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Massively parallel approximate Bayesian computation for estimating nanoparticle diffusion coefficients, sizes and concentrations using confocal laser scanning microscopy
RISE - Research Institutes of Sweden (2017-2019), Bioscience and Materials, Agrifood and Bioscience.ORCID iD: 0000-0002-5956-9934
Chalmers University of Technology, Sweden.
2018 (English)In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 271, no 2, p. 174-182Article in journal (Refereed) Published
Abstract [en]

We implement a massively parallel population Monte Carlo approximate Bayesian computation (PMC-ABC) method for estimating diffusion coefficients, sizes and concentrations of diffusing nanoparticles in liquid suspension using confocal laser scanning microscopy and particle tracking. The method is based on the joint probability distribution of diffusion coefficients and the time spent by a particle inside a detection region where particles are tracked. We present freely available central processing unit (CPU) and graphics processing unit (GPU) versions of the analysis software, and we apply the method to characterize mono- and bidisperse samples of fluorescent polystyrene beads.

Place, publisher, year, edition, pages
2018. Vol. 271, no 2, p. 174-182
Keywords [en]
Concentration, Confocal laser scanning microscopy, Diffusion coefficient, Nanoparticles, Particle tracking
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33847DOI: 10.1111/jmi.12706PubMedID: 29676793Scopus ID: 2-s2.0-85045743188OAI: oai:DiVA.org:ri-33847DiVA, id: diva2:1204625
Available from: 2018-05-08 Created: 2018-05-08 Last updated: 2023-05-25Bibliographically approved

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Röding, Magnus

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CiteExportLink to record
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Citation style
  • apa
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  • Other style
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Language
  • de-DE
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  • nn-NO
  • nn-NB
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Output format
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