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Record W2013113049 · doi:10.1038/srep03606

Revealing the Role of Catalysts in Carbon Nanotubes and Nanofibers by Scanning Transmission X-ray Microscopy

2014· article· en· W2013113049 on OpenAlexfundno aff
Jing Gao, Jun Zhong, Lili Bai, Jinyin Liu, Guanqi Zhao, Xuhui Sun

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchPriority Academic Program Development of Jiangsu Higher Education InstitutionsNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaUniversity of Saskatchewan
KeywordsCarbon nanotubeCatalysisCarbon nanofiberNanotechnologyMaterials scienceNanomaterialsNanofiberNanoscopic scaleCarbon fibersTransmission electron microscopyNanoparticleCharacterization (materials science)Carbon nanotube supported catalystChemical engineeringChemistryComposite materialComposite numberOrganic chemistry

Abstract

fetched live from OpenAlex

The identification of effective components on the atomic scale in carbon nanomaterials which improve the performance in various applications remains outstanding challenges. Here the catalyst residues in individual carbon nanotube (CNT) and carbon nanofiber (CNF) were clearly imaged with a concurrent characterization of their electronic structure by nanoscale scanning transmission X-ray microscopy. Except for prominent catalyst nanoparticle at the tip, tiny catalyst clusters along the tube (fiber) were detected, indicating a migration of the catalysts with the growth of CNTs (CNFs). The observation provides the direct evidence on the atomic metal in CNT for oxygen reduction reported in the literature. Interaction between catalysts (Fe, Ni) and CNTs (CNFs) at the tip was also identified by comparing the X-ray absorption spectra. A deep understanding of catalyst residues such as Fe or Ni in carbon nanomaterials is very vital to growth mechanism development and practical 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)0.003
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 score0.008
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.004
GPT teacher head0.223
Teacher spread0.219 · 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

Citations45
Published2014
Admission routes1
Has abstractyes

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