Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Diffusion in the Membrane

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...

You might also read

Related Articles

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

Sort by

Same author

Retraction notice to "The emergence of metal oxide nanoparticles (NPs) as a phytomedicine: A two-facet role in plant growth, nano-toxicity and anti-phyto-microbial activity" [Biomedicine & Pharmacotherapy 155 (2022) 113658].

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2026

Same author

Valorisation of rice straw for lipase immobilisation: toward sustainable treatment of lipid-rich wastewater.

Environmental technology·2026

Same author

External Magnetic Field Suppression of Carbon Diffusion in Iron.

Physical review letters·2026

Same author

Numerical calculation finds the revised elastic field of an edge dislocation to be incorrect.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

A D-optimal RSM-guided computational strategy for lipase immobilization on rice straw toward enhanced biocatalytic efficiency.

Preparative biochemistry & biotechnology·2025

Same author

siRNA as a criterion in host immunity and cancer immunotherapy: modulating factors and nano-conjugate based approach for intervention.

International journal of biological sciences·2025

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

Search research articles

Related Experiment Video

Updated: Jun 26, 2025

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

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

Published on: September 5, 2019

Contributions to Diffusion in Complex Materials Quantified with Machine Learning.

Soham Chattopadhyay¹, Dallas R Trinkle¹

¹Department of Materials Science and Engineering, University of Illinois, Urbana-Champaign, Illinois 61801, USA.

Physical Review Letters

|May 17, 2024

Summary

This summary is machine-generated.

We developed a machine learning method to model diffusion in alloys by calculating "kinosons," individual diffusion contributions. This approach is highly efficient and provides new analytic models for macroscale diffusivity.

More Related Videos

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

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

Published on: September 2, 2016

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

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: Jun 26, 2025

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

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

Published on: September 5, 2019

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

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

Published on: September 2, 2016

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

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:

Materials Science
Computational Materials Science
Machine Learning Applications

Background:

Modeling diffusion in complex multicomponent alloys is computationally intensive.
Traditional methods often require calculating entire atomic trajectories, limiting efficiency.

Purpose of the Study:

To develop a more efficient method for modeling diffusion in complex alloys.
To understand the fundamental kinetic mechanisms governing diffusion.
To create accurate analytic models for macroscale diffusivity.

Main Methods:

Utilized machine learning with a variational formula for diffusivity.
Recast diffusion as a sum of individual contributions, termed "kinosons".
Computed the statistical distribution of kinosons to model alloy behavior.

Main Results:

Calculating kinosons is orders of magnitude more efficient than computing whole trajectories.
The method elucidates kinetic mechanisms for diffusion.
The density of kinosons with temperature yields new accurate analytic models for macroscale diffusivity.

Conclusions:

The combination of machine learning and diffusion theory offers significant advancements in materials modeling.
This approach promises new insights into the behavior of other complex materials.
Kinason analysis provides a powerful tool for understanding and predicting material properties.