Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Single particle raster image analysis of diffusion for particle mixtures
Chalmers University of Technology, Sweden ; University of Gothenburg, Sweden.
RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.ORCID iD: 0000-0002-5956-9934
RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
RISE - Research Institutes of Sweden, Bioscience and Materials, Agrifood and Bioscience.
Show others and affiliations
2018 (English)In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 269, no 3, p. 269-281Article in journal (Refereed) Published
Abstract [en]

Recently we complemented the raster image correlation spectroscopy (RICS) method of analysing raster images via estimation of the image correlation function with the method single particle raster image analysis (SPRIA). In SPRIA, individual particles are identified and the diffusion coefficient of each particle is estimated by a maximum likelihood method. In this paper, we extend the SPRIA method to analyse mixtures of particles with a finite set of diffusion coefficients in a homogeneous medium. In examples with simulated and experimental data with two and three different diffusion coefficients, we show that SPRIA gives accurate estimates of the diffusion coefficients and their proportions. A simple technique for finding the number of different diffusion coefficients is also suggested. Further, we study the use of RICS for mixtures with two different diffusion coefficents and investigate, by plotting level curves of the correlation function, how large the quotient between diffusion coefficients needs to be in order to allow discrimination between models with one and two diffusion coefficients. We also describe a minor correction (compared to published papers) of the RICS autocorrelation function. Lay description Diffusion is a key mass transport mechanism for small particles. Efficient methods for estimating diffusion coefficients are crucial for analysis of microstructures, for example in soft biomaterials. The sample of interest may consist of a mixture of particles with different diffusion coefficients. Here, we extend a method called Single Particle Raster Image Analysis (SPRIA) to account for particle mixtures and estimation of the diffusion coefficients of the mixture components. SPRIA combines elements of classical single particle tracking methods with utilizing the raster scan with which images obtained by using a confocal laser scanning microscope. In particular, single particles are identified and their motion estimated by following their center of mass. Thus, an estimate of the diffusion coefficient will be obtained for each particle. Then, we analyse the distribution of the estimated diffusion coefficients of the population of particles, which allows us to extract information about the diffusion coefficients of the underlying components in the mixture. On both simulated and experimental data with mixtures consisting of two and three components with different diffusion coefficients, SPRIA provides accurate estimates and, with a simple criterion, the correct number of mixture components is selected in most cases.

Place, publisher, year, edition, pages
2018. Vol. 269, no 3, p. 269-281
Keywords [en]
Bootstrap; Confocal laser scanning microscopy; Diffusion; Fluorescent beads; Maximum likelihood; Particle mixtures; Single particle tracking
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33367DOI: 10.1111/jmi.12625OAI: oai:DiVA.org:ri-33367DiVA, id: diva2:1187745
Available from: 2018-03-05 Created: 2018-03-05 Last updated: 2018-08-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Röding, MagnusLoren, Niklas

Search in DiVA

By author/editor
Röding, MagnusLoren, Niklas
By organisation
Agrifood and Bioscience
In the same journal
Journal of Microscopy
Natural Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 3 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.35.3