4.7 Article

First principles viscosity and derived models for MgO-SiO2 melt system at high temperature

期刊

GEOPHYSICAL RESEARCH LETTERS
卷 40, 期 1, 页码 94-99

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1029/2012GL054372

关键词

-

资金

  1. National Science Foundation [EAR-1118869]
  2. UK National Environmental Research Council [NE/ F01787/1]
  3. Directorate For Geosciences
  4. Division Of Earth Sciences [1118869] Funding Source: National Science Foundation
  5. Natural Environment Research Council [NE/I010734/1, hpc010001, NE/F017871/1] Funding Source: researchfish
  6. NERC [hpc010001, NE/F017871/1, NE/I010734/1] Funding Source: UKRI

向作者/读者索取更多资源

The viscosity of silicate liquids at high temperature is crucial to our understanding of chemical and thermal evolution of the Earth since its early stages. First-principles molecular dynamics simulations of seven liquids across the MgO-SiO2 binary show that the viscosity varies by several orders of magnitudes with temperature and composition. Our results follow a compensation law: on heating, the viscosity of all compositions approaches a uniform value at 5000 K, above which pure silica becomes the least viscous liquid. Viscosity depends strongly on composition (fourth power), implying a strong nonlinear dependence of the configurational entropy on composition. Using the simulation results, we derive and evaluate different types (Arrhenius and non-Arrhenius) of models for accurate description of the viscosity-temperature-composition relationship. Our results span the thermal regime expected in a magma ocean, and indicate that melt migration is important for understanding the generation and preservation of melts from frictional heating at very fast slip in impact processes. Citation: Karki B. B., J. Zhang, and L. Stixrude (2013), First principles viscosity and derived models for MgO-SiO2 melt system at high temperature, Geophys. Res. Lett., 40, 94-99, doi: 10.1029/2012GL054372.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据