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Record W2790920659 · doi:10.1088/2053-1591/aaadd6

The effects of hydrogen proportion on the synthesis of carbon nanomaterials with gaseous detonation (deflagration) method

2018· article· en· W2790920659 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMaterials Research Express · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsNanomaterialsHydrogenMaterials scienceDeflagrationCarbon fibersCarbon nanotubeRaman spectroscopyDetonationChemical engineeringDiffractometerNanotechnologyTransmission electron microscopyAnalytical Chemistry (journal)Scanning electron microscopeChemistryComposite materialExplosive materialOrganic chemistry

Abstract

fetched live from OpenAlex

Using ferrocene, H 2 and O 2 , Carbon nanomaterials were prepared with gaseous detonation (deflagration) method. The effects of H 2 on the phase and morphology of carbon nanomaterials were studied by various proportions of H 2 in the reaction. The prepared samples were characterized by x-ray diffractometer, transmission electron microscope and Raman spectrometer. The results show that hydrogen proportion has a great influence on the phase and morphology of carbon nanomaterials. The high hydrogen proportion leads to much unreacted hydrogen, which could protect the iron atom from oxidation of carbon and dilute the reactants contributing to uniform particle size. In addition, the graphitization degree of multi-walled carbon nanotubes, observed in samples with high H 2 proportion, is high enough to see the lattice fringes, but the degree of graphitization of whole sample is lower than which fabricated with low H 2 proportion, and it may result from the low energy generation.

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.

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)0.007
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
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 science0.0010.000
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 head0.024
GPT teacher head0.326
Teacher spread0.302 · 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