A Method for the Production of Titanium-Tantalum Binary Alloys Using the Metalysis-FFC Process
Bibliographic record
Abstract
Titanium-tantalum alloys have historically been difficult to produce using conventional techniques due to the large difference in melting point and poor interdiffusivity rates of the two elements, leading to large levels of segregation. In this work we have investigated using the Metalysis-FFC process to overcome this problem. The Metalysis-FFC process is an electrochemical process where metal oxides are reduced by acting as a cathode in an electrolytic cell combined typically with a graphite anode and molten calcium chloride electrolyte. Previous work has proven that the process works when applied to mixed oxides to produce alloys. The reduction process is performed in the solid state and so negates the problems associated with traditional melting practices such as segregation and evaporation losses. In this work we have been able to successfully produce a range of Ti-Ta alloy powders from pure titanium to pure tantalum in 10 wt% increments using the Metalysis-FFC process. Moreover, energy dispersive spectroscopy analysis of the powders suggests uniform distribution of titanium and tantalum within all compositional ranges, suggesting the Metalysis-FFC process has the potential to be a transformative manufacturing methodology for producing titanium-tantalum alloys.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".