The heat flux through the cores of small terrestrial planetary bodies from electrical resistivity measurements of liquid Fe-S-Si at high pressures
Bibliographic record
Abstract
Dynamo action in liquid Fe planetary cores varies according to alloyed light elements such as S and Si. This study experimentally constrains the thermal conductivity of Fe-S-Si alloys at planetary core conditions, which may be used in combination with thermal evolution models to find the total thermal convective force in the core. A sample of Fe alloy with 16wt%S and 2wt%Si was chosen as a predicted composition of the core of Asteroid 4 Vesta, based on studies of HED meteorites [1-2]. This is near the miscibility limit of S and Si in liquid Fe [3].Experiments were performed at 2-5 GPa in a 1000-ton cubic anvil press and at up to 9 GPa in a 3000-ton multi-anvil press. Temperatures as high as 2100 K into the melt of Fe-S-Si. The electrical resistivity of the liquid Fe-S-Si alloy was measured in situ; to find the electronic component of the thermal conductivity, the Wiedemann-Franz Law was used. To confirm the sample composition and homogenization, electron microprobe analysis was performed on samples recovered from various stages of melting, yielding compositional maps of Fe, S, and Si across each sample.The individual effects of S and Si on the electrical resistivity of liquid Fe are seen in the results for the conditions of small planetary cores. Fe-16wt%S-2wt%Si has an electrical resistivity of 300-450 µΩ·cm at the complete melt in the pressure range of 2-7 GPa. Pure Fe at the same pressures is at most half this value [4], meaning that a moderate amount of S greatly decreases thermal conductivity in the liquid core. These results may be used to find the adiabatic heat flux at the top of the core of a given planetary body, with direct application to the formation of a magnetic dynamo in the liquid cores of objects such as Vesta, Ganymede, and Mars.References:[1] Steenstra, E.S., Dankers, D., Berndt, J., Klemme, S., Matveev, S., van Westrenen, W., 2019. Icarus, v. 317, p. 669-681.[2] Pringle, E.A., Savage, P.S., Badro, J., Barrat, J.-A., Moynier, F., 2013. Earth Planet. Sci. Lett., v. 373, p. 75-82.[3] Chabot, N.L., Wollack, E.A., Klima, R.L., Minitti, M.E., 2014. Earth Planet. Sci. Lett., v. 390, p. 199-208.[4] Yong, W., Secco, R.A., Littleton, J.A.H., Silber, R.E., 2019. Geophys. Res. Lett., v. 46, p. 11065-11070.
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) | 0.001 | 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".