对超冷液体的非添加式随机模型的概念验证
A C P Rosa1, E Brito1, W S Santana1
1Centro de Ciências Exatas e das Tecnologias, <a href="https://ror.org/03raeyn57">Universidade Federal do Oeste da Bahia</a>, Rua Bertioga, 892 Morada Nobre I, 47810-059 Barreiras, Bahia, Brazil.
Physical review. E
|August 20, 2024
概括
非添加式随机模型 (NSM) 有效地模拟玻璃形成液体的粘度,提供对超冷液体行为和脆弱性的物理洞察. 它在匹配实验数据方面优于其他模型.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学是一种材料科学.
- 物理化学 物理化学
背景情况:
- 超冷的液体表现出复杂的动态,偏离了阿雷尼乌斯的行为.
- 像VFT,AM和MYEGA这样的既定模型描述了粘度-温度关系,成功率不同.
- 了解玻璃成型系统中的扩散现象对于材料设计至关重要.
研究的目的:
- 评估用于描述玻璃成型液体粘度的非添加式随机模型 (NSM).
- 将NSM的装配性能与已建立的粘度模型进行比较.
- 评估NSM能够描述激活能量和液体脆弱性的能力.
主要方法:
- 将NSM粘度方程与实验温度依赖的粘度数据相匹配.
- 分析了25个不同的玻璃形成液体数据集.
- 将NSM的适合参数与来自Vogel-Fulcher-Tammann (VFT),Avramov-Milchev (AM) 和Mauro-Yue-Ellison-Gupta-Allan (MYEGA) 模型的参数进行比较.
主要成果:
- 对于实验粘度数据,NSM提供了一个有效的适配方程.
- NSM参数与脆弱的玻璃形成液体的激活能量相关.
- 该模型表现出与VFT,AM和MYEGA相当或更高的性能.
结论:
- 非添加式随机模型 (NSM) 提供了玻璃成型系统中扩散现象的物理连贯解释.
- NSM是模拟粘度和评估超冷液体脆弱性的可靠工具.
- 该模型的非指数关系为非Arrhenius行为提供了更深入的见解.
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