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Characteristics and Nomenclature of Copolymers01:24

Characteristics and Nomenclature of Copolymers

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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...
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Polymer Classification: Stereospecificity01:26

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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...
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Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

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

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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...
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Polymer Classification: Architecture01:14

Polymer Classification: Architecture

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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...
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Polymers02:34

Polymers

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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...
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Copolymer Sequence Regulation Enabled by Reactivity Ratio Fingerprints via Machine Learning.

Zexi Zhang1, Chengda Zhou1, Yufei Chen1

  • 1Department of Macromolecular Science, State Key Laboratory of Molecular Engineering of Polymers, Research Center of AI for Polymer Science, Fudan University, Shanghai, 200433, China.

Angewandte Chemie (International Ed. in English)
|October 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning platform using reactivity ratio fingerprints (rFPs) to efficiently determine reactivity ratios in copolymerizations. This approach enables on-demand polymer sequence tailoring for advanced material properties.

Keywords:
CopolymersMachine learningRadical polymerizationReactivity ratioSequence control

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Area of Science:

  • Polymer Chemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Sequence control is vital for tuning polymer properties in advanced applications.
  • Reactivity ratios are key parameters for analyzing and regulating polymer sequences.
  • Traditional methods for determining reactivity ratios are inefficient and limited to binary systems.

Purpose of the Study:

  • To develop a machine learning platform for efficient reactivity ratio determination.
  • To enable reactivity ratio analysis in both binary and ternary copolymerizations.
  • To facilitate on-demand polymer sequence tailoring and property engineering.

Main Methods:

  • Development of a machine learning platform utilizing novel "reactivity ratio fingerprints" (rFPs).
  • Training deep learning models on millions of rFPs for high-efficiency determination.
  • Application to binary and ternary copolymerizations with sparse experimental data.

Main Results:

  • Millisecond-level determination of reactivity ratios from minimal data.
  • Demonstrated versatility across diverse conditions (temperature, solvent).
  • Successful rFP-guided reaction design for on-demand sequence tailoring.
  • Identification of binary and ternary azeotropic copolymerizations.

Conclusions:

  • The developed machine learning platform offers a highly efficient strategy for determining reactivity ratios.
  • The approach provides a generalizable framework for sequencing complex polymer chains.
  • This facilitates precise control over polymer properties for advanced applications.