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Record W4286587301 · doi:10.3390/hydrogen3030018

Methods to Improve the First Hydrogenation of the Vanadium-Rich BCC Alloy Ti16V60Cr24

2022· article· en· W4286587301 on OpenAlexafffund
Francia Ravalison, Eugen Rabkin, Jacques Huot

Bibliographic record

VenueHydrogen · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrogen storageAlloyVanadiumMaterials scienceEnthalpyTetragonal crystal systemZirconiumBall millHydrogenPhase (matter)MetallurgyAnalytical Chemistry (journal)CrystallographyCrystal structureChemistryThermodynamicsChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

In this paper we report the effect of three different preparation methods on the first hydrogenation of the vanadium-rich BCC alloy Ti16V60Cr24: one-time cold rolling, 5 min ball milling and addition of 4 wt.% of Zr. All samples were synthesized by arc melting. Without Zr addition the alloy was single phase, but when 4 wt.% Zr was added, a secondary zirconium-rich phase was present. However, X-ray diffraction patterns only showed a single-body-centred cubic phase before hydrogenation for all samples. The crystal structure of the fully hydrogenated samples was body-centred tetragonal. The highest hydrogen capacity (3.8 wt.%) was measured for the Zr-doped alloy. The ball-milled alloy also exhibited a high storage capacity and fast kinetics. However, the maximum hydrogen storage capacity slightly decreased after cold rolling. It was found that air exposure increases incubation time for the first hydrogenation. The incubation time was shortened by cold rolling which, however, reduced the hydrogen storage capacity. The Pressure-Composition isotherms of Ti16V60Cr24 + 4 wt.% Zr at 297, 303 and 323 K were determined. The determined enthalpy and entropy of hydrides formation were −41 ± 5 kJ/mol and −134 ± 14 J/mol/K, respectively.

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)0.003
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 score0.193
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.278
Teacher spread0.263 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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