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Updated: Mar 29, 2026

Evaluation of the Curing of Adhesive Systems by Rheological and Thermal Testing
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A Theory-Guided Machine Learning and Molecular Dynamics Approach for Characterizing Fast-Curing Polyurethane Systems.

Luohaoran Wang1, Jacob Harris1, Steven Mamolo2

  • 1Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Polymers
|March 28, 2026
PubMed
Summary

Quantifying cure kinetics and glass transition temperature (Tg) in fast-curing polyurethane (PU) systems is challenging at low degrees of cure (DoC). This study developed a framework linking kinetics, gelation, and Tg evolution, improving accuracy in the low-DoC region.

Keywords:
cure kineticsdegree of cure (DoC)differential scanning calorimetry (DSC)gelationglass transition temperature (Tg)molecular dynamics (MD)polyurethane

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Area of Science:

  • Polymer Science and Engineering
  • Materials Chemistry
  • Computational Materials Science

Background:

  • Fast-curing polyurethane (PU) systems are crucial for high-throughput manufacturing.
  • Accurate quantification of cure kinetics, gelation, and glass transition temperature (Tg) is difficult, particularly at low degrees of cure (DoC).
  • Existing methods struggle to precisely characterize early-stage curing behavior.

Purpose of the Study:

  • To develop a comprehensive framework for characterizing fast-curing PU systems.
  • To accurately determine cure kinetics, gelation points, and Tg evolution across the entire DoC range.
  • To extend the reliable prediction of Tg into the challenging low-DoC regime.

Main Methods:

  • Non-isothermal differential scanning calorimetry (DSC) at multiple heating rates to analyze reaction enthalpy and conversion.
  • Rheometry at 50 °C to determine the gelation point.
  • Molecular dynamics (MD) simulations (LAMMPS) with topological crosslinking and NPT thermal scans to extract Tg.
  • Gaussian process regression, constrained by the DiBenedetto relationship, to fuse experimental and simulation data.

Main Results:

  • Kamal-Sourour (KS) parameters accurately modeled conversion and curing rates (R² > 0.99 and >0.95).
  • Gelation was experimentally determined between 475 and 625 s (DoC ≈ 0.53).
  • A combined experimental and MD approach successfully extended Tg prediction into the low-DoC regime, yielding λ ≈ 0.29.

Conclusions:

  • The developed framework effectively links cure kinetics, gelation, and Tg evolution in fast-curing PU systems.
  • The study highlights the low-DoC region as the primary source of uncertainty in Tg prediction.
  • This integrated approach provides a more robust understanding of PU curing behavior for manufacturing applications.