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Record W2588310759 · doi:10.1002/cjce.22814

Hydrogenation of <i>m</i>‐chloronitrobenzene over amorphous Ni‐B/CNTs catalysts: Promoting effect of CNTs confinement on the catalytic performance

2017· article· en· W2588310759 on OpenAlexvenueno aff
Li Feng, Jinrong Liang, Keliang Wang, Bo Cao, Wenxi Zhu, Hua Song

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsnot available
Fundersnot available
KeywordsCatalysisMaterials scienceCarbon nanotubeAmorphous solidChemical engineeringThermal stabilityAlloyHydrogenAmorphous carbonAmorphous metalNanotechnologyComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Ni‐B amorphous alloy particles were selectively loaded inside and outside of carbon nanotubes (CNTs) to form a series of Ni‐B/CNTs amorphous alloy catalysts. The confinement effects of the CNTs on the physicochemical properties of Ni‐B/CNTs were investigated. The results show that compared with external loading, the Ni‐B active components after internal loading were subjected to the confinement effect, which better inhibited the growth and aggregation of Ni‐B particles, forming smaller‐size Ni‐B particles, and improved the thermal stability of the Ni‐B amorphous alloy. Moreover, the Ni‐B particles confined in the channels can more efficiently activate hydrogen. During the m ‐chloronitrobenzene hydrogenation, the Ni‐B/CNTs with Ni‐B internal loading showed higher catalytic hydrogenation activity than that for Ni‐B external loading. High‐temperature treatment caused a decrease in the catalytic activity, but the internal loading improved the stability of the catalysts. The internal loading method could effectively reduce the loss of the active component, which contributed to improving the stability of the catalyst.

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)0.001
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 score0.002
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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