Measurement of the Soret coefficients for a ternary hydrocarbon mixture in low gravity environment
Bibliographic record
Abstract
While the Soret coefficients of binary mixtures have been widely measured in the past, here we report the first measurement of the Soret coefficient of a ternary mixture in a low gravity environment on board the International Space Station. The sample was contained in a 10 mm × 10 mm × 5 mm (w, l, h) cell and was monitored by means of a Mach-Zehnder interferometer at two wavelengths. The analyzed sample was a mixture of tetrahydronaphthalene, isobutylbenzene, and dodecane at the weight fraction of 0.1∕0.8∕0.1. While the lateral walls of the cell did not possess complete thermal isolation, the separation of the components in the central region of the cavity was comparable to purely diffusive behavior. The same experimental parameters have been monitored in Run7 and Run12 of the Selectable Optical Diagnostics Instrument-Diffusion and Soret Coefficient experiment in order to verify the accuracy of the setup. The similarity of the results demonstrates the repeatability of thermodiffusion experiments in a microgravity environment. There was nearly equal separation of the tetrahydronaphthalene and isobutylbenzene components in opposite directions, while dodecane experienced a weak separation in the same direction as isobutylbenzene. Finally, Fourier image processing and calculations of the transient separation of the components were used to analyze the heat transfer in the system and to measure the Soret coefficients for this ternary mixture. The successful measurements shown in this work can serve as the standard for ground experiments and for numerical modeling of hydrocarbon mixtures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".