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Related Concept Videos

Types of Step-Growth Polymers: Polyesters01:20

Types of Step-Growth Polymers: Polyesters

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The introduction of polyesters has brought major development to the textile industry. The wrinkle-free behavior of polyester blends has eliminated the need for starching and ironing clothes.
Polyesters are commonly prepared from terephthalic acid and ethylene glycol; the crude product is known as poly(ethylene terephthalate) or PET. However, polyesters are synthesized industrially by transesterification of dimethyl terephthalate with ethylene glycol at 150 °C. The two reactants and the...
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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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Step-Growth Polymerization: Overview01:03

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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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Polymer Classification: Architecture01:14

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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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Free-Radical Chain Reaction and Polymerization of Alkenes02:35

Free-Radical Chain Reaction and Polymerization of Alkenes

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The conversion of alkenes to macromolecules called polymers is a reaction of high commercial importance. The structure of the polymer is defined by a repeating unit, while the terminal groups are considered insignificant. The average degree of polymerization represents the number of repeating units in the polymer molecule and is denoted by the subscript n.
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Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

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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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Updated: May 10, 2025

Designed for Molecular Recycling: A Lignin-Derived Semi-aromatic Biobased Polymer
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Machine Learning for Developing Sustainable Polymers.

Ziyu Huo1, Xiaoyu Xie1, Rong Tong1

  • 1Department of Chemical Engineering, Virginia Polytechnic Institute and State University, 635 Prices Fork Road, Blacksburg, Virginia, 24061, USA.

Chemistry (Weinheim an Der Bergstrasse, Germany)
|April 23, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accelerates the discovery of sustainable polymers from renewable resources. This approach enhances efficiency and reduces environmental impact compared to traditional methods.

Keywords:
bayesian optimizationmachine learningpolyestersustainable polymer

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Area of Science:

  • Polymer Science
  • Materials Science
  • Sustainable Chemistry

Background:

  • Growing demand for eco-friendly materials drives interest in sustainable polymers.
  • Conventional polymer development is inefficient and resource-intensive.
  • Machine learning (ML) offers advanced predictive and discovery capabilities.

Purpose of the Study:

  • To review emerging trends in ML applications for sustainable polymer development.
  • To focus on ML's role in catalyst discovery, property optimization, and polymer design.
  • To identify challenges and solutions for ML in sustainable polymer science.

Main Methods:

  • Literature review of ML applications in sustainable polymer science.
  • Analysis of ML techniques for catalyst discovery.
  • Examination of ML for property prediction and optimization.
  • Exploration of ML in novel polymer design.

Main Results:

  • ML significantly accelerates the identification and design of sustainable polymers.
  • ML aids in optimizing polymer properties for specific applications.
  • Key challenges include data scarcity and model interpretability.
  • Solutions involve transfer learning and physics-informed ML.

Conclusions:

  • ML is a transformative tool for advancing sustainable polymer research.
  • Addressing ML challenges is crucial for unlocking its full potential.
  • Future work should focus on integrated ML approaches for efficient material design.