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Single particle raster image analysis of diffusion for particle mixtures
Chalmers University of Technology, Sweden; University of Gothenburg, Sweden.
RISE - Research Institutes of Sweden (2017-2019), Biovetenskap och material, Jordbruk och livsmedel.ORCID-id: 0000-0002-5956-9934
RISE - Research Institutes of Sweden (2017-2019), Biovetenskap och material, Jordbruk och livsmedel.ORCID-id: 0009-0000-1671-4583
RISE - Research Institutes of Sweden (2017-2019), Biovetenskap och material, Jordbruk och livsmedel.
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2018 (Engelska)Ingår i: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 269, nr 3, s. 269-281Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
2018. Vol. 269, nr 3, s. 269-281
Nyckelord [en]
Bootstrap; Confocal laser scanning microscopy; Diffusion; Fluorescent beads; Maximum likelihood; Particle mixtures; Single particle tracking
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Naturvetenskap
Identifikatorer
URN: urn:nbn:se:ri:diva-33367DOI: 10.1111/jmi.12625Scopus ID: 2-s2.0-85041951653OAI: oai:DiVA.org:ri-33367DiVA, id: diva2:1187745
Tillgänglig från: 2018-03-05 Skapad: 2018-03-05 Senast uppdaterad: 2023-10-06Bibliografiskt granskad

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Röding, MagnusAltskär, AnnikaLoren, Niklas

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