Thermophysical Property Measurements of Refractory Oxide Melts With an Electrostatic Levitation Furnace in the International Space Station
Bibliographic record
Abstract
Due to their high melting temperatures and the risk of contamination from the crucibles, molten oxides which melting temperatures are above 2000 °C can hardly be processed using conventional methods. This explains that their thermophysical properties are very scarce. Containerless methods with gas flows have been developed and several thermophysical properties such as density, surface tension, and viscosity have been reported. However, the gas flow has detrimental side effects such as deformation of the sample and induction of internal flows in the molten sample, which affect the accuracy of the measurements. The electrostatic levitation furnace onboard the International Space Station (ISS-ELF), which utilizes the Coulomb force to levitate and melt samples in microgravity, has several advantages for thermophysical property measurements of refractory oxide melts. Levitation without a gas flow coupled to a reduced gravity environment minimizes the required levitation (positioning) force and reduces the deformation as well as the internal flow. This report briefly introduces the ISS-ELF facility and the thermophysical property measurement methods. The measured density, surface tension, and viscosity of molten Al 2 O 3 are then presented and compared with the ones obtained by other methods. Finally, the measured data of refractory oxides whose melting temperatures are above 2,400 °C are summarized.
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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 | 0.001 | 0.001 |
| 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".