Experimental Investigation on the Transport of Sulfide Driven by Melt-rock Reaction in Partially Molten Peridotite
Bibliographic record
Abstract
Extraction of sulfides from the partially molten mantle is vital to elucidate the cycling of metal and sulfur elements between different geochemical circles but has not been investigated systematically. Using laboratory experiments and theoretical calculations, this study documents systematical variations in lithologies and compositions of silicate minerals and melts, which are approximately consistent with the results of the thermodynamically-constrained model. During a melt-peridotite reaction, the dissolution of olivine and precipitation of new orthopyroxene generate an orthopyroxene-rich layer between the melt source and peridotite. With increasing reaction degree, more melt is infiltrated into and reacts with upper peridotite, which potentially enhances the concomitant upward transport of dense sulfide droplets. Theoretical analyses suggest an energetically focused melt flow with a high velocity (~ 170.9 μm/h) around sulfide droplets through the pore throat. In this energic melt flow, we, for the first time, observed the mechanical coalescence of sulfide droplets, and the associated drag force was likely driving upward entrainment of fine μm-scale sulfide. For coarse sulfide droplets whose sizes are larger than the pore throat in the peridotite, their entrainment through narrow constrictions in crystal framework seems to be physically possible only when high-degree melt-peridotite reaction drives high porosity of peridotite and channelized melt flows with extremely high velocity. Hence, the melt-rock reaction could drive and enhance upward entrainment of μm- to mm-scale sulfide in the partially molten mantle, potentially contributing to the fertilization of the sub-continental lithospheric mantle and the endowment of metal-bearing sulfide for the formation of magmatic sulfide deposits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".