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

Correlation of Experimental Data01:23

Correlation of Experimental Data

274
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
274
Distribution of Molecular Speeds01:27

Distribution of Molecular Speeds

4.2K
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...
4.2K
Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy03:07

Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy

28.1K
The kinetic molecular theory qualitatively explains the behaviors described by the various gas laws. The postulates of this theory may be applied in a more quantitative fashion to derive these individual laws.
28.1K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.8K
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
1.8K
Kinetic Energy - II00:56

Kinetic Energy - II

6.2K
The kinetic energy of a particle is one-half of the product of the particle’s mass and the square of its speed. Note that just as Newton’s second law can be expressed as either the rate of change of momentum or mass multiplied by the rate of change of velocity, so too can the kinetic energy of a particle be expressed in terms of its mass and momentum, instead of its mass and velocity. 
6.2K
Molecular Kinetic Energy01:21

Molecular Kinetic Energy

5.2K
The word "gas" comes from the Flemish word meaning "chaos," first used to describe vapors by the chemist J. B. van Helmont. Consider a container filled with gas, with a continuous and random motion of molecules. During collisions, the velocity component parallel to the wall is unchanged, and the component perpendicular to the wall reverses direction but does not change in magnitude. If the molecule’s velocity changes in the x-direction, then its momentum is changed.
5.2K

You might also read

Related Articles

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

Sort by
Same author

Dual-Zero-Scattering in Diffusive Transport.

Physical review letters·2026
Same author

Diffusion crossover of protein molecules: Two-step coarse-graining and oscillating memory.

The Journal of chemical physics·2026
Same author

Active phase separation triggered by chemotactic defects.

The Journal of chemical physics·2026
Same author

Roughness-induced diffusion enhancement in asymmetric potentials under nonequilibrium fluctuations.

Physical review. E·2026
Same author

Lévy Diffusion Under Power-Law Stochastic Resetting.

Entropy (Basel, Switzerland)·2026
Same author

Training strategies for competing multiagent dynamical systems.

Physical review. E·2026
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Sep 18, 2025

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging
06:34

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

Published on: September 2, 2016

6.5K

Kinetic Energy Diffusivity and Scaling Velocity Correlation Functions.

Jing-Dong Bao1, Fabio Marchesoni2,3

  • 1Beijing Normal University, School of Physics and Astronomy, Beijing 100875, China.

Physical Review Letters
|June 23, 2025
PubMed
Summary
This summary is machine-generated.

We introduce a new method to study how systems become ergodic, linking kinetic energy diffusivity to anomalous diffusion. This approach reveals a universal law for systems transitioning into an ergodic phase.

More Related Videos

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy
09:16

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Published on: January 9, 2017

14.5K
The Diffusion of Passive Tracers in Laminar Shear Flow
08:01

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

8.7K

Related Experiment Videos

Last Updated: Sep 18, 2025

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging
06:34

In Situ Monitoring of Diffusion of Guest Molecules in Porous Media Using Electron Paramagnetic Resonance Imaging

Published on: September 2, 2016

6.5K
Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy
09:16

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Published on: January 9, 2017

14.5K
The Diffusion of Passive Tracers in Laminar Shear Flow
08:01

The Diffusion of Passive Tracers in Laminar Shear Flow

Published on: May 1, 2018

8.7K

Area of Science:

  • Statistical Mechanics
  • Physical Chemistry
  • Complex Systems Dynamics

Background:

  • Ergodicity and anomalous diffusion are key concepts in understanding complex systems.
  • Existing methods struggle to quantify the transition between ergodic and non-ergodic behaviors.

Purpose of the Study:

  • To develop a Green-Kubo-like relation for kinetic energy diffusivity.
  • To investigate the interplay between ergodicity and anomalous diffusion.
  • To establish a framework for analyzing systems transitioning into an ergodic phase.

Main Methods:

  • Introduced a fluctuation metric for time-averaged kinetic energy.
  • Analyzed scaling velocity correlation functions.
  • Applied the method to protein folding and single-particle tracking data.

Main Results:

  • Demonstrated convergence to a universal law as systems become ergodic.
  • Showcased the method's effectiveness even with weak ergodicity breaking or bounded processes.
  • Explored non-ergodic transitions in granular gases via aging velocity correlations.

Conclusions:

  • The proposed kinetic energy diffusivity relation offers a robust framework for understanding system dynamics.
  • This method facilitates extraction of physical parameters like friction and relaxation time from experimental data.
  • Provides insights into ergodicity transitions in diverse physical systems.