Improved nano-particle tracking analysis

Nano-particle tracking is a method to estimate a particle size distribution by tracking the movements of individual particles, using multiple images of particles moving under Brownian motion. A novel method to recover a particle size distribution from nano-particle tracking data is described. Unlike a simple histogram-based method, the method described is able to account for the finite number of steps in each particle track and consequently for the measurement uncertainty in the step-length data. Computer simulation and experimental results are presented to demonstrate the performance of the approach compared with the current method.

[1]  Dennis E. Koppel,et al.  Analysis of Macromolecular Polydispersity in Intensity Correlation Spectroscopy: The Method of Cumulants , 1972 .

[2]  Edward Roy Pike,et al.  Photon correlation and light beating spectroscopy , 1974 .

[3]  Mario Bertero,et al.  Linear inverse problems with discrete data. I. General formulation and singular system analysis , 1985 .

[4]  S. Provencher A constrained regularization method for inverting data represented by linear algebraic or integral equations , 1982 .

[5]  B. Dahneke Measurement of suspended particles by quasi-elastic light scattering , 1983 .

[6]  M. Epple,et al.  Possibilities and limitations of different analytical methods for the size determination of a bimodal dispersion of metallic nanoparticles , 2011 .

[7]  Jong-Won Yoon,et al.  Pulsed laser induced synthesis of scheelite-type colloidal nanoparticles in liquid and the size distribution by nanoparticle tracking analysis , 2007 .

[8]  Vasco Filipe,et al.  Critical Evaluation of Nanoparticle Tracking Analysis (NTA) by NanoSight for the Measurement of Nanoparticles and Protein Aggregates , 2010, Pharmaceutical Research.

[9]  T. J. Herbert Statistical stopping criteria for iterative maximum likelihood reconstruction of emission images , 1990 .

[10]  B. De Baets,et al.  Accurate particle size distribution determination by nanoparticle tracking analysis based on 2-D Brownian dynamics simulation. , 2010, Journal of colloid and interface science.

[11]  L. Shepp,et al.  Maximum Likelihood Reconstruction for Emission Tomography , 1983, IEEE Transactions on Medical Imaging.

[12]  B. Frisken,et al.  Revisiting the method of cumulants for the analysis of dynamic light-scattering data. , 2001, Applied optics.

[13]  Mario Bertero,et al.  On the recovery and resolution of exponential relaxation rates from experimental data: a singular-value analysis of the Laplace transform inversion in the presence of noise , 1982, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[14]  John G. Walker,et al.  Maximum likelihood data inversion for photon correlation spectroscopy , 2008 .

[15]  G. Gee,et al.  Particle-size Analysis , 2018, SSSA Book Series.

[16]  Henk G. Merkus,et al.  Particle Size Measurements , 2009 .

[17]  S. Provencher CONTIN: A general purpose constrained regularization program for inverting noisy linear algebraic and integral equations , 1984 .

[18]  L. Shepp,et al.  A Statistical Model for Positron Emission Tomography , 1985 .

[19]  E. Veklerov,et al.  Stopping Rule for the MLE Algorithm Based on Statistical Hypothesis Testing , 1987, IEEE Transactions on Medical Imaging.