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

What is a Mode?01:07

What is a Mode?

22.6K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
22.6K
Modes of Standing Waves - I01:03

Modes of Standing Waves - I

3.3K
A close look at earthquakes provides evidence for the conditions appropriate for resonance, standing waves, and constructive and destructive interference. A building may vibrate for several seconds with a driving frequency matching the building's natural frequency of vibration; this produces a resonance that results in one building collapsing while the neighboring buildings do not. Often, buildings of a certain height are devastated, while other taller buildings remain intact. This...
3.3K
Modes of Standing Waves: II01:04

Modes of Standing Waves: II

1.1K
The starting point for expressing the modes of standing waves is understanding the boundary conditions that the waves must follow. The boundary conditions are derived from the physical understanding of how the standing waves are sustained, that is, how the vibrating particles of the medium behave at the boundaries imposed on them.
For a tube open at one end and closed at the other filled with air, the modes are such that there is always an antinode at the open end and a node at the closed end....
1.1K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

217
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
217
Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity01:15

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

382
Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.
382
Fineness Modulus01:19

Fineness Modulus

1.0K
The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Jamming transition and normal modes of polydispersed soft particle packing.

Soft matter·2025
Same author

A minimal-length approach unifies rigidity in underconstrained materials.

Proceedings of the National Academy of Sciences of the United States of America·2019
Same author

Coarsening and mechanics in the bubble model for wet foams.

Physical review. E·2018
Same author

Normal Stresses, Contraction, and Stiffening in Sheared Elastic Networks.

Physical review letters·2018
Same author

Viscous forces and bulk viscoelasticity near jamming.

Soft matter·2017
Same author

Nonlocal Elasticity near Jamming in Frictionless Soft Spheres.

Physical review letters·2017

Related Experiment Video

Updated: Nov 1, 2025

Characterization of Anisotropic Leaky Mode Modulators for Holovideo
09:36

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

8.1K

Moduli and modes in the Mikado model.

Karsten Baumgarten1, Brian P Tighe1

  • 1Delft University of Technology, Process & Energy Laboratory, Leeghwaterstraat 39, 2628 CB Delft, The Netherlands. b.p.tighe@tudelft.nl.

Soft Matter
|June 21, 2021
PubMed
Summary

Low frequency vibrations control the elastic shear modulus in Mikado networks. New research reveals a precise relationship between soft modes and shear, clarifying previous inconsistencies in fiber network mechanics.

More Related Videos

Measurement of Chladni Mode Shapes with an Optical Lever Method
04:39

Measurement of Chladni Mode Shapes with an Optical Lever Method

Published on: June 5, 2020

5.4K
Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.3K

Related Experiment Videos

Last Updated: Nov 1, 2025

Characterization of Anisotropic Leaky Mode Modulators for Holovideo
09:36

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

8.1K
Measurement of Chladni Mode Shapes with an Optical Lever Method
04:39

Measurement of Chladni Mode Shapes with an Optical Lever Method

Published on: June 5, 2020

5.4K
Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures
08:01

Spectral and Angle-Resolved Magneto-Optical Characterization of Photonic Nanostructures

Published on: November 21, 2019

7.3K

Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Network Mechanics

Background:

  • Mikado networks are minimal models for semi-flexible fiber networks.
  • Previous studies noted power-law scaling of shear modulus (G ~ ρ^α) and soft modes (ω ~ ρ^β) with fiber density (ρ).
  • A discrepancy existed between the predicted and observed exponents α and β.

Purpose of the Study:

  • To investigate the control of elastic shear modulus by low-frequency vibrational modes in Mikado networks.
  • To resolve the inconsistency in the scaling exponents α and β.
  • To provide new insights into the coupling between soft modes and shear response.

Main Methods:

  • Experimental measurement of the vibrational density of states in 2D Mikado networks.
  • Analytical demonstration of the relationship between scaling exponents.

Main Results:

  • The vibrational density of states in Mikado networks was measured for the first time.
  • An analytical relationship α = β + 1 was derived, resolving the inconsistency.
  • New understanding of the coupling between soft modes and shear was achieved.

Conclusions:

  • Low-frequency vibrational modes are critical in determining the elastic shear modulus of Mikado networks.
  • The derived relationship α = β + 1 clarifies the mechanics of these fiber networks.
  • The study elucidates the origin of the transition from bending- to stretching-dominated responses.