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Related Concept Videos

Radical Chain-Growth Polymerization: Mechanism01:09

Radical Chain-Growth Polymerization: Mechanism

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The radical chain-growth polymerization mechanism consists of three steps: initiation, propagation, and termination of polymerization. The polymerization initiates when a free radical generated from the radical initiator adds to the unsaturated bond in the monomer. The unpaired electron of the free radical and one π electron in the unsaturated bond creates a σ bond between the free radical and the monomer. As a result, the other π electron in the unsaturated bond converts this...
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Radical Chain-Growth Polymerization: Overview01:10

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Chain-growth or addition polymerization is successive addition reactions of monomers with a polymer chain. In radical chain-growth polymerization, the reaction proceeds via a free-radical intermediate. The free radical is formed from radical initiators, which spontaneously generate free radicals by homolytic fission. Organic peroxides (such as dibenzoyl peroxide, as shown in Figure 1) or azo compounds are popular radical initiators. A low concentration ratio of radical initiator to monomer is...
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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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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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Cationic Chain-Growth Polymerization: Mechanism00:57

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The cationic polymerization mechanism consists of three steps: initiation, propagation, and termination. In the initiation step of the polymerization process, the π bond of a monomer gets protonated by the Lewis acid catalyst, which is formed from boron trifluoride and water. The protonation of the π bond generates a carbocation stabilized by the electron‐donating group. In the propagation step, the π bond of the second monomer acts as a nucleophile and attacks the...
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Radical Chain-Growth Polymerization: Chain Branching01:17

Radical Chain-Growth Polymerization: Chain Branching

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The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...
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The Preparation and Properties of Thermo-reversibly Cross-linked Rubber Via Diels-Alder Chemistry
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GFN-xTB-Based Computations Provide Comprehensive Insights into Emulsion Radiation-Induced Graft Polymerization.

Kiho Matsubara1, Kei Takahashi2,3, Takeshi Matsuda4

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Machine learning optimizes radiation-induced graft polymerizations using realistic monomer data. This approach enhances understanding and control of polymerization processes under emulsion conditions.

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Area of Science:

  • Polymer Chemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Radiation-induced graft polymerization is a key technique for modifying material properties.
  • Optimizing these processes, especially under emulsion conditions, presents significant challenges.
  • Accurate monomer information is crucial for predictive modeling and process control.

Purpose of the Study:

  • To develop a machine learning-based framework for optimizing radiation-induced graft polymerizations.
  • To interpret the polymerization process using realistic monomer data.
  • To apply this methodology to polymerizations conducted under emulsion conditions.

Main Methods:

  • Utilized machine learning algorithms for process optimization.
  • Employed state-of-the-art semiempirical methods to calculate realistic monomer information.
  • Investigated radiation-induced graft polymerizations under emulsion conditions.

Main Results:

  • Successfully optimized radiation-induced graft polymerization parameters using machine learning.
  • Achieved accurate interpretation of the polymerization process through data-driven insights.
  • Demonstrated the effectiveness of the approach for emulsion-based systems.

Conclusions:

  • Machine learning provides a powerful tool for optimizing complex polymerization reactions.
  • Realistic monomer data is essential for accurate modeling and prediction in radiation chemistry.
  • The developed framework offers enhanced control and understanding of graft polymerization processes.