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A Practical User Guide to Stress Relaxation Spectra of Dynamic Covalent Networks.

Roy Wink¹, Oscar B Geerars¹, Johan P A Heuts¹

¹Laboratory of Supramolecular Polymer Chemistry, Department of Chemical Engineering & Chemistry, Institute for Complex Molecular Systems, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.

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Summary

Tikhonov regularization accurately extracts relaxation times from stress relaxation data, even with experimental challenges like noise. However, overlapping relaxation modes can reduce spectral resolution, highlighting the need for complementary methods.

Keywords:

Tikhonov regularization covalent adaptable networks dynamic covalent networks multimodality noise practical guidelines relaxation spectra simulations stress relaxation

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Published on: January 25, 2020

Area of Science:

Materials Science
Polymer Physics
Rheology

Background:

Stress relaxation tests are crucial for understanding polymer dynamics.
Extracting accurate relaxation spectra from experimental data can be challenging.
Tikhonov regularization is a common numerical method for spectral analysis.

Purpose of the Study:

To evaluate the impact of experimental challenges on Tikhonov regularization for relaxation spectra.
To provide practical guidance for interpreting stress relaxation data.
To enhance the characterization of dynamic covalent networks.

Main Methods:

Systematic simulation study of Tikhonov regularization.
Evaluation under conditions of noisy data, limited measurement times, and complex relaxation behavior.
Analysis of spectral resolution and accuracy of dominant relaxation times.

Main Results:

Tikhonov regularization reliably recovers dominant relaxation times across various conditions.
Spectral resolution decreases with overlapping relaxation modes or incomplete relaxation.
Accuracy is maintained for dominant modes but reduced for complex behaviors.

Conclusions:

Tikhonov regularization is a robust tool for relaxation spectra analysis, with practical limitations.
Understanding experimental challenges is key to accurate spectral interpretation.
Combining Tikhonov regularization with prior chemical knowledge and complementary methods optimizes characterization of dynamic materials.