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Record W2972250793 · doi:10.3390/en12183477

Effect of Hafnium Addition on the Hydrogenation Process of TiFe Alloy

2019· article· en· W2972250793 on OpenAlexaff
Volatiana Razafindramanana, Stéphane Gorsse, Jacques Huot, Jean‐Louis Bobet

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHafniumAlloyMaterials scienceHydrogenMicrostructureLaves phaseHydrogen storageElectron microprobeMetallurgyChemical engineeringIntermetallicChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The alloy TiFe has interesting hydrogen storage properties for practical applications: low cost, operation at room temperature, and good hydrogen capacity. However, the first hydrogenation is difficult and increases the cost of the alloy. In this work, we studied the effect of adding hafnium to TiFe in order to enhance the first hydrogenation process. TiFe + x Hf alloys, with x = 0, 4, 8, 12, and 16 wt.%, were synthesized by arc melting. The microstructure of the as-cast alloys was investigated by scanning electron microscopy and electron microprobe analysis. These alloys consisted of B2-TiFe, C14-Laves, and BCC (Body Centered Cubic) phases. A minimum of 8 wt.% of hafnium is required to obtain an enhancement of the first hydrogenation. In the first hydrogenation, the material reaches its maximal hydrogen capacity in less than two hours at room temperature and under 20 bars of hydrogen. Hafnium addition also had the effect of lowering the plateau pressure in the pressure-composition isotherm. It could be concluded that hafnium has a positive effect on the activation properties of TiFe.

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)0.001
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.005
GPT teacher head0.240
Teacher spread0.234 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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