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: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

3.6K
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.
3.6K
Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

2.5K
Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
2.5K
Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

3.0K
Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
3.0K
Polymers02:34

Polymers

36.2K
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...
36.2K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.3K
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...
2.3K
Polymer Classification: Architecture01:14

Polymer Classification: Architecture

2.8K
Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Mechanically Robust and Depolymerizable Polyesters With Near-Complete Monomer Recovery for Circular Additive Manufacturing.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Composition consistency as a critical quality attribute for material extrusion additive manufacturing.

MRS communications·2026
Same author

AI-Assisted Design of Chemically Recyclable Polymers for Food Packaging.

Polymers·2026
Same author

AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes.

The journal of physical chemistry. B·2026
Same author

Effect of polymer binder on dispersion stability of dense non-colloidal pastes.

Soft matter·2026
Same author

Machine Learning for Green Solvents: Assessment, Selection and Substitution.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025

Related Experiment Video

Updated: Aug 23, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.5K

Solvent selection for polymers enabled by generalized chemical fingerprinting and machine learning.

Joseph Kern1, Shruti Venkatram1, Manali Banerjee1

  • 1School of Materials Science and Engineering, College of Engineering, Georgia Institute of Technology, 771 Ferst Drive, J. Erskine Love Building, Atlanta, GA 30332-0245, USA. rampi.ramprasad@mse.gatech.edu.

Physical Chemistry Chemical Physics : PCCP
|October 31, 2022
PubMed
Summary

Machine learning models predict polymer-solvent solubility at room temperature. These advanced models offer high accuracy for known polymers and solvents, improving with more diverse data.

More Related Videos

Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer
10:22

Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer

Published on: November 30, 2020

3.5K
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

25.5K

Related Experiment Videos

Last Updated: Aug 23, 2025

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
09:04

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Published on: April 18, 2019

12.5K
Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer
10:22

Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer

Published on: November 30, 2020

3.5K
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

25.5K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Polymer Science

Background:

  • Predicting polymer solubility is crucial for materials design.
  • Existing data-driven methods have limitations in versatility and accuracy.

Purpose of the Study:

  • To develop advanced machine learning models for predicting polymer-solvent solubility at room temperature.
  • To establish a generalizable approach for handling diverse polymer-solvent combinations.

Main Methods:

  • Trained machine learning models on experimental solubility data.
  • Employed a generalizable fingerprinting method for polymers and solvents.
  • Validated model performance across various polymer-solvent pairs.

Main Results:

  • Achieved high prediction accuracy when polymer and/or solvent were present in the training set.
  • Observed modest performance for novel solvents due to limited solvent diversity in training data.
  • Demonstrated significant advancements in protocol, validity, and versatility over previous methods.

Conclusions:

  • The developed machine learning models show strong potential for predicting polymer-solvent solubility.
  • Expanding the diversity of solvents in the training dataset is key to improving model performance for new combinations.
  • This data-driven approach offers a versatile tool for materials discovery and formulation.