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Record W2141969363 · doi:10.3390/en81112330

Effect of Magnesium Fluoride on Hydrogenation Properties of Magnesium Hydride

2015· article· en· W2141969363 on OpenAlexaff
Pragya Jain, Viney Dixit, Ankur Jain, O. N. Srivastava, Jacques Huot

Bibliographic record

VenueEnergies · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHydrogen storageMagnesium hydrideMagnesiumHydrideDesorptionCatalysisBall millMaterials scienceHydrogenSorptionInorganic chemistryFluorideAbsorption (acoustics)MetalChemical engineeringChemistryAdsorptionMetallurgyPhysical chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

A cost effective catalyst is of great importance for consideration of MgH2 as potential hydrogen storage material. In this regard, we investigated the catalytic role of alkaline metal fluoride on the hydrogen storage behavior of MgH2. Samples were synthesized by admixing 5 mol % MgF2 into MgH2 powder using planetary ball mill. Hydrogenation measurements made at 335 °C showed that in comparison to only 70% absorption by pure MgH2, catalyzed material absorbed 92% of theoretical capacity in less than 20 min and desorbed completely in almost the same time. Sorption studies done at lower temperatures revealed that complete absorption at temperature as low as 145 °C is possible. This is due to uniform distribution of MgF2 nano particles within the MgH2 powder. X-ray diffraction patterns also showed the presence of stable MgF2 phase that does not decompose upon hydrogen absorption-desorption. Cyclic measurements done at 310 °C showed negligible loss in the overall storage capacity with cycling. These results reveal that the presence of the chemically inert and stable MgF2 phase is responsible for good reversible characteristic and improved kinetics.

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 score0.003
Threshold uncertainty score0.546

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 head0.018
GPT teacher head0.240
Teacher spread0.221 · 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

Citations28
Published2015
Admission routes1
Has abstractyes

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