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Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

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Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...
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Recrystallization: Solid–Solution Equilibria01:10

Recrystallization: Solid–Solution Equilibria

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Recrystallization is a purification technique used to separate impurities from solid compounds. In this technique, no chemical reactions occur. Instead, it exploits physical properties only, specifically, the solubility differences between the desired compound and impurities, either at a single temperature or at different temperatures, and under other selected conditions. The solid-solution equilibrium (solubility equilibrium) of each component in the solution represents a binary phase...
1.1K
Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

2.2K
Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
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Related Experiment Video

Updated: Jun 18, 2025

Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering
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Growing Protein Crystals with Distinct Dimensions Using Automated Crystallization Coupled with In Situ Dynamic Light Scattering

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An Algorithm for Modeling Thermoplastic Spherulite Growth Using Crystallization Kinetics.

Jamal F Husseini1, Evan J Pineda2, Scott E Stapleton1

  • 1Department of Mechanical Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.

Materials (Basel, Switzerland)
|July 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a polymer crystallization model using kinetics to predict local crystalline distributions. The model accurately simulates spherulite formation and validates against experimental data, aiding in microstructure prediction for manufacturing.

Keywords:
crystallizationmodelingspherulitesthermoplastics

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Area of Science:

  • Polymer Science
  • Materials Science
  • Computational Modeling

Background:

  • Understanding polymer crystallization is crucial for material properties.
  • Spherulite morphology significantly impacts polymer performance.
  • Predictive models are needed for optimizing polymer processing.

Purpose of the Study:

  • To develop a spherulite growth model based on crystallization kinetics.
  • To determine local crystalline distributions within polymers.
  • To predict semi-crystalline microstructures for various manufacturing methods.

Main Methods:

  • Utilized crystallization kinetics to simulate nucleation and growth.
  • Employed an optimization algorithm to determine local crystallinities.
  • Discretized the domain into voxels for detailed simulation.
  • Validated the model against differential scanning calorimetry and microscopy data.

Main Results:

  • The model successfully simulated homogeneous and heterogeneous nucleation and spherulite growth.
  • Local crystallinities were determined based on volume and distance from the nucleus.
  • Model predictions showed good agreement with experimental data for polyether ether ketone.
  • The effect of cooldown rates on crystalline distributions was analyzed.

Conclusions:

  • The developed model accurately predicts polymer crystalline distributions.
  • This tool aids in understanding and controlling semi-crystalline microstructures.
  • The model can be integrated into multiscale thermomechanical analyses for improved material design.