2020
DOI: 10.1016/j.chemgeo.2020.119700
|View full text |Cite
|
Sign up to set email alerts
|

Multicomponent diffusion in a basaltic melt: Temperature dependence

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
1
0

Year Published

2020
2020
2025
2025

Publication Types

Select...
6
2

Relationship

1
7

Authors

Journals

citations
Cited by 8 publications
(7 citation statements)
references
References 43 publications
0
1
0
Order By: Relevance
“…While much progress has been made (Kress and Ghiorso, 1995;Liang, 2010;Guo and Zhang, 2016, 2018, multicomponent diffusion matrices are still only available for dry haplobasaltic and basaltic melts at a few temperatures. Effective binary treatment of MgO diffusion during olivine dissolution has been shown to work well experimentally (Chen and Zhang, 2008) and multicomponent diffusion calculations are consistent with effective binary calculations for MgO (Guo and Zhang, 2020). The advantage of multicomponent diffusion treatment is the ability to simultaneously model diffusion profiles of other major oxides, but the temperature dependence of the multicomponent diffusion matrices are less well constrained than that of the effective binary diffusivity of MgO.…”
Section: Effect Of Water Addition and Major Element Composition On Thmentioning
confidence: 62%
“…While much progress has been made (Kress and Ghiorso, 1995;Liang, 2010;Guo and Zhang, 2016, 2018, multicomponent diffusion matrices are still only available for dry haplobasaltic and basaltic melts at a few temperatures. Effective binary treatment of MgO diffusion during olivine dissolution has been shown to work well experimentally (Chen and Zhang, 2008) and multicomponent diffusion calculations are consistent with effective binary calculations for MgO (Guo and Zhang, 2020). The advantage of multicomponent diffusion treatment is the ability to simultaneously model diffusion profiles of other major oxides, but the temperature dependence of the multicomponent diffusion matrices are less well constrained than that of the effective binary diffusivity of MgO.…”
Section: Effect Of Water Addition and Major Element Composition On Thmentioning
confidence: 62%
“…We compare the results of our mixing model with their experiments after 300 s. The experiments are performed at 1473 K, 0.5 GPa, and 2 wt% of water. The high-pressure conditions employed here, dictated by the limitations of the experimental apparatus, are not expected to affect the mixing process significantly, since the diffusion coefficients are poorly dependent on pressure (Deegan et al, 2010;Guo and Zhang, 2020). The coefficients of the diffusion matrix (D in Equation 7) are defined according to Guo and Zhang (2020).…”
Section: Chemical Mixingmentioning
confidence: 99%
“…The high-pressure conditions employed here, dictated by the limitations of the experimental apparatus, are not expected to affect the mixing process significantly, since the diffusion coefficients are poorly dependent on pressure (Deegan et al, 2010;Guo and Zhang, 2020). The coefficients of the diffusion matrix (D in Equation 7) are defined according to Guo and Zhang (2020). The authors obtained a temperature-dependent diffusion matrix D for an 8-components (SiO 2 , TiO 2 , Al 2 O 3 , FeO, MgO, CaO, Na 2 O, K 2 O) basaltic melt by simultaneously fitting diffusion profiles of couple experiments at different temperatures (1260, 1500 • C) and pressures (0.5-1 Gpa), by considering SiO 2 as the solvent component.…”
Section: Chemical Mixingmentioning
confidence: 99%
See 1 more Smart Citation
“…The diffusion-driven model of Guo and Zhang (2020) applied multicomponent diffusion to the modeling of olivine and anorthite dissolution in basaltic melts as well as to melt mixing (which happened in the Bushveld igneous complex during its formation). The authors noted that the data do not permit an accurate treatment of the temperature dependence of the diffusion matrix in basaltic system.…”
Section: Kinetic Model Of Guo and Zhang (2020)mentioning
confidence: 99%