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Related Concept Videos

Carrier Transport01:21

Carrier Transport

The generation of electrical current in semiconductors is fundamentally driven by two mechanisms: drift and diffusion. These processes are essential for the functionality and performance of semiconductor-based devices.
Drift Current:
The drift of charge carriers is started by an external electric field (E). Charged particles, such as electrons and holes, experience an acceleration between collisions with lattice atoms. For electrons, this results in a drift velocity (vd) given by:
Diffusion01:21

Diffusion

Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
Diffusion01:12

Diffusion

Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
Reynolds Transport Theorem01:24

Reynolds Transport Theorem

The Reynolds transport theorem provides a framework to relate the time rate of change of an extensive property within a system to that in a control volume, which is crucial for analyzing fluid dynamics. Extensive properties, such as mass, velocity, acceleration, temperature, and momentum, can be expressed in terms of the mass of a fluid portion. These properties are called extensive because they depend on the system's size, while intensive properties are their corresponding values per unit mass.
Transport Number01:31

Transport Number

The transport number is the fraction of the total current carried by an ion in an electrolyte solution. It is defined as the ratio of the current carried by a specific ion to the total current flowing through the solution. The transport number, t, is central to understanding ionic mobility, which describes how fast an ion moves under the influence of an electric field. This link connects the physical behavior of ions in solution to the chemical processes that occur during electrochemical...
Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...

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Related Experiment Video

Updated: Jul 2, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Random time-scale invariant diffusion and transport coefficients.

Y He1, S Burov, R Metzler

  • 1Department of Physics, Bar Ilan University, Ramat-Gan, Israel.

Physical Review Letters
|September 4, 2008
PubMed
Summary
This summary is machine-generated.

Single particle tracking reveals subdiffusive motion in cells. This study investigates ergodicity breaking in particle dynamics and quantifies fluctuations, offering new insights for interpreting single-molecule tracking data.

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The Diffusion of Passive Tracers in Laminar Shear Flow
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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

Related Experiment Videos

Last Updated: Jul 2, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

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

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

Area of Science:

  • Cellular dynamics
  • Biophysics
  • Statistical mechanics

Background:

  • Single particle tracking (SPT) is crucial for observing molecular motion within living cells.
  • Subdiffusive motion and ergodicity breaking are common but not fully understood phenomena in intracellular transport.
  • Interpreting SPT data requires robust models that account for inherent randomness.

Purpose of the Study:

  • To investigate ergodicity breaking in subdiffusive particle motion within living cells.
  • To analyze the statistical properties of the time-averaged mean squared displacement (delta2[over ]).
  • To generalize the Einstein relation for systems exhibiting non-ergodic behavior.

Main Methods:

  • Utilizing the continuous time random walk (CTRW) model.
  • Analyzing single particle tracking data of mRNA molecules and lipid granules.
  • Deriving the probability distribution for fluctuations in delta2[over ].

Main Results:

  • Demonstrated that delta2[over ] is a random variable, confirming subdiffusive particle motion.
  • Showed that delta2[over ] differs from the ensemble average, indicating ergodicity breaking.
  • Derived the distribution for delta2[over ] fluctuations and generalized the Einstein relation.

Conclusions:

  • The findings provide a deeper understanding of non-ergodic dynamics in biological systems.
  • The derived distributions offer a framework for more accurate analysis of SPT data.
  • This work has significant implications for interpreting molecular transport in cellular environments.