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Record W1852105190 · doi:10.3390/met5042021

Effect of Cold Rolling on the Hydrogen Desorption Behavior of Binary Metal Hydride Powders under Microwave Irradiation

2015· article· en· W1852105190 on OpenAlexaff
Ivaldete da Silva Dupim, Sydney Ferreira Santos, Jacques Huot

Bibliographic record

VenueMetals · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMaterials scienceMicrowaveDesorptionHydrideHydrogenDecompositionIrradiationDiffractionHydrogen storageMetalAnalytical Chemistry (journal)MetallurgyChemistryPhysical chemistryAlloyChromatographyNuclear physicsOptics

Abstract

fetched live from OpenAlex

In this paper we report that cold rolling could drastically improve hydrogen desorption kinetics under microwave irradiation. Samples of metal hydride powders (TiH2, ZrH2, and MgH2) in as-received conditions and after cold rolling were microwave irradiated in a vacuum using a simple experimental setup. After irradiation, the samples were characterized by X-ray diffraction in other to evaluate the effectiveness of microwave heating. The diffraction patterns indicated that only MgH2 could be fully decomposed (dehydrided) in the as received state. TiH2 was only partially decomposed while no decomposition was observed for ZrH2. However, cold rolling the hydride powders prior to microwave heating led to a significant improvement of hydride decomposition, resulting in the complete dehydriding of TiH2 and extensive dehydriding of ZrH2. These results clearly indicated the positive effects of cold rolling on the microwave assisted desorption of the investigated binary hydrides.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.034
GPT teacher head0.275
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2015
Admission routes1
Has abstractyes

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