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Record W2890849917 · doi:10.14447/jnmes.v19i2.338

Accelerated Hydrolysis of Solid-state NaBH4/Al System by Co2B Milled with Li for Hydrogen Generation

2016· article· en· W2890849917 on OpenAlexvenueno aff
Jiasong Chang, Wenlong Song, Ting Li, Jindan Chen, Hanmei Wu, Chengming Li, Meiqiang Fan

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2016
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsHydrolysisHydrogen productionCatalysisHydrogenYield (engineering)Composite numberChemical engineeringMaterials scienceChemistryMetallurgyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Co2B catalyst was milled with Al and Li to form Al-Li-Co2B composite, and the hydrogen generation performance of Al-Li-Co2B/NaBH4 system was investigated in this study. 100% hydrogen yield was reached, and high hydrogen generation rate was regulated by optimizing the composition design and preparation technology. The improvement was attributed to the synergetic effect of Co2B and Li in the preparation process, whereas a large specific surface area was obtained with the increase in Li content, Co2B, and milling time. In addition, the catalytic activity of Co2B and LiOH from Li hydrolysis was heightened for the hydrolysis of the Al/NaBH4 system because of the addition of Al(OH)3, LiAl2(OH)7.xH2O, and NaBO2 in the hydrolysis process.

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)0.002
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 score0.030
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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