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A Dynamically Correlated Network Model for the Collective Dynamics in Glass-Forming Molecular Liquids and Polymers
Takashi Sasaki1, Yuya Tsuzuki1, Tatsuki Nakane1
1Department of Materials Science and Engineering, University of Fukui, Fukui 9108507, Japan.
Polymers
|October 13, 2021
Summary
A new dynamically correlated network (DCN) model explains the complex dynamics of supercooled liquids. This network structure, with interpenetrating networks, accurately predicts viscous slowdown and matches experimental data.
Area of Science:
- Soft matter physics
- Condensed matter physics
- Physical chemistry
Background:
- Non-Arrhenius behavior in glass-forming liquids remains a significant mystery.
- Understanding the dynamics of supercooled liquids is crucial for material science.
Purpose of the Study:
- To propose a Dynamically Correlated Network (DCN) model to explain the growing behavior of dynamically correlated regions during cooling.
- To elucidate the mechanisms behind the viscous slowdown observed in supercooled liquids.
Main Methods:
- Development of a novel Dynamically Correlated Network (DCN) model.
- Utilizing Monte Carlo simulations to generate DCNs at various temperatures.
- Analyzing fractal dimensions and size distributions of the DCNs.
Main Results:
- Monte Carlo simulations demonstrated increasing fractal dimensions and broader size distributions of DCNs with decreasing temperature.
- Segmental relaxation time was successfully modeled using a power law dependent on DCN size.
- The DCN model's predictions align with experimental observations in molecular and polymeric liquids.
Conclusions:
- The proposed DCN model provides a robust framework for understanding the non-Arrhenius dynamics of supercooled liquids.
- The network structure and cooperative motion are key to explaining viscous slowdown.
- The model's consistency with experimental data validates its applicability to diverse liquid systems.
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