Challenges of Handling, Processing, and Studying Liquid and Supercooled Materials at Temperatures above 3000 K with Electrostatic Levitation
Bibliographic record
Abstract
Over the last 20 years, great progress has been made in techniques for electrostatic levitation, with innovations such as containerless thermophysical property measurements and combination of levitators with synchrotron radiation source and neutron beams, to name but a few. This review focuses on the technological developments necessary for handling materials whose melting temperatures are above 3000 K. Although the original electrostatic levitator designed by Rhim et al. allowed the handling, processing, and study of most metals with melting points below 2500 K, several issues appeared, in addition to the risk of contamination, when metals such as Os, Re, and W were processed. This paper describes the procedures and the innovations that made successful levitation and the study of refractory metals at extreme temperatures (>3000 K) possible; namely, sample handling, electrode design (shape and material), levitation initiation, laser heating configuration, and UV range imaging. Typical results are also presented, putting emphasis on the measurements of density, surface tension, and viscosity of refractory materials in their liquid and supercooled phases. The data obtained are exemplified by tungsten, which has the highest melting temperature among metals (and is second only to carbon in the periodic table), rhenium and osmium. The remaining technical difficulties such as temperature measurement and evaporation are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".