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Record W2046883743 · doi:10.1039/c5nr01168j

Revealing the synergetic effects in Ni nanoparticle-carbon nanotube hybrids by scanning transmission X-ray microscopy and their application in the hydrolysis of ammonia borane

2015· article· en· W2046883743 on OpenAlexafffund
Guanqi Zhao, Jun Zhong, Jian Wang, Tsun‐Kong Sham, Shuit‐Tong Lee

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsWestern UniversityCanadian Light Source (Canada)
FundersNational Key Research and Development Program of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsAmmonia boraneCatalysisCarbon nanotubeMaterials scienceAmmoniaBoraneHydrolysisNanoparticleChemical engineeringNanotechnologyTransmission electron microscopyChemistryOrganic chemistryHydrogen production

Abstract

fetched live from OpenAlex

The hybrids of carbon nanotubes (CNTs) and the supported Ni nanoparticles (NPs) have been studied by scanning transmission X-ray microscopy (STXM) and tested by the hydrolysis reaction of ammonia borane (AB, NH3BH3). Data clearly showed the existence of a strong interaction between Ni NPs and thin CNTs (C-O-Ni bonds), which favored the tunable (buffer) electronic structure of Ni NPs facilitating the catalytic process. The hydrolysis process of AB confirmed the hypothesis that the hybrids with a strong interfacial interaction would show superior catalytic performance, while the hybrids with a weak interfacial interaction show poor performance. Our results provide a wealth of detailed information regarding the electronic structure of the NP-CNT hybrids and provide guidance towards the rational design of high-performance catalysts for energy applications.

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 score0.005
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.256
Teacher spread0.246 · 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

Citations40
Published2015
Admission routes2
Has abstractyes

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