A New Bayesian Method for High Resolution Nanoparticle Sizing in Single Particle Tracking


This software aids in determining nanoparticle size and has applications as a research tool and in pharmaceutical quality assurance.

Problem Addressed

Measuring suspensions of particulate matter is notoriously difficult, and accurate measurement is required for protein-based pharmaceutical quality control and nanoparticle research and development. For example, measuring protein pharmaceuticals for the presence of agglomeration is a critical quality control step. Additionally, characterizing nanoparticle size is a critical step in engineering new nanoparticle technologies. Dynamic light scattering is one technique currently used to measure size distributions; however, it relies on generalized assumptions and fails to measure polydisperse solutions accurately. Individual particle tracking via scattered laser light in a dark-field microscope is more accurate, but is limited by the current statistical methods used to analyze the data. These inventors have created a software to accurately track particle size distribution.


This technology uses a Bayesian algorithm to analyze data from individual particle tracking. This algorithm can accurately pick out peaks of a polydisperse solution with a resolution of 5nm and can additionally determine the relative proportions of each population. This algorithm can also be used to measure the size of single walled carbon nanotube suspensions and eliminates the length biases common in current methods for measuring carbon nanotubes.


  • Accurate characterization of polydisperse solutions
  • Calculates size distribution of nanoparticles, protein aggregates, or carbon nanotubes
  • Size resolution within 5nm
  • Accurate measurement of carbon nanotubes by eliminating length bias