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Related Concept Videos

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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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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Ziegler–Natta Chain-Growth Polymerization: Overview01:17

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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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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.
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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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Olefin Metathesis Polymerization: Ring-Opening Metathesis Polymerization (ROMP)01:16

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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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Updated: Nov 21, 2025

Combinatorial Synthesis of and High-throughput Protein Release from Polymer Film and Nanoparticle Libraries
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Automated knowledge extraction from polymer literature using natural language processing.

Pranav Shetty1, Rampi Ramprasad2

  • 1School of Computational Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA 30332, USA.

Iscience
|January 18, 2021
PubMed
Summary
This summary is machine-generated.

Scientists can automatically extract materials science knowledge from millions of research papers using natural language processing. This approach aids in discovering new polymers for innovative applications.

Keywords:
Artificial IntelligenceComputer ScienceMaterials SciencePolymers

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Data Science

Background:

  • Exponential growth in materials science literature presents challenges for hypothesis formulation.
  • Information overload hinders the discovery of new materials and applications.

Purpose of the Study:

  • To investigate the automatic inference of materials science knowledge from textual data.
  • To leverage natural language processing (NLP) for knowledge extraction from scientific literature.

Main Methods:

  • Utilized a dataset of 0.5 million polymer research papers.
  • Applied NLP techniques to train word vector representations in an unsupervised manner.
  • Performed time-based analyses to track polymer popularity and predict new applications.

Main Results:

  • Unsupervised vector representations successfully captured materials science knowledge.
  • Demonstrated the ability to track polymer trends and identify emerging applications.
  • Successfully predicted novel polymers for new applications based on literature data.

Conclusions:

  • Automatic knowledge inference from scientific text offers a new paradigm for materials discovery.
  • NLP methods can unlock hidden correlations within vast datasets.
  • This approach accelerates the identification of promising materials for future applications.