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Related Concept Videos

Plasticizers01:31

Plasticizers

107
Water-reducers, or plasticizers, are chemical admixtures used in concrete to improve strength and workability. These additives reduce the water-cement ratio without compromising workability, lower the cement content while maintaining the same workability, or increase workability to assist concrete placement in inaccessible areas.
Plasticizers function by using surface-active agents to create repulsive electrostatic forces between cement particles. This dispersion enhances the concrete's...
107
Superplasticizers01:30

Superplasticizers

112
Superplasticizers are advanced admixtures that enhance the workability of concrete by lowering the water content without compromising the strength of the material. These substances are highly effective water reducers, improving concrete flow, making it easier to work with, and enabling concrete to reach inaccessible areas or densely reinforced sections without mechanical vibration. The key components in superplasticizers are either sulfonated melamine or naphthalene formaldehyde condensates,...
112
Plastic Behavior01:21

Plastic Behavior

249
A material's elastic behavior is characterized by the disappearance of stress once the load is removed, allowing the material to return to its original state. However, when stress surpasses the yield point, yielding commences, marking the onset of plastic deformation or permanent set. This change from elastic to plastic behavior is influenced by the peak stress value and the duration before the load is removed. An intriguing observation occurs when a specimen is loaded, unloaded, and...
249

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Prediction of Plasticizer Property Based on an Improved Genetic Algorithm.

Yuyin Zhang1, Ningjie Deng1, Shiding Zhang1

  • 1National Energy R&D Center for Biorefinery, Beijing University of Chemical Technology, Beijing 100029, China.

Polymers
|October 27, 2022
PubMed
Summary

A novel genetic algorithm accurately predicts plasticizer substitution factors (SF) by identifying key molecular descriptors. This method enhances prediction accuracy, aiding in plasticizer performance evaluation and cost accounting.

Keywords:
genetic algorithmgrid search algorithmmachine learningplasticizersubstitution factor

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Area of Science:

  • Polymer Science
  • Computational Chemistry
  • Materials Science

Background:

  • Plasticizers exhibit varying plasticizing properties, impacting product performance and cost.
  • The substitution factor (SF) is a critical metric for evaluating plasticizer efficiency and cost-effectiveness.

Purpose of the Study:

  • To develop a predictive model for plasticizer substitution factor (SF) based on molecular structure.
  • To identify key molecular descriptors highly correlated with SF using an advanced genetic algorithm.

Main Methods:

  • Implementation of a genetic algorithm with variable mutation probability for descriptor screening.
  • Development of a quantitative structure-property relationship (QSPR) model for SF prediction.
  • Validation of the model using test sets and cross-validation techniques.

Main Results:

  • The improved genetic algorithm significantly enhanced prediction accuracy for SF.
  • The developed model achieved a coefficient of determination (R²) of 0.92 for both test and cross-validation sets.
  • Selected molecular descriptors predominantly relate to molecular branching, highlighting its importance in plasticization.

Conclusions:

  • This study establishes a robust link between plasticizer molecular structure and SF.
  • The findings provide a foundation for advanced plasticizer performance evaluation and system modeling.
  • The developed predictive model offers a valuable tool for optimizing plasticizer selection and design.