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

Characteristics of Fluids01:20

Characteristics of Fluids

4.0K
When a force is applied parallel to the top surface of a solid, it resists the applied force due to the internal frictional forces between the layers of the solid known as shearing resistance. However, when the force is removed, the shearing forces restore the original shape of the solid. Other deformation forces also cause temporary changes in shape if the forces are not beyond a threshold magnitude. Solids tend to retain their shape, making the study of their rest and motion easier. Beyond...
4.0K
Laminar and Turbulent Flow01:07

Laminar and Turbulent Flow

8.6K
Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the...
8.6K
Turbulent Flow01:24

Turbulent Flow

220
Turbulent flow is characterized by unpredictable fluctuations in velocity and pressure, which result in a chaotic fluid movement distinct from the orderly patterns of laminar flow. While laminar flow is governed by smooth, parallel layers with minimal mixing, turbulent flow exhibits highly irregular, three-dimensional patterns. This behavior arises due to instabilities in the fluid's velocity profile, and amplifies as the flow velocity increases. Minor disturbances, known as turbulent...
220
Types of Fluids01:27

Types of Fluids

326
Fluids can be classified into Newtonian and non-Newtonian fluids based on their response to shear stress. Newtonian fluids have a linear relationship between shear stress and the shear strain rate, following Newton's law of viscosity. Their viscosity remains constant regardless of the shear rate, making their behavior predictable and easier to analyze. Common examples include water, air, oil, and gasoline.
In contrast, non-Newtonian fluids do not follow Newton's law of viscosity, and...
326
Surface Tension of Fluid01:22

Surface Tension of Fluid

338
Surface tension is a fundamental property of fluids, occurring at the boundary between a liquid and a gas or between two immiscible liquids. This phenomenon arises from the cohesive forces between molecules at the fluid's surface, creating an effect similar to a stretched elastic membrane. Inside each fluid, molecules are equally attracted in all directions by neighboring molecules, but surface molecules experience a net inward force, resulting in surface tension.
Surface tension varies...
338
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

265
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
265

You might also read

Related Articles

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

Sort by
Same author

Molecular Simulations of Phase Separation in Elastic Polymer Networks.

The journal of physical chemistry. B·2026
Same author

Dynamics of phase separation in non-local elastic networks.

Soft matter·2026
Same author

Systematic Parameterization of Flory-Huggins Models from Molecular Dynamics Simulations for Ternary Lipid Mixtures.

Journal of chemical theory and computation·2025
Same author

Physics of droplet regulation in biological cells.

Reports on progress in physics. Physical Society (Great Britain)·2025
Same author

Theory of Condensate Size Control by Molecular Charge Asymmetry.

ACS macro letters·2025
Same author

Crossover patterning through condensation and coarsening of pro-crossover factors.

Nature cell biology·2025

Related Experiment Video

Updated: Jul 23, 2025

Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature
08:04

Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature

Published on: November 26, 2019

7.2K

Physical interactions in non-ideal fluids promote Turing patterns.

Lucas Menou1, Chengjie Luo1, David Zwicker1

  • 1Max Planck Institute for Dynamics and Self-Organization, Am Faßberg 17, Göttingen 37077, Germany.

Journal of the Royal Society, Interface
|July 12, 2023
PubMed
Summary
This summary is machine-generated.

Interactions between chemical activators and inhibitors significantly influence Turing patterns. Incorporating these interactions, even weak ones, is crucial for accurately modeling pattern formation in nature.

Keywords:
Turing patternsnon-ideal systemsphase separationreaction–diffusion systems

More Related Videos

Pool-Boiling Heat-Transfer Enhancement on Cylindrical Surfaces with Hybrid Wettable Patterns
07:32

Pool-Boiling Heat-Transfer Enhancement on Cylindrical Surfaces with Hybrid Wettable Patterns

Published on: April 10, 2017

9.0K
Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions
11:51

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Published on: February 22, 2018

8.8K

Related Experiment Videos

Last Updated: Jul 23, 2025

Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature
08:04

Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature

Published on: November 26, 2019

7.2K
Pool-Boiling Heat-Transfer Enhancement on Cylindrical Surfaces with Hybrid Wettable Patterns
07:32

Pool-Boiling Heat-Transfer Enhancement on Cylindrical Surfaces with Hybrid Wettable Patterns

Published on: April 10, 2017

9.0K
Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions
11:51

Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions

Published on: February 22, 2018

8.8K

Area of Science:

  • Chemical kinetics
  • Pattern formation
  • Theoretical chemistry

Background:

  • Turing's mechanism explains natural periodic patterns via reaction-diffusion systems.
  • Formation requires slow activator diffusion and nonlinear reactions, often from cooperativity.
  • Experimental evidence for Turing patterns remains limited.

Purpose of the Study:

  • To investigate the impact of direct physical interactions between chemical species on Turing pattern formation.
  • To explore how these interactions modify the conditions for pattern emergence and characteristics.
  • To bridge the gap between traditional Turing patterns and chemically active phase separation.

Main Methods:

  • Theoretical modeling of reaction-diffusion systems incorporating direct intermolecular interactions.
  • Analysis of how activator-inhibitor repulsion affects differential diffusivity and reaction nonlinearity.
  • Examination of pattern formation under strong interaction conditions, including phase separation.

Main Results:

  • Weak repulsion between activator and inhibitor substantially reduces required differential diffusivity and reaction nonlinearity.
  • Strong interactions can lead to phase separation, but pattern length scales remain governed by reaction-diffusion dynamics.
  • Direct interactions significantly alter Turing pattern characteristics, deviating from predictions based solely on reaction-diffusion.

Conclusions:

  • Direct physical interactions are critical and must be included in models of realistic reaction-diffusion systems.
  • The theory unifies Turing patterns with chemically active phase separation, broadening applicability.
  • Even minor interactions have a substantial effect, necessitating their consideration for accurate pattern prediction.