Data · dataset · 2016
The effect of shear flow on nanoparticle agglomeration and deposition in in vitro dynamic flow models
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Traditional in vitro toxicity experiments typically involve exposure of a mono- or co-culture of cells to nanoparticles (NPs) in static conditions with the assumption of 100% deposition (i.e. dose) of well-dispersed particles.
Description
However, cellular dose can be affected by agglomeration and the unique transport kinetics of NPs in biological media. We hypothesize that shear flow can address these issues and achieve more predictable dosage.
Here, we compare the behavior of gold NPs with diameters of 5, 10 and 30 nm in static and dynamic in vitro models. We also utilize transport modeling to approximate the shear rate experienced by the cells in dynamic conditions to evaluate physiological relevance. The transport kinetics show that NP behavior is governed by both gravity and diffusion forces in static conditions and only diffusion in dynamic conditions.
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Our results reveal that dynamic systems are capable of producing a more predictable dose compared to static systems, which has strong implications for improving repeatability in nanotoxicity assessments.
Links
Where it is published
- Repository landing page tandf.figshare.com/articles/dataset/The_effect_of_shear_flow_on_nanoparticle_aggl… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.2796916 ↗
DOI / persistent id · from DataCite
Documentation and papers
- Creative Commons Attribution 4.0 International creativecommons.org/licenses/by/4.0/legalcode ↗
license · from DataCite
- IsSupplementTo 10.3109/17435390.2015.1018978 doi.org/10.3109/17435390.2015.1018978 ↗
publication · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.2796916 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.2796916 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Biological sciences · Physical sciences
- From keywords
- Cancer · Medicine & Health
- Inferred from text
- Medical biotechnology 72%
Related
Provenance · 1 source records, 13 field assertions
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|---|---|---|---|
| DataCite | 10.6084/m9.figshare.2796916 | 15 d ago | JSON v1 |
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| concepts[field].local:field:medicine-health | mapping · DataCite | vocabulary-mapper@1.0.0 | keywords['Medicine'] |
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