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Record W2783944040 · doi:10.14447/jnmes.v18i4.353

Preparation of an Active Ni2B/SBA-15 Catalyst to Improve NaBH4 Hydrolysis for Hydrogen Generation

2015· article· en· W2783944040 on OpenAlexvenueno aff
Fei Wu, Ting Li, Jindan Chen, Xiu Ru Yang, Cheng Ming Li, Mei Qiang Fan, Kang Ying Shu

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2015
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisAmorphous solidMesoporous materialSinteringHydrolysisChemical engineeringMaterials scienceMesoporous silicaNanostructureNanoparticleHydrogen productionChemistryNanotechnologyOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Ni2B nanoparticle distributed on ordered SBA-15 mesoporous silica was prepared in situ and its catalytic activity for NaBH4 hydrolysis was investigated in the presented paper. The problem of Ni2B aggregation is resolved and the catalytic activity of Ni2B is also improved due to the effect of SBA-15. The catalytic activity increases with Ni2B/SBA-15 weight ratio increased from 1:4 to 2:1 and the catalyst with Ni2B/SBA-15 weight ratio of 2:1 has high catalytic activity close to that of Ni2B. The sintered experiment shows that the catalytic activity comes from amorphous Ni2B, not crystalline Ni2B. High sintering temperature leads to the conversion from amorphous Ni2B to crystalline Ni2B and damages the nanostructure of amorphous Ni2B framework.

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)0.001
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 score0.008
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.311
Teacher spread0.284 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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