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

Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

4.0K
Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
4.0K
Cationic Chain-Growth Polymerization: Mechanism00:57

Cationic Chain-Growth Polymerization: Mechanism

2.6K
The cationic polymerization mechanism consists of three steps: initiation, propagation, and termination. In the initiation step of the polymerization process, the π bond of a monomer gets protonated by the Lewis acid catalyst, which is formed from boron trifluoride and water. The protonation of the π bond generates a carbocation stabilized by the electron‐donating group. In the propagation step, the π bond of the second monomer acts as a nucleophile and attacks the...
2.6K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.6K
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...
2.6K
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

3.6K
Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
3.6K
Characteristics and Nomenclature of Copolymers01:24

Characteristics and Nomenclature of Copolymers

3.0K
Copolymers are the products obtained from the polymerization of multiple monomer species. So, in a polymer chain itself, there can be multiple repeating units that come from different monomers. The process of synthesizing a polymer from different monomer species is called copolymerization. When two monomers are involved, the polymer is known as a bipolymer. Polymers with three and four monomers are termed terpolymers and quaterpolymers, respectively. Figure 1 depicts the copolymerization of...
3.0K
Anionic Chain-Growth Polymerization: Overview01:20

Anionic Chain-Growth Polymerization: Overview

2.3K
The polymerization process that involves carbanion as an intermediate is called anionic polymerization. It is also a type of addition or chain-growth polymerization. Anionic polymerization gets initiated by a strong nucleophile such as an organolithium or a Grignard reagent. The most commonly used initiator for anionic polymerization is butyl lithium. Monomers involved in anionic polymerization must possess a vinyl group bonded to one or two electron-withdrawing groups. For instance,...
2.3K

You might also read

Related Articles

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

Sort by
Same author

Quantitative prediction of oil-water interfacial tension in surfactant systems using dissipative particle dynamics.

Soft matter·2026
Same author

Assessment and Optimization of Force Fields for Glycine Polymorphism and Solution Properties.

Journal of chemical theory and computation·2026
Same author

Clusterin reverses epitheliopathy, reduces inflammation, and restores goblet cells and corneal nerves in a mouse model of autoimmune dry eye.

Scientific reports·2026
Same author

Clusterin: A clinical translation spearhead for extracellular chaperones that promotes neural regeneration.

Neural regeneration research·2026
Same author

Revisiting reliability with human and machine learning raters under scoring design and rater configuration in the many-facet Rasch model.

The British journal of mathematical and statistical psychology·2026
Same author

Nonmimetic Gels Direct Novel Crystallization Behavior of Lenalidomide.

Crystal growth & design·2025

Related Experiment Video

Updated: Nov 8, 2025

Using Polystyrene-block-polyacrylic acid-coated Metal Nanoparticles as Monomers for Their Homo- and Co-polymerization
09:02

Using Polystyrene-block-polyacrylic acid-coated Metal Nanoparticles as Monomers for Their Homo- and Co-polymerization

Published on: July 9, 2015

12.5K

Phase Behavior of Correlated Random Copolymers.

Elena Patyukova1, Erte Xi2, Mark R Wilson1

  • 1Chemistry Department, Durham University, Durham DH1 3LE, U.K.

Macromolecules
|April 19, 2021
PubMed
Summary

This study calculates phase diagrams for correlated random copolymers using a novel distribution function and the method of moments. Findings reveal how segment fractions and polymerization influence phase transitions and compositions.

More Related Videos

Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives
09:22

Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives

Published on: February 7, 2017

8.0K
Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

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

Published on: September 26, 2016

8.1K

Related Experiment Videos

Last Updated: Nov 8, 2025

Using Polystyrene-block-polyacrylic acid-coated Metal Nanoparticles as Monomers for Their Homo- and Co-polymerization
09:02

Using Polystyrene-block-polyacrylic acid-coated Metal Nanoparticles as Monomers for Their Homo- and Co-polymerization

Published on: July 9, 2015

12.5K
Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives
09:22

Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives

Published on: February 7, 2017

8.0K
Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

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

Published on: September 26, 2016

8.1K

Area of Science:

  • Polymer Science
  • Statistical Mechanics
  • Computational Chemistry

Background:

  • Flory-Huggins theory is a cornerstone for understanding polymer phase behavior.
  • Correlated random copolymers present unique challenges due to sequence-dependent interactions.
  • Previous models often simplified copolymer structures, limiting applicability.

Purpose of the Study:

  • To develop a method for calculating Flory-Huggins phase diagrams for correlated random copolymers.
  • To investigate the impact of sequence characteristics and chain length on phase behavior.
  • To incorporate the effects of fractionation into phase diagram calculations.

Main Methods:

  • Derivation of a distribution function for two-letter (A, B) copolymer chains.
  • Application of the method of moments (Sollich and Cates, 1998) for computational efficiency.
  • Analysis of phase transition points and coexisting phase compositions.

Main Results:

  • Successfully calculated Flory-Huggins phase diagrams for correlated random copolymers.
  • Demonstrated dependence of phase diagram features on A-segment and AB-duplet fractions.
  • Quantified the influence of the degree of polymerization on phase transitions.
  • Showcased the significant role of fractionation in shaping phase diagrams.

Conclusions:

  • The developed approach accurately models phase behavior in correlated random copolymers.
  • Fractionation is a critical factor influencing the phase diagrams of statistical copolymers.
  • This work provides a robust framework for predicting copolymer phase behavior.