New Data to the SURDAT-Database of Modeled and Experimental Physical Properties of Lead-Free Solder Alloys
Bibliographic record
Abstract
Experimental studies of surface tension and density by the maximum bubble pressure method and dilatometric technique were carried out and compiled with almost over ten years results for liquid pure components, binary and multicomponent alloys in order to create the SURDAT database [1] of the Pb-free soldering materials (available free of charge on website http://www.imim.pl). The implementation of experimental contact angles, wettability force, wettability time, interfacial tension and differential thermal analysis data into the SURDAT database has been initiated in the last 2 years as a result of cooperation with industrial institutes. Additionally, a new computer software for calculation of viscosity based on the models proposed by Moelwin-Hughes [2], Iida-Ueda-Morita [3,4], Seetharaman-Du Sichen [5], Kozlov-Romanov-Petrov [6] and Kaptay [7] was developed. The modification of Moelwyn-Hughes model was proposed for binary alloys, with positive deviations from the ideal behavior by the introduction of the correction factor kH = f (Hmax) which depends on the maximal value of the enthalpy of mixing Hmax of liquid alloys. The calculated viscosity of some binary systems showed satisfactory agreement with those from experiments. The cooperation with Alberta University in Edmonton was initiated on this matter and the experimental data of viscosity obtained with the capillary and dynamic flow method will be accessible in SURDAT database in future. All the old and new data will be available on the website of Institute of Metallurgy and Materials Science in a new electronic and book edition of the SURDAT database being in preparation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.020 |
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".