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Olefin Metathesis Polymerization: Overview01:13

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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.
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The polymerization process that involves carbanion as an intermediate is called anionic polymerization. It is also a type of addition or chain-growth polymerization. Anionic polymerization gets initiated by a strong nucleophile such as an organolithium or a Grignard reagent. The most commonly used initiator for anionic polymerization is butyl lithium. Monomers involved in anionic polymerization must possess a vinyl group bonded to one or two electron-withdrawing groups. For instance,...
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Anionic Chain-Growth Polymerization: Mechanism01:04

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The mechanism for anionic chain-growth polymerization involves initiation, propagation, and termination steps. In the initiation step, a nucleophilic anion, such as butyl lithium, initiates the polymerization process by attacking the π bond of the vinylic monomer. As a result, a carbanion, stabilized by the electron‐withdrawing group, is generated. The resulting carbanion acts as a Michael donor in the propagation step and attacks the second vinylic monomer, which acts as a Michael...
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Ring-opening metathesis polymerization or ROMP involves strained cycloalkenes as starting materials. The mechanism of ROMP proceeds by reacting cycloalkene with Grubbs catalyst to give metallacyclobutane intermediate which undergoes a ring-opening reaction to form new carbene. The new carbene reacts with another molecule of cycloalkene. Repetition of these steps leads to the formation of an unsaturated open-chain polymer product. All these steps are reversible, however, relieving the ring...
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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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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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A general-purpose material property data extraction pipeline from large polymer corpora using natural language

Pranav Shetty1, Arunkumar Chitteth Rajan2, Chris Kuenneth2

  • 1School of Computational Science & Engineering, Atlanta, GA USA.

Npj Computational Materials
|April 10, 2023
PubMed
Summary
This summary is machine-generated.

We developed an automated pipeline using natural language processing to extract polymer material property data from scientific literature. This system efficiently retrieves chemical structure-property relationships, aiding materials discovery.

Keywords:
Computational methodsPolymers

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Polymer Science

Background:

  • The vast volume of materials science literature hinders the extraction of crucial chemistry-structure-property relationships.
  • Manual data extraction from scientific papers is time-consuming and inefficient for large-scale analysis.

Purpose of the Study:

  • To develop and validate an automated natural language processing (NLP) pipeline for extracting material property data from polymer literature abstracts.
  • To create a searchable database of polymer properties to accelerate materials discovery and innovation.

Main Methods:

  • Utilized natural language processing (NLP) techniques to automatically extract material property data from polymer abstracts.
  • Trained a specialized language model, MaterialsBERT, on 2.4 million materials science abstracts, achieving superior performance in named entity recognition tasks.
  • Processed approximately 130,000 abstracts to extract around 300,000 material property records within 60 hours.

Main Results:

  • The MaterialsBERT model demonstrated superior performance compared to baseline models in three out of five named entity recognition datasets.
  • Successfully extracted ~300,000 material property records from ~130,000 polymer literature abstracts.
  • The extracted data revealed non-trivial insights applicable to diverse fields including fuel cells, supercapacitors, and polymer solar cells.

Conclusions:

  • An automated NLP pipeline can effectively extract valuable material property information from published scientific literature.
  • The developed system significantly accelerates the process of data retrieval for materials science research.
  • The publicly available dataset (polymerscholar.org) facilitates easier access to material property data, supporting future research and development.