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Record W2766449972 · doi:10.3390/en10111741

The Effects of Additives on the Dehydrogenation of Amorphous Manganese Borohydride and Its Crystalline Form after Solvent Filtration/Extraction

2017· article· en· W2766449972 on OpenAlexafffund
Robert A. Varin, Deepak Kini Mattar, Marek Polański, Amirreza Shirani Bidabadi, Leszek Stobiński

Bibliographic record

VenueEnergies · 2017
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrideDehydrogenationDifferential scanning calorimetryBorohydrideThermal decompositionAmorphous solidInorganic chemistryMaterials scienceManganeseExtraction (chemistry)ChemistryNuclear chemistryOrganic chemistryMetalCatalysis

Abstract

fetched live from OpenAlex

A non-stoichiometric, amorphous a-Mn(BH4)(2x) hydride, accompanied by a NaCl-type salt, was mechanochemically synthesized from the additive-free mixture of (2NaBH4 + MnCl2), as well as from the mixtures containing the additives of ultrafine filamentary carbonyl nickel (Ni), graphene, and LiNH2. It is shown that both graphene and LiNH2 suppressed the release of B2H6 during thermal gas desorption, with the LiNH2 additive being the most effective suppressor of B2H6. During solvent filtration and extraction of additive-free, as well as additive-bearing, (Ni and graphene) samples from diethyl ether (Et2O), the amorphous a-Mn(BH4)(2x) hydride transformed into a crystalline c-Mn(BH4)2 hydride, exhibiting a microstructure containing nanosized crystallites (grains). In contrast, the LiNH2 additive most likely suppressed the formation of a crystalline c-Mn(BH4)2 hydride during solvent filtration/extraction. In a differential scanning calorimeter (DSC), the thermal decomposition peaks of both amorphous a-Mn(BH4)(2x) and crystalline c-Mn(BH4)2 were endothermic for the additive-free samples, as well as the samples with added graphene and Ni. The samples with LiNH2 exhibited an exothermic DSC decomposition peak.

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)0.000
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 score0.261

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.245
Teacher spread0.236 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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