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

Polymers02:34

Polymers

37.7K
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...
37.7K
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

3.9K
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.
3.9K
Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

3.7K
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...
3.7K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.4K
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...
2.4K
Olefin Metathesis Polymerization: Overview01:13

Olefin Metathesis Polymerization: Overview

2.3K
Recently, the development of olefin metathesis polymerization advanced the field of polymer synthesis. Simply put, the reorganization of substituents on their double bonds between two olefins in the presence of a catalyst is known as the olefin metathesis reaction. The use of metathesis reaction for polymer synthesis is called olefin metathesis polymerization.
Ruthenium-based Grubbs catalyst is the most commonly used catalyst for olefin metathesis polymerization. Grubbs catalyst consists...
2.3K
Characteristics and Nomenclature of Copolymers01:24

Characteristics and Nomenclature of Copolymers

2.7K
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...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Hierarchical Chiral Self-Assembly of Nanocylinders Composed of Sequence-Defined Mesogenic Dimers.

Journal of the American Chemical Society·2026
Same author

Molecular dynamics investigation of the impact of methylation on the nematic phase of phenyl benzoate mesogens and dimers.

Soft matter·2026
Same author

A user's guide to your first self-driving liquid handling lab.

Digital discovery·2026
Same author

Iron-Catalyzed Cross-[2 + 2] Cycloaddition of Butadiene and α,ω-Dienes for Ductile and Chemically Recyclable Poly(oligocyclobutanes).

Journal of the American Chemical Society·2026
Same author

Asymmetric Effects Underlying Dynamic Heterogeneity in Miscible Blends of Poly(methyl methacrylate) with Poly(ethylene oxide).

Macromolecules·2026
Same author

Automation-Assisted Photoinduced Atom Transfer Radical Polymerization.

ACS polymers Au·2026
Same journal

Can nanozymes achieve more than enzymes?

Nature reviews. Materials·2026
Same journal

Delivering living medicines with biomaterials.

Nature reviews. Materials·2026
Same journal

Materials Advances for Distributed Environmental Sensor Networks at Scale.

Nature reviews. Materials·2026
Same journal

Atomically thin bioelectronics.

Nature reviews. Materials·2025
Same journal

Ingestible Electronics for Diagnostics and Therapy.

Nature reviews. Materials·2025
Same journal

Materials and device strategies to enhance spatiotemporal resolution in bioelectronics.

Nature reviews. Materials·2025
See all related articles

Related Experiment Video

Updated: Sep 29, 2025

Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight MALDI-TOF Mass Spectrometry
06:56

Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight MALDI-TOF Mass Spectrometry

Published on: June 10, 2018

25.6K

Machine learning in combinatorial polymer chemistry.

Adam J Gormley1, Michael A Webb2

  • 1Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.

Nature Reviews. Materials
|August 16, 2021
PubMed
Summary
This summary is machine-generated.

Designing functional polymers relies on understanding their structure-function relationships. New methods in polymer chemistry and machine learning enable the creation of tailored polymeric materials for specific applications.

More Related Videos

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries
10:58

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries

Published on: September 6, 2012

10.5K
Polymer Microarrays for High Throughput Discovery of Biomaterials
13:37

Polymer Microarrays for High Throughput Discovery of Biomaterials

Published on: January 25, 2012

14.7K

Related Experiment Videos

Last Updated: Sep 29, 2025

Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight MALDI-TOF Mass Spectrometry
06:56

Characterization of Synthetic Polymers via Matrix Assisted Laser Desorption Ionization Time of Flight MALDI-TOF Mass Spectrometry

Published on: June 10, 2018

25.6K
Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries
10:58

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries

Published on: September 6, 2012

10.5K
Polymer Microarrays for High Throughput Discovery of Biomaterials
13:37

Polymer Microarrays for High Throughput Discovery of Biomaterials

Published on: January 25, 2012

14.7K

Area of Science:

  • Polymer Science
  • Materials Engineering
  • Computational Chemistry

Background:

  • Designing functional polymers requires navigating complex structure-function relationships.
  • Traditional methods face challenges in exploring vast chemical spaces.
  • Emerging technologies offer new avenues for material discovery.

Purpose of the Study:

  • To explore the potential of combinatorial polymer chemistry and machine learning for polymer design.
  • To engineer polymers with specific, fit-for-purpose functionalities.
  • To accelerate the discovery of novel polymeric materials.

Main Methods:

  • Utilizing combinatorial polymer synthesis techniques.
  • Applying machine learning algorithms to predict polymer properties.
  • Analyzing structure-property correlations in polymer datasets.

Main Results:

  • Demonstrated the feasibility of using combined approaches for polymer design.
  • Identified key structural features influencing polymer function.
  • Showcased the creation of polymers with targeted properties.

Conclusions:

  • Combinatorial polymer chemistry and machine learning are powerful tools for designing functional polymers.
  • These integrated approaches can accelerate the development of advanced polymeric materials.
  • Future research can leverage these methods for broader material innovation.