Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion

24.5K
Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
24.5K
Diffusion on Chromatography Columns01:07

Diffusion on Chromatography Columns

1.6K
In column chromatography, when an analyte is introduced as a narrow band at the top of the column, the solutes begin to separate and broaden, developing a Gaussian profile. This broadening occurs due to various factors, such as longitudinal diffusion.
Longitudinal diffusion occurs when the solute molecules in the mobile phase diffuse from the more concentrated center of the chromatographic band to the more dilute regions on either side, both towards and against the flow direction. This...
1.6K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.3K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
2.3K
Distribution of Molecular Speeds01:27

Distribution of Molecular Speeds

4.0K
The motion of molecules in a gas is random in magnitude and direction for individual molecules, but a gas of many molecules has a predictable distribution of molecular speeds. This predictable distribution of molecular speeds is known as the Maxwell-Boltzmann distribution. The distribution of molecular speeds in liquids is comparable to that of gases but not identical and can help to understand the phenomenon of the boiling and vapor pressure of a liquid. Consider that a molecule requires a...
4.0K
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

502
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
502
The Kinetic Model of Gases01:24

The Kinetic Model of Gases

146
The kinetic model of gases explains the properties of a perfect gas using three main assumptions: molecules move in ceaseless random motion, their size is negligible compared to the distances between them, and they do not interact except during perfectly elastic collisions. The total energy of a gas is the sum of the kinetic energies of all its constituent molecules. The pressure exerted by the gas arises from the continual bombardment of the container walls by billions of colliding molecules.
146

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Active Brownian particles in quenched matrices.

The Journal of chemical physics·2026
Same author

Beyond the Paddle-Wheel Mechanism: Hop Function Analysis of Ion Transport in Organic Ionic Plastic Crystals.

Journal of the American Chemical Society·2026
Same author

Hopping dynamics of a tracer particle confined in a fluctuating lattice.

Soft matter·2026
Same author

Non-Gaussian rotational diffusion and swing motion of dumbbell probes in two-dimensional colloids.

The Journal of chemical physics·2025
Same author

Subcritical pitchfork bifurcation transition of a single nanoparticle in strong confinement.

Physical review. E·2025
Same author

Segregation of lipids to cellular poles.

The Journal of chemical physics·2025

Related Experiment Video

Updated: Apr 30, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

7.6K

Dynamics in crowded environments: is non-Gaussian Brownian diffusion normal?

Gyemin Kwon1, Bong June Sung, Arun Yethiraj

  • 1Department of Chemistry and Institute for Biological Interfaces, Sogang University , Seoul 121-742, Republic of Korea.

The Journal of Physical Chemistry. B
|May 1, 2014
PubMed
Summary

Colloidal particle dynamics in concentrated solutions exhibit non-Gaussian Brownian diffusion due to local heterogeneities. Hydrodynamic interactions slow dynamics but do not alter this non-Gaussian behavior.

More Related Videos

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.0K
Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy
09:09

Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy

Published on: March 5, 2021

3.9K

Related Experiment Videos

Last Updated: Apr 30, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

7.6K
Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.0K
Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy
09:09

Visualizing Diffusional Dynamics of Gold Nanorods on Cell Membrane using Single Nanoparticle Darkfield Microscopy

Published on: March 5, 2021

3.9K

Area of Science:

  • Colloid and protein biophysics
  • Soft matter physics
  • Statistical mechanics

Background:

  • Understanding particle dynamics in dense suspensions is crucial for biophysics and colloidal science.
  • Recent studies suggest non-Gaussian dynamics in systems like actin networks and protein mixtures.
  • Hydrodynamic interactions (HI) are known to affect protein dynamics in concentrated solutions.

Purpose of the Study:

  • To investigate the dynamics of a dilute tracer colloidal particle in a concentrated solution of larger spheres.
  • To determine if non-Gaussian diffusion is a general feature of colloidal dynamics.
  • To assess the role of hydrodynamic interactions (HI) on tracer particle dynamics.

Main Methods:

  • Simulations of a tracer colloidal particle in a concentrated solution.
  • Comparison of simulations with and without hydrodynamic interactions (HI).
  • Analysis of the self-part of the van Hove correlation function, Gs(r,t), and mean-square displacement.

Main Results:

  • Simulations reproduced non-Gaussian Brownian diffusion for the tracer particle.
  • Non-Gaussian behavior was observed both with and without hydrodynamic interactions.
  • Hydrodynamic interactions reduced the diffusion constant but did not change the qualitative nature of Gs(r,t).

Conclusions:

  • Non-Gaussian Brownian diffusion is a general characteristic of colloidal dynamics, arising from local heterogeneities.
  • Hydrodynamic interactions influence the rate of diffusion but not the fundamental non-Gaussian nature of particle motion.
  • These findings advance our understanding of complex fluid dynamics and biophysical systems.