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

4.5K
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...
4.5K
Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

4.1K
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...
4.1K
Cationic Chain-Growth Polymerization: Mechanism00:57

Cationic Chain-Growth Polymerization: Mechanism

2.9K
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...
2.9K
Polymers: Defining Molecular Weight01:01

Polymers: Defining Molecular Weight

3.9K
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...
3.9K
Polymers02:34

Polymers

41.7K
The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...
41.7K
Polymers02:34

Polymers

23.4K
23.4K

You might also read

Related Articles

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

Sort by
Same author

Coinfection with Giardiasis and Taeniasis.

The American journal of tropical medicine and hygiene·2026
Same author

Dibothriocephalus nihonkaiensis in a Regular Sushi Consumer.

The American journal of tropical medicine and hygiene·2026
Same author

Diagnosis of Listeria monocytogenes Meningitis Using the FilmArray Meningitis/Encephalitis Panel in a Patient With Prior Antibiotic Exposure: A Case Report.

Cureus·2025
Same author

Hydrogel spacer infection during prostate cancer radiotherapy: a case report of successful abscess management through radical prostatectomy.

International cancer conference journal·2025
Same author

Generative Model for Constructing Reaction Path from Initial to Final States.

Journal of chemical theory and computation·2025
Same author

CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne·2024
Same journal

From Cation Solvation to Anion Coordination: Lewis-Acidic Boranes Enable Halide Salt Electrolytes.

The journal of physical chemistry. B·2026
Same journal

In Vitro-Prepared A30P Alpha-Synuclein Fibrils Adopt the Conserved and Disease-Relevant Greek Key Fold.

The journal of physical chemistry. B·2026
Same journal

Metastructure Analysis of Self-Assembled Nanocubes with Different Equatorial Methyl Groups Based on Molecular Dynamics Simulations.

The journal of physical chemistry. B·2026
Same journal

A Cocoordinated <sup>1</sup>H Internal Reference Quantifies Proton-Exchange Bias in Coordinated-Water Diffusion.

The journal of physical chemistry. B·2026
Same journal

Unveiling Electrolyte-Dependent Coordination Site Dynamics for Redox Mediator Design in Lithium-O<sub>2</sub> Batteries: Exchange vs Rearrangement.

The journal of physical chemistry. B·2026
Same journal

The Role of Functional Groups in Substituted Benzoic Acids Used as Dopants in Liquid Crystal Mixtures on the Nematic-Isotropic Transitions.

The journal of physical chemistry. B·2026
See all related articles

Related Experiment Video

Updated: Feb 20, 2026

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.2K

Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with

Hodaka Mori1, Shunsuke Tonogai1, Yu Miyazaki1

  • 1Preferred Networks, Inc., Tokyo 100-0004, Japan.

The Journal of Physical Chemistry. B
|February 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new simulation method combining universal machine learning interatomic potentials (uMLIPs) with a time-dependent bond boost. This approach enables efficient and accurate simulations of polymerization and curing processes for advanced materials.

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.4K
Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

5.1K

Related Experiment Videos

Last Updated: Feb 20, 2026

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches
07:31

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches

Published on: September 1, 2023

3.2K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.4K
Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

5.1K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Polymer Science

Background:

  • Simulating polymerization and curing is crucial for advanced materials but challenging due to potential accuracy and rare chemical events.
  • Existing methods like ReaxFF require system-specific tuning, while universal machine learning interatomic potentials (uMLIPs) have limited sampling efficiency.

Purpose of the Study:

  • To develop a novel simulation framework for efficient and transferable modeling of polymerization and curing.
  • To overcome limitations of existing reactive force fields and uMLIPs in simulating complex chemical reactions.

Main Methods:

  • Integration of a universal machine learning interatomic potential (uMLIP) with a time-dependent bond-boost scheme.
  • A monotonically increasing bias potential accelerates simulations without system-specific parameterization.
  • A unified parameter set applicable across different reaction classes.

Main Results:

  • Accurate reproduction of trends in radical polymerization, including molecular-weight growth and monomer reactivity.
  • Captures the sharp molecular weight increase in nylon-6,6 polycondensation at high conversions.
  • Reveals interfacial ring-opening and cross-linking in epoxy curing on copper, consistent with experimental data.

Conclusions:

  • The coupled uMLIP and time-dependent bond boost framework enables practical, transferable simulations of polymerization and curing.
  • Provides molecular-level insights into polymer growth, interfacial adhesion, mechanistic pathways, and relative reactivity.