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Related Concept Videos

Molecular Shapes01:18

Molecular Shapes

Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.Two regions of electron density in a diatomic...

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Related Experiment Video

Updated: Jun 13, 2026

Modulating Shape of Polyester Based Polymersomes using Osmotic Pressure
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Modulating Shape of Polyester Based Polymersomes using Osmotic Pressure

Published on: April 21, 2021

Nanoparticle shape evolution identified through multivariate statistics.

David Huitink1, Subrata Kundu, Chiwoo Park

  • 1Department of Mechanical Engineering, Texas A&M University, College Station, Texas 77843, USA.

The Journal of Physical Chemistry. A
|April 16, 2010
PubMed
Summary
This summary is machine-generated.

Researchers developed a new statistical method to observe nanoparticle growth in real-time. This technique reveals how crystal surfaces and surfactants influence gold nanoparticle morphology and kinetics.

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Last Updated: Jun 13, 2026

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Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles
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Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles

Published on: March 28, 2021

Area of Science:

  • Materials Science
  • Nanotechnology
  • Physical Chemistry

Background:

  • Precise control over nanoparticle morphology is crucial for applications.
  • Observing instantaneous growth steps of nanoparticles (NPs) in situ has been a significant challenge.
  • Understanding nanoparticle nucleation and growth kinetics remains incomplete.

Purpose of the Study:

  • To develop and demonstrate a novel image detection statistical approach for morphological characterization of gold nanoparticles (Au NPs).
  • To enable the observation of instantaneous growth steps and kinetics of NPs.
  • To relate thermodynamic analysis of growth stages to surface energies and growth kinetics.

Main Methods:

  • Utilized a multivariate statistical technique for image detection and nanoparticle recognition.
  • Applied thermodynamic analysis to correlate surface energies with observed growth kinetics.
  • Investigated the influence of surfactant adsorption and local surface energies on growth steps.

Main Results:

  • Successfully identified nanoparticle morphology and growth stages using the novel statistical approach.
  • Demonstrated preferred growth of NPs on specific crystallographic surfaces.
  • Found that surfactant adsorption and local surface energies dominate NP growth steps.
  • Enabled detailed observation of NP growth kinetics.

Conclusions:

  • The developed statistical approach provides in situ observation of NP growth kinetics.
  • This method enhances the understanding of factors governing NP morphology.
  • The technique is applicable to other metallic nanoparticles for morphological control.