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Record W4410524317 · doi:10.1016/j.jacomc.2025.100089

Thermal stability and hydrogen storage properties of Hf0.75Ti0.25NbVZr and TiNbVZr high entropy alloys

2025· article· en· W4410524317 on OpenAlexaff
Mourad Moussa, Christophe Cona, Jacques Huot, Jean‐Louis Bobet

Bibliographic record

VenueJournal of Alloys and Compounds Communications · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersConseil Régional AquitaineAgence Nationale de la Recherche
KeywordsHydrogen storageThermal stabilityMaterials scienceThermodynamicsEntropy (arrow of time)MetallurgyPhysicsEngineeringChemical engineering

Abstract

fetched live from OpenAlex

This study examines the thermal stability and hydrogen storage performance of Hf 0.75 Ti 0.25 NbVZr and TiNbVZr high-entropy alloys (HEAs). Heat treatments were conducted at 1300°C for 24 and 48 hours and at 600°C for 1 and 2 months. X-ray diffraction (XRD) analysis revealed that Hf 0.75 Ti 0.25 NbVZr transitioned from a mixture of BCC and C15 Laves phases to a single BCC phase at 1300°C. At 600°C, a combination of BCC, C14 Laves, and HCP phases was detected, with phase compositions remaining stable over time. Hydrogen absorption tests showed that heat-treated samples required activation at 250°C. After 2 months at 600°C, Hf 0.75 Ti 0.25 NbVZr alloy had a hydrogen uptake of 0.95 wt.% while TiNbVZr treated at 1300°C for 24 hours absorbed 1.10 wt.% of hydrogen. In contrast, the as-cast alloys absorbed 2.05 wt.% and 2.7 wt.%, respectively, at room temperature. Hydrogenation induced phase transformations, notably causing significant expansion in the C14 Laves phase. The highest hydrogen absorption was observed in the as-synthesized arc-melted alloys, which outperformed heat-treated samples in storage capacity. These findings demonstrate that heat treatment lowers hydrogen storage efficiency due to phase transformations and reduced BCC phase content.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.594

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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

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