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Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

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For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
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Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

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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: Aug 25, 2025

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants
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Determining the Rheological Parameters of Polymers Using Artificial Neural Networks.

Anton Chepurnenko1

  • 1Strength of Materials Department, Faculty of Civil and Industrial Engineering, Don State Technical University, 344003 Rostov-on-Don, Russia.

Polymers
|October 14, 2022
PubMed
Summary

Artificial neural networks can predict polymer properties from stress relaxation curves. This method accurately determines rheological parameters without data smoothing, offering a versatile alternative to standard techniques.

Keywords:
artificial neural networkscreeppolyvinyl chloriderelaxationrheological parameters

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Area of Science:

  • Polymer Science
  • Materials Science
  • Computational Modeling

Background:

  • Predicting polymer properties is crucial for material design.
  • Traditional methods for analyzing stress relaxation data can be complex and require data preprocessing.
  • Artificial neural networks (ANNs) offer a promising computational approach for complex material behavior analysis.

Purpose of the Study:

  • To investigate the efficacy of ANNs in determining polymer rheological parameters from stress relaxation curves.
  • To develop and validate an ANN model for this purpose.

Main Methods:

  • Utilized the nonlinear Maxwell-Gurevich equation as the governing deformation law.
  • Developed an algorithm for generating training data from theoretical stress relaxation curves using the Euler method.
  • Trained ANNs in the MATLAB environment, using Mean Square Error (MSE) as the training performance criterion.
  • Validated the ANN model against experimental stress relaxation data of recycled polyvinyl chloride.

Main Results:

  • The ANN model demonstrated good approximation quality for experimental relaxation curves.
  • The performance of the ANN model was comparable to standard data processing methods.
  • A key advantage of the ANN approach was the elimination of the need for preliminary data smoothing.

Conclusions:

  • ANNs provide a viable and efficient method for determining polymer rheological parameters from stress relaxation data.
  • The proposed technique is adaptable for analyzing creep curves and various testing conditions (tension, bending, torsion, shear).
  • This approach offers a robust alternative to conventional methods, simplifying data analysis and expanding applicability.