An Experimental Study of Trace Element Partitioning between Peridotite Minerals and Alkaline Basaltic Melts at 1250°C and 1 GPa: Crystal and Melt Composition Impacts on Partition Coefficients
Bibliographic record
Abstract
Abstract Peridotite–magma interaction is important in establishing magma pathways through the mantle and in metasomatism of the lithospheric mantle. Reactions that consume orthopyroxene and produce olivine and clinopyroxene are of particular interest because these reactions should lead to a redistribution of trace elements between the solid and melt phases at equilibrium. This study examines interaction of a silica-undersaturated alkaline basalt (basanite) with a range of peridotite compositions from dunite, through harzburgite to wehrlite at 1250°C and 1 GPa. Our experiments used the natural concentration of trace elements in the starting materials which allowed us to measure mineral—olivine partition coefficients for Rb, Ca, Co, Sr, Sc, Ct, Y, Ti, V and Zr. For orthopyroxene—and clinopyroxene—melt we additionally measured partitioning of Cs, Ba, all rare earth elements (REE; except Pm), Hf, Th, U, Nb and Ta. We show that there are subtle variations in the partition coefficients, particularly of the REEs that are related to the bulk composition of the system. We also show that with the exception of cations that can have multiple valence states, e.g. vanadium, the lattice strain model and in particular the double fit routine gives excellent agreement between the calculated and experimentally determined partition coefficients. The double fit model allows us to examine the effect of mineral composition on partitioning such that we can show preference of trace elements for the M1 and M2 sites in the pyroxenes. Although our results are similar to those of previous studies, there are two main differences: first we have a complete set of partition coefficients for every trace element that is measurable by LA-ICPMS in our starting material, where previous studies may be missing one or more elements in particular one or more of the middle REE in the pyroxenes Second, we show that although partition coefficients for trace elements in orthopyroxene are comparable between this and previous studies, the REE in clinopyroxene are typically a factor of 2–3 lower in this study. We also note that are correlations between partition coefficient and the composition of olivine, orthopyroxene, clinopyroxene and glass (melt). The relation of partitioning to melt composition suggests that some further development of the lattice strain model is needed. Finally, we show that there is agreement between our measured partition coefficients and those predicted from parameterized models of clinopyroxene–melt partitioning, however, there are unresolved differences that may result from differences in the substitution mechanisms of trace elements in M1 vs. M2 sites in clinopyroxene that are in part related to the composition of the coexisting melt.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
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