composition, and different adsorbates by combining first principles calculations with machine learning. Our model fits a large number of data and accurately predicts adsorption trends on nanoparticles (both monometallic and alloy) that have not been previously seen. In addition to its application power, the model is simple and uses tabulated and rapidly calculated data for metals and adsorbates. We connect adsorption with stability behavior of nanoparticles, thus advancing the design of optimal nanoparticles for applications of interest, such as catalysis.
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- Award ID(s):
- 1634880
- NSF-PAR ID:
- 10114082
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 5
- Issue:
- 9
- ISSN:
- 2375-2548
- Page Range / eLocation ID:
- eaax5101
- 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/10.1126/sciadv.aax5101
