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

Metallic Solids02:37

Metallic Solids

20.8K
Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
20.8K
Structures of Solids02:22

Structures of Solids

17.9K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
17.9K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

20.2K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
20.2K
Molecular Comparison of Gases, Liquids, and Solids02:26

Molecular Comparison of Gases, Liquids, and Solids

55.3K
Particles in a solid are tightly packed together (fixed shape) and often arranged in a regular pattern; in a liquid, they are close together with no regular arrangement (no fixed shape); in a gas, they are far apart with no regular arrangement (no fixed shape). Particles in a solid vibrate about fixed positions (cannot flow) and do not generally move in relation to one another; in a liquid, they move past each other (can flow) but remain in essentially constant contact; in a gas, they move...
55.3K
Energy Bands in Solids01:01

Energy Bands in Solids

2.0K
Isolated atoms have discrete energy levels that are well described by the Bohr model. And, it quantifies the energy of an electron in a hydrogen atom as En. Higher quantum numbers 'n' yield less negative, closer electron energy levels.
 Band Formation:
When atoms are brought close together, as in a solid, these discrete energy levels begin to split due to the overlap of electron orbitals from adjacent atoms. This split occurs because of the Pauli exclusion principle, which states...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Active Solids: Topological Defect Self-Propulsion Without Flow.

Physical review letters·2026
Same author

Continuum mechanics of differential growth in disordered granular matter.

Soft matter·2025
Same author

Geometrically frustrated rose petals.

Science (New York, N.Y.)·2025
Same author

Active Fluids Form System-Spanning Filamentary Networks.

Physical review letters·2025
Same author

The 2025 motile active matter roadmap.

Journal of physics. Condensed matter : an Institute of Physics journal·2025
Same author

Asymmetric fluctuations and self-folding of active interfaces.

Proceedings of the National Academy of Sciences of the United States of America·2024
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: Feb 7, 2026

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.6K

Geometric Frustration and Solid-Solid Transitions in Model 2D Tissue.

Michael Moshe1,2, Mark J Bowick2,3, M Cristina Marchetti2

  • 1Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA.

Physical Review Letters
|July 14, 2018
PubMed
Summary
This summary is machine-generated.

We explored the mechanical properties of 2D cellular tissues, finding a transition from soft to stiff nonlinear elasticity as cell shape parameters change. This transition is driven by geometric incompatibility, distinct from classical models.

More Related Videos

Isolation and Analysis of Traceable and Functionalized Extracellular Vesicles from the Plasma and Solid Tissues
09:57

Isolation and Analysis of Traceable and Functionalized Extracellular Vesicles from the Plasma and Solid Tissues

Published on: October 17, 2022

2.8K
Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry
11:08

Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Published on: May 5, 2017

13.5K

Related Experiment Videos

Last Updated: Feb 7, 2026

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.6K
Isolation and Analysis of Traceable and Functionalized Extracellular Vesicles from the Plasma and Solid Tissues
09:57

Isolation and Analysis of Traceable and Functionalized Extracellular Vesicles from the Plasma and Solid Tissues

Published on: October 17, 2022

2.8K
Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry
11:08

Analysis of Cell Suspensions Isolated from Solid Tissues by Spectral Flow Cytometry

Published on: May 5, 2017

13.5K

Area of Science:

  • Biophysics
  • Materials Science
  • Computational Biology

Background:

  • Two-dimensional cellular tissues exhibit complex mechanical behaviors.
  • Understanding tissue mechanics is crucial for developmental biology and tissue engineering.
  • Discrete vertex models offer a framework to study cellular tissue mechanics.

Purpose of the Study:

  • To investigate the mechanical behavior of two-dimensional cellular tissues using continuum limits of discrete vertex models.
  • To identify the transition between soft and nonlinear elastic regimes based on cell shape parameters.
  • To analyze the role of geometric incompatibility in tissue elasticity.

Main Methods:

  • Formulating the continuum limit of discrete vertex models.
  • Defining an energy function penalizing deviations from target cell area (A₀) and perimeter (P₀).
  • Analyzing the dimensionless target shape index (s₀ = P₀/√A₀) to characterize tissue behavior.

Main Results:

  • A transition from a soft elastic regime to a stiffer nonlinear elastic regime was observed as s₀ varied.
  • Geometric incompatibility between target area and perimeter drives the transition to nonlinear elasticity.
  • The ground state in the soft regime exhibits degenerate solutions, lifted by geometric incompatibility at a critical s₀ᶜ.

Conclusions:

  • Cellular tissues display nonlinear elastic responses distinct from classical elasticity due to geometric incompatibility.
  • The study provides insights into the fundamental mechanical principles governing cellular tissues.
  • An analogy is drawn between cellular tissue mechanics and anelastic deformations in solids.