Predicting yielding in attractive colloidal gels
Deepak Mangal1, Mohammad Nabizadeh1, Safa Jamali1
1Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts 02115, USA.
Physical Review. E
|February 17, 2024
Summary
Network science reveals edge betweenness centrality predicts yielding in soft glassy materials. This finding aids in designing materials with specific elasto-visco-plastic properties by identifying structural failure points.
Area of Science:
- Materials Science
- Soft Matter Physics
- Network Science
Background:
- Soft glassy materials exhibit yield stress, leading to elasto-visco-plastic mechanics.
- Understanding topological and structural failure points during yielding is crucial for material design.
- Predicting these failure points is challenging due to dynamic structure development and complex processes like rearrangements and anisotropy.
Purpose of the Study:
- To investigate colloidal gels as a model for soft glassy materials during yielding.
- To identify reliable predictors for yielding in these materials.
- To apply network science tools to understand structural failure mechanisms.
Main Methods:
- Utilized network science tools to analyze colloidal gels.
- Modeled soft glassy materials to study yielding behavior.
- Investigated the relationship between network topology and material failure.
Main Results:
- Identified edge betweenness centrality as a universal predictor for yielding.
- Demonstrated the predictor's effectiveness across various state variables (e.g., volume fraction, attraction strength/range).
- Established a link between network structure and macroscopic material properties.
Conclusions:
- Edge betweenness centrality is a robust indicator of yielding in soft glassy materials.
- Network science provides valuable insights into the mechanical failure of soft materials.
- This approach facilitates the rational design of soft materials with tailored properties.
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