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Related Concept Videos

Characteristics and Nomenclature of Copolymers01:24

Characteristics and Nomenclature of Copolymers

3.0K
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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Anionic Chain-Growth Polymerization: Mechanism01:04

Anionic Chain-Growth Polymerization: Mechanism

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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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Predicting Molecular Geometry02:27

Predicting Molecular Geometry

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VSEPR Theory for Determination of Electron Pair Geometries
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Anionic Chain-Growth Polymerization: Overview01:20

Anionic Chain-Growth Polymerization: Overview

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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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Cationic Chain-Growth Polymerization: Mechanism00:57

Cationic Chain-Growth Polymerization: Mechanism

2.6K
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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Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

3.7K
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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Related Experiment Video

Updated: Nov 29, 2025

Synthesis of Monodisperse Cylindrical Nanoparticles via Crystallization-driven Self-assembly of Biodegradable Block Copolymers
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Machine Learning Predictions of Block Copolymer Self-Assembly.

Kun-Hua Tu1, Hejin Huang1, Sangho Lee1

  • 1Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

Advanced Materials (Deerfield Beach, Fla.)
|November 18, 2020
PubMed
Summary

Machine learning models accurately predict block copolymer self-assembly patterns for nanofabrication. This approach aids in controlling solvent annealing parameters for improved morphology and reduced defects in nanodevices.

Keywords:
block copolymersmachine learningnanomanufacturingridge regressionself-assembly

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Science

Background:

  • Directed self-assembly of block copolymers is crucial for nanofabrication of sub-10 nm devices.
  • Solvent annealing is a critical step influencing film morphology and pattern quality.
  • Predicting and controlling solvent annealing parameters remains challenging due to complex interactions.

Purpose of the Study:

  • To apply machine learning tools for analyzing the solvent annealing process in block copolymer self-assembly.
  • To predict the effects of process parameters on morphology and defectivity.
  • To identify critical parameters influencing line/space pattern quality.

Main Methods:

  • Development and training of two neural networks to predict final morphology.
  • Construction of a ridge regression model to identify critical parameters.
  • Analysis of experimental data to validate machine learning predictions.

Main Results:

  • Neural networks achieved accurate predictions of final block copolymer morphology.
  • The ridge regression model successfully identified key parameters affecting pattern quality.
  • Machine learning models demonstrated strong agreement with experimental results.

Conclusions:

  • Machine learning offers a powerful approach to understand and control block copolymer self-assembly.
  • This methodology can inform and optimize nanomanufacturing processes.
  • Predictive modeling enhances the efficiency and reliability of nanofabrication.