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Quantitative Sizing of Nano/Microparticles with a Tunable Elastomeric Pore Sensor
Abstract
The use of a "size-tunable" polyurethane resistive pulse sensor for quantitative sizing of nano- and microparticles is presented. A linear relationship, as first suggested by Maxwell, between particle volume and change in electric resistance across the pore was observed. Particle sizes were quantified for a given size-tunable membrane, by first creating a linear calibration curve to a series of monodisperse carboxylated polystyrene particles of various diameters and then applying this curve to calculate the size of "unknown" nanoparticles. The diameters of a selection of synthetic and biological particles, being PMMA and nonfunctionalized polystyrene particles, along with biological nanoparticles (adenovirus) were calculated using this methodology. Calculated particle diameters and coefficients of variation were shown to be in good agreement with both transmission electron microscopy and dynamic light scattering results.
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Cited by
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Exosome Processing and Characterization Approaches for Research and Technology Development
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Anderson, W., Kozak, D., Coleman, V. A., Jämting, Å. K., & Trau, M. (2013) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2013.02.030 · Cited by 373
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Metadata
- Authors
- Vogel, R., Willmott, G., Kozak, D., Roberts, G. S., Anderson, W., Groenewegen, L., Glossop, B., Barnett, A., Turner, A., & Trau, M.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-04-11
- Volume
- 83
- Issue
- 9
- Pages
- 3499-3506
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W1982377931
- Cited by
- 287
- References
- 0
- FWCI
- 13.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- Vogel, R., Willmott, G., Kozak, D., Roberts, G. S., Anderson, W., Groenewegen, L., Glossop, B., Barnett, A., Turner, A., & Trau, M. (2011). Quantitative Sizing of Nano/Microparticles with a Tunable Elastomeric Pore Sensor. Analytical Chemistry. https://doi.org/10.1021/ac200195n
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
