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On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature
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Modeling of PEEK Crystallization Kinetics Under Transient Thermal Conditions.

Shahil Hamid1, To Yu Troy Su2, Soroush Azhdari1

  • 1Department of Materials Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

Polymers
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a kinetic model for poly-ether-ether-ketone (PEEK) crystallization, accurately predicting enthalpy changes under various cooling rates relevant to additive manufacturing processes.

Keywords:
additive manufacturingcrystallization kinetics modelingfast scanning calorimetrytransient thermal profile

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Area of Science:

  • Polymer Science
  • Materials Science
  • Chemical Engineering

Background:

  • Crystallization kinetics of polymers like PEEK are crucial for material properties.
  • Accurate prediction of crystallization under dynamic processing conditions remains challenging.

Purpose of the Study:

  • To develop a unified kinetic model for poly-ether-ether-ketone (PEEK) crystallization.
  • To accurately predict enthalpy evolution under arbitrary thermal profiles, including non-isothermal conditions relevant to additive manufacturing.

Main Methods:

  • A parallel dual-Nakamura kinetic model was formulated and calibrated using isothermal crystallization data from flash-differential scanning calorimetry (FSC).
  • Physics-based functions were developed to describe temperature-dependent crystallization weights and cooling-rate-dependent scalars were introduced for non-isothermal predictions.
  • The model was validated against interrupted FSC experiments with complex cooling-remelt cycles.

Main Results:

  • The developed model accurately predicts PEEK enthalpy evolution under arbitrary thermal profiles (R² ≈ 0.95).
  • The model successfully captures crystallization kinetics across a wide range of temperatures and cooling rates.
  • Validation against interrupted experiments mimicking additive manufacturing thermal histories confirmed model performance without additional fitting.

Conclusions:

  • The proposed kinetic modeling framework offers a practical and accurate method for predicting polymer crystallinity.
  • This approach is valuable for optimizing processing conditions in additive manufacturing and other polymer processing techniques.