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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles
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High-Resolution Nanoparticle Sizing with Maximum A Posteriori Nanoparticle Tracking Analysis.

Kevin S Silmore1, Xun Gong1, Michael S Strano1

  • 1Department of Chemical Engineering , Massachusetts Institute of Technology , Cambridge , Massachusetts 02139 , United States.

ACS Nano
|March 12, 2019
PubMed
Summary
This summary is machine-generated.

A new Bayesian method, maximum a posteriori nanoparticle tracking analysis (MApNTA), accurately characterizes nanoparticle size distributions. This advanced technique improves upon existing methods for nanoparticle analysis in nanotechnology and biopharmaceuticals.

Keywords:
Bayesian inferenceBrownian dynamicscross validationparticle sizingpolydispersitysingle-particle tracking

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Area of Science:

  • Nanotechnology
  • Biopharmaceuticals
  • Materials Science

Background:

  • Characterizing polydisperse nanoparticle dispersions is challenging for current methods like dynamic light scattering.
  • Existing techniques often face issues with statistical biases, complex sample preparation, and data analysis.

Purpose of the Study:

  • To introduce a novel Bayesian approach for accurate nanoparticle size distribution estimation.
  • To provide an alternative to traditional methods with improved resolution and efficiency.

Main Methods:

  • Developed maximum a posteriori nanoparticle tracking analysis (MApNTA), a Bayesian method.
  • Utilized high-throughput single-particle tracking experiments.
  • Derived unbiased statistical models for mean square displacement and trajectory length.

Main Results:

  • MApNTA accurately infers nanoparticle size distributions with high resolution.
  • Demonstrated effectiveness with gold nanoparticles and single-walled carbon nanotubes.
  • Successfully identified minority components and impurities in nanoparticle samples.

Conclusions:

  • MApNTA offers a powerful tool for routine characterization of complex nanoparticle dispersions.
  • This method can significantly advance nanoparticle synthesis, separation, and functionalization.
  • The technique shows particular utility in detecting minority populations and impurities.