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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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Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)00:53

Olefin Metathesis Polymerization: Acyclic Diene Metathesis (ADMET)

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Acyclic diene metathesis polymerization or ADMET polymerization involves cross-metathesis of terminal dienes, such as 1,8-nonadiene, to give linear unsaturated polymer and ethylene. As ADMET is a reversible process, the formed ethylene gas must be removed from the reaction mixture to complete the polymerization process.
Similar to cross-metathesis, ADMET also involves the formation of metallacyclobutane intermediate by [2+2] cycloaddition of one of the double bonds of a terminal diene with...
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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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Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

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

Ziegler–Natta Chain-Growth Polymerization: Overview

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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...
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Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

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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...
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Polymer Microarrays for High Throughput Discovery of Biomaterials
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PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers.

A Nolan Wilson1, Peter C St John1, Daniela H Marin1

  • 1Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States.

Macromolecules
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

Scientists developed PolyID, a machine learning tool, to discover high-performance biobased polymers from renewable resources. This AI accelerates the search for sustainable plastics, identifying promising alternatives to fossil-derived materials.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Sustainable Chemistry

Background:

  • The transition to a sustainable economy necessitates replacing fossil-derived plastics with polymers from biomass and waste.
  • Exploring the vast potential of renewable feedstocks for advanced material properties is experimentally challenging.
  • Machine learning offers a powerful approach to navigate complex material design spaces.

Purpose of the Study:

  • To develop a machine learning tool, PolyID, for efficient discovery of biobased polymers with enhanced performance.
  • To reduce the search space for renewable feedstocks and accelerate the identification of sustainable polymer candidates.
  • To enable quantitative structure-property relationship (QSPR) analysis for biobased polymers.

Main Methods:

  • Development of PolyID, a multioutput graph neural network for polymer QSPR analysis.
  • Implementation of a novel domain-of-validity method to improve model accuracy by addressing data gaps.
  • Benchmarking the model against held-out data and experimentally synthesized polymers.
  • Prediction of properties for over 1.4 million potential biobased polymers derived from accessible small molecules.

Main Results:

  • PolyID achieved a mean absolute error of 19.8 °C for glass transition temperature predictions on test data and 26.4 °C on experimental data.
  • Identified five poly(ethylene terephthalate) (PET) analogues with predicted improvements in thermal and transport properties.
  • Experimental validation confirmed a PET analogue's glass transition temperature (85–112 °C), exceeding that of PET and aligning with PolyID predictions.
  • Demonstrated model explainability through analysis of bond importance for a biobased nylon.

Conclusions:

  • PolyID effectively reduces the design space for renewable feedstocks, enabling efficient discovery of high-performance biobased polymers.
  • The tool provides accurate and explainable predictions, aiding researchers in navigating the vast landscape of sustainable materials.
  • PolyID facilitates the discovery of novel biobased polymers with superior thermal and transport properties, contributing to a more sustainable economy.