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In situ <i>μ</i><sup>+</sup>SR measurements on the hydrogen desorption reaction of magnesium hydride

2014· article· en· W2086040648 on OpenAlexafffund
Izumi Umegaki, Hiroshi Nozaki, Masaru Harada, Yuki Higuchi, Tatsuo Noritake, M. Matsumoto, S-i Towata, Eduardo J. Ansaldo, J. H. Brewer, A. Koda, Yasuhiro Miyake, Jun Sugiyama

Bibliographic record

VenueJournal of Physics Conference Series · 2014
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of British ColumbiaTRIUMF
FundersJapan Society for the Promotion of ScienceTRIUMF
KeywordsDesorptionOscillation (cell signaling)HydrogenDiffusionMagnesium hydrideAnalytical Chemistry (journal)MagnesiumHydrideMagnetic fieldChemistryActivation energyAtomic physicsMaterials scienceNuclear magnetic resonancePhysical chemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

In order to clarify the reason why the hydrogen desorption temperature ( T d ) of MgH 2 is lowered by milling, we have studied the change in a local nuclear magnetic field with temperature by means of μ + SR. We have found a very clear oscillation in the ZF-spectrum at 2 K for the "milled" and "milled with Nb 2 O 5 " samples, while such oscillation is weaker for the "as prepared" MgH 2 . It was also found that the oscillation signal is stable up to 250 K and is assigned mainly due to the formation of a H- μ -H system. At temperatures above ambient T , we also found that the ZF- μ + SR spectrum exhibits a static Kubo-Toyabe behavior due to the nuclear magnetic field of 1 H. Furthermore, it was clarified that rapid H diffusion starts well below T d only in the milled samples, leading to the conclusion that the consequent enhanced diffusion rate in MgH 2 is essential to accelerate the desorption reaction and to decrease T d .

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.000
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 head0.041
GPT teacher head0.251
Teacher spread0.210 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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