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A Method for the Production of Titanium-Tantalum Binary Alloys Using the Metalysis-FFC Process

2020· article· en· W3093441657 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMATEC Web of Conferences · 2020
Typearticle
Languageen
FieldChemical Engineering
TopicMolten salt chemistry and electrochemical processes
Canadian institutionsDiscovery Centre
FundersEngineering and Physical Sciences Research Council
KeywordsTantalumMaterials scienceTitaniumMetallurgyTitanium alloyAlloyChemical engineering

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.299
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it