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Ligand-Mediated Nucleation and Growth of Palladium Metal Nanoparticles
Published on: June 25, 2018
Approximate Bayesian computation for estimating number concentrations of monodisperse nanoparticles in suspension by
Magnus Röding1, Elisa Zagato2, Katrien Remaut2
1SP Food and Bioscience, Soft Materials Science, Göteborg, Sweden and School of Energy and Resources, UCL Australia, University College London, Adelaide, Australia.
Abstract:
We present an approximate Bayesian computation scheme for estimating number concentrations of monodisperse diffusing nanoparticles in suspension by optical particle tracking microscopy. The method is based on the probability distribution of the time spent by a particle inside a detection region. We validate the method on suspensions of well-controlled reference particles. We illustrate its usefulness with an application in gene therapy, applying the method to estimate number concentrations of plasmid DNA molecules and the average number of DNA molecules complexed with liposomal drug delivery particles.

