A Comparison of Surface Tension, Viscosity, and Density of Sn and Sn–Ag Alloys Using Different Measurement Techniques
Bibliographic record
Abstract
This work is aimed at comparing several methods for the measurement of physical properties for molten Sn and Sn–Ag alloys, namely, surface tension, density, and viscosity. The method used for viscosity in this work is the modified capillary method. For surface tension and density, the data used for comparison were previously measured using the maximum bubble pressure method and the dilatometer technique, respectively, for four Sn–Ag alloys having (3.8, 32, 55, and 68) at% Ag. The results are compared with those obtained using a new method based on a fluid draining from a crucible under the influence of gravity, designated the Roach–Henein (RH) method. This new method enables the determination of these three physical properties in one set of measurements. Liquid Sn was used as well as two liquid Sn–Ag alloys having (3.8 and 34.6) at% Ag with the RH method. It was determined that the RH method may be used to simultaneously obtain surface tension, viscosity, and density and that the errors associated with these measurements were similar to those obtained using traditional and separate techniques. Comparisons of the measured viscosity and surface tension to those predicted using thermodynamic models will also be presented. Finally a comparison of mixing model predictions with the experimentally measured alloy surface tension and viscosity is also presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".