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Related Concept Videos

Radical Chain-Growth Polymerization: Chain Branching01:17

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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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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 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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When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
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A kinetic chain growth algorithm in coarse-grained simulations.

Hong Liu1,2, You-Liang Zhu3, Zhong-Yuan Lu4

  • 1State Key Laboratory of Supramolecular Structure and Materials, Institute of Theoretical Chemistry, Jilin University, Changchun, 130021, China. hongliu@jlu.edu.cn.

Journal of Computational Chemistry
|September 29, 2016
PubMed
Summary
This summary is machine-generated.

A new kinetic chain growth algorithm for coarse-grained (CG) simulations accurately models polymerization. This method reproduces experimental polymer properties and allows for detailed kinetic and macroscopic feature analysis.

Keywords:
GPU-accelerationchain growthcoarse-grainingmolecular dynamics

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Area of Science:

  • Polymer Science
  • Computational Chemistry
  • Materials Science

Background:

  • Coarse-grained (CG) simulations are essential for modeling large-scale polymer systems.
  • Accurately capturing polymerization kinetics within CG models remains a challenge.
  • Existing CG models often lack detailed chemical information crucial for reaction dynamics.

Purpose of the Study:

  • To introduce a novel kinetic chain growth algorithm for CG simulations.
  • To enable the description of consecutive polymerization processes with defined reaction probabilities.
  • To bridge the gap between generic CG chain growth and specific chemical details.

Main Methods:

  • Developed a kinetic chain growth algorithm based on reaction probability.
  • Modeled the polymerization of styrene monomers into polystyrene chains.
  • Incorporated an Arrhenius-type reaction rate coefficient for kinetic modeling.
  • Allowed for both gradual and jump processes in bond formation.

Main Results:

  • The algorithm successfully reproduced experimental properties of polystyrene.
  • Validated the accurate simulation of individual monomer polymerization into chains.
  • Demonstrated the ability to reflect reaction impediments by linking CG and chemical details.
  • Showcased flexibility in modeling various bond formation processes.

Conclusions:

  • The proposed kinetic chain growth algorithm provides a robust framework for CG polymerization simulations.
  • It accurately captures polymer properties and kinetics, bridging CG and chemical scales.
  • The algorithm's versatility supports diverse CG models and has potential for simulating variable macroscopic polymer features.