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Record W2078154426 · doi:10.1021/nl010005+

Sensitivity of Nanocrystalline MgH<sub>2</sub>−V Hydride Composite to the Carbon Monoxide during a Long-Term Cycling

2001· article· en· W2078154426 on OpenAlexaff
Zahir Dehouche, J. Goyette, Tungadri Bose, Jacques Huot, Robert Schulz

Bibliographic record

VenueNano Letters · 2001
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-RivièresHydro-Québec
Fundersnot available
KeywordsNanocrystalline materialHydrogenCarbon monoxideHydrideMaterials scienceDesorptionHydrogen storageComposite numberNanocompositeAnalytical Chemistry (journal)HysteresisTemperature cyclingChemical engineeringChemistryThermodynamicsComposite materialPhysical chemistryNanotechnologyAdsorptionCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

In this work, we present the results of the investigation of the effect of prolonged cycling on the hydriding/dehydriding properties and on the structure of nanocrystalline MgH 2 −V composite under hydrogen containing 110 ppm of carbon monoxide. The hydrogen charge and discharge kinetics of the nanocomposite hydride were examined at 300 °C using up to 1000 cycles. Pressure composition isotherm measurements at 300 °C were also carried out. The measured kinetics curves show a significant and systematic slowing down of the absorption and desorption rates and, consequently, a decreased amplitude and delayed heat transfer signals in the reaction temperature curves. Associated with this effect, we observe an enlargement of the pressure hysteresis and a gradual enhancement of the plateau slopes. The results also show some loss in the hydrogen storage capacity of the composite during cycling. The X-ray crystal structure analysis after 1000 cycles reveals that the degradation of the hydriding and dehydriding properties of the sample is related to the crystal growth and the formation of MgO x phases.

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 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 score0.872

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)0.0000.000

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations29
Published2001
Admission routes1
Has abstractyes

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