Massively parallel approximate Bayesian computation for estimating nanoparticle diffusion coefficients, sizes and concentrations using confocal laser scanning microscopy
Artikel i vetenskaplig tidskrift, 2018

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.

Diffusion coefficient

Confocal laser scanning microscopy

Particle tracking

Concentration

Nanoparticles

Författare

Magnus Röding

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Markus Billeter

Chalmers, Rymd-, geo- och miljövetenskap, Astronomi och plasmafysik, Plasmafysik och fusionsenergi

Journal of Microscopy

0022-2720 (ISSN) 1365-2818 (eISSN)

Ämneskategorier

Datorteknik

Biomedicinsk laboratorievetenskap/teknologi

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling

Infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

DOI

10.1111/jmi.12706