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

Polymers: Defining Molecular Weight01:01

Polymers: Defining Molecular Weight

Unlike small molecules with definite molecular weights, polymers are a mixture of individual polymer chains of varying lengths, each with a unique molecular weight. So, the molecular weight of a polymer is expressed as an average value based on the average size of the polymer chains. The two most common forms of averages used for polymers are the number average molecular weight and weight average molecular weight.
The number average molecular weight (Mn) is the summation of the number...
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
Anionic Chain-Growth Polymerization: Mechanism01:04

Anionic Chain-Growth Polymerization: Mechanism

The mechanism for anionic chain-growth polymerization involves initiation, propagation, and termination steps. In the initiation step, a nucleophilic anion, such as butyl lithium, initiates the polymerization process by attacking the π bond of the vinylic monomer. As a result, a carbanion, stabilized by the electron‐withdrawing group, is generated. The resulting carbanion acts as a Michael donor in the propagation step and attacks the second vinylic monomer, which acts as a Michael acceptor.
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
Determination of Molar Masses of Polymers I01:24

Determination of Molar Masses of Polymers I

Polymerization produces macromolecules with a range of chain lengths due to the random nature of molecular growth processes. As chains form and terminate at different stages, a single polymer sample contains molecules of varying sizes rather than a uniform structure. This variability is described using average molar masses and distribution-related parameters, which together provide a comprehensive understanding of polymer characteristics.The distribution of molar masses plays a critical role in...
Determination of Molar Masses of Polymers II01:27

Determination of Molar Masses of Polymers II

Polymer samples typically consist of macromolecular chains with a distribution of lengths, resulting in a range of molar masses rather than a single discrete value. Conventional descriptors such as the number-average molar mass and weight-average molar mass quantify this distribution but do not fully capture polymer behavior in solution..The viscosity-average molar mass provides a more realistic description of polymer behavior in solution because it accounts for the enhanced contribution of...

You might also read

Related Articles

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

Sort by
Same author

Molecular Model for Linear Viscoelastic Properties of Entangled Polymer Networks.

Macromolecules·2024
Same author

A Comparison between Predictions of the Miller-Macosko Theory, Estimates from Molecular Dynamics Simulations, and Long-Standing Experimental Data of the Shear Modulus of End-Linked Polymer Networks.

Macromolecules·2024
See all related articles

Related Experiment Video

Updated: Jun 25, 2026

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers
08:00

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

Published on: October 25, 2017

Phantom Force Balance Procedure for Predicting the Modulus of Entangled Polymer Networks.

Tim Bernhard1,2, Andrei A Gusev2

  • 1Laboratory for Nanometallurgy, Department of Materials, ETH Zürich, 8093 Zürich, Switzerland.

ACS Polymers Au
|October 13, 2025
PubMed
Summary
This summary is machine-generated.

A new computational method accurately predicts polymer network shear modulus using minimal resources. This approach offers a faster, cost-effective alternative to traditional simulation methods for materials science.

Keywords:
Monte Carlo network generationend-linked polymer networksentanglementsequilibrium shear modulusforce balance homogenization procedure

More Related Videos

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy
11:13

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy

Published on: August 20, 2018

Covalent Attachment of Single Molecules for AFM-based Force Spectroscopy
10:37

Covalent Attachment of Single Molecules for AFM-based Force Spectroscopy

Published on: March 16, 2020

Related Experiment Videos

Last Updated: Jun 25, 2026

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers
08:00

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

Published on: October 25, 2017

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy
11:13

Covalent Immobilization of Proteins for the Single Molecule Force Spectroscopy

Published on: August 20, 2018

Covalent Attachment of Single Molecules for AFM-based Force Spectroscopy
10:37

Covalent Attachment of Single Molecules for AFM-based Force Spectroscopy

Published on: March 16, 2020

Area of Science:

  • Polymer Science
  • Computational Materials Science
  • Rheology

Background:

  • Predicting the shear modulus of entangled polymer networks is crucial for material design.
  • Existing methods like molecular dynamics (MD) simulations are computationally intensive.
  • Theoretical models may not capture complex network architectures accurately.

Purpose of the Study:

  • To present a novel computational phantom Force Balance, Maximum Entropy Homogenization Procedure (FB-MEHP).
  • To efficiently predict the equilibrium shear modulus of entangled polymer networks.
  • To validate the FB-MEHP against established simulation and theoretical methods.

Main Methods:

  • Utilizing a Monte Carlo method to generate bead-spring polymer network microstructures.
  • Introducing entanglements by creating tetrafunctional cross-links between adjacent network strands.
  • Optimizing microstructures to their minimum free energy state for modulus calculation.

Main Results:

  • FB-MEHP demonstrated near-perfect agreement with stress-relaxation MD simulations and Miller-Macosko theory (MMT).
  • Computational resources required by FB-MEHP were significantly lower (4+ orders of magnitude less) than MD simulations.
  • The procedure showed good agreement with experimental data for diverse polymer networks, including bottlebrush and comb-like structures.

Conclusions:

  • The FB-MEHP is a computationally efficient and accurate method for predicting polymer network shear modulus.
  • This procedure offers a practical tool for predicting the modulus of various polymer network architectures.
  • The FB-MEHP has potential applications in designing and optimizing polymer-based materials.