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Record W4296625577 · doi:10.3390/hydrogen3040023

First Hydrogenation of TiFe with Addition of 20 wt.% Ti

2022· article· en· W4296625577 on OpenAlexaff
Elena Ulate-Kolitsky, Bernard Tougas, Jacques Huot

Bibliographic record

VenueHydrogen · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsIngotVacuum induction meltingAlloyInduction furnaceMicrostructureMaterials sciencePhase (matter)MetallurgyKineticsStoichiometryHydrogenAnalytical Chemistry (journal)ChemistryPhysical chemistryChromatography

Abstract

fetched live from OpenAlex

In this paper, we report the first hydrogenation (activation) of a 1.2Ti-0.8Fe alloy synthesized by induction melting (9 kg ingot). The alloy presented a three-phase structure composed of a main TiFe phase, a secondary Ti2Fe phase and a Ti-rich BCC phase. The alloy required cold rolling to achieve activation at room temperature. However, it did so with good kinetics, reaching saturation (2.6 wt.% H) in about 6 h. After activation, the phases identified were TiFe, Ti2FeHx and an FCC phase. The Ti2FeHx and FCC are the stable hydrides formed by the secondary Ti2Fe and BCC phases, respectively. The stoichiometry of the Ti2FeHx was calculated to be between x = 3.2–4.75. As the microstructure obtained by an industrial-scale synthesis method (induction melting) may be different than the one obtained by laboratory-scale method (arc melting), a small 3 g sample of Ti1.2Fe0.8 was synthesized by arc melting. The lab-scale sample activated (2 wt.% H in ~12 h) without the need for cold rolling. The phases identified for the lab-scale sample matched those found for the induction-melted sample. The phase fractions differed between the samples; the lab-scale sample presented a lower abundance and a finer distribution of the secondary phases. This explains the difference in the kinetics and H capacity. Based on these results it can be concluded that the alloy of composition, 1.2Ti-0.8Fe, can absorb hydrogen without the need for a heat treatment, and that finer microstructures have a strong influence on the activation kinetics regardless of the secondary phases’ phase fractions.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
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 score0.018
Threshold uncertainty score0.994

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)0.0070.000

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2022
Admission routes1
Has abstractyes

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