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Record W2038158248 · doi:10.5539/jmsr.v2n1p163

Variations in the Electrical Resistivity of Vapour Grown Carbon Fibres by Effect of Successive Operations of Intake and Outgassing of Hydrogen

2012· article· en· W2038158248 on OpenAlexvenueno aff
César Mota, A. Madroñero, José Maria Amo, José Ignacio Robla, Mario Culebras, A. Cantarero

Bibliographic record

VenueJournal of Materials Science Research · 2012
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen Storage and Materials
Canadian institutionsnot available
FundersMinisterio de Ciencia e Innovación
KeywordsOutgassingMaterials scienceHydrogenElectrical resistivity and conductivityCarbon fibersDesorptionNitric acidDecompositionMethaneKineticsEtching (microfabrication)CatalysisSorptionUltra-high vacuumChemical engineeringComposite materialInorganic chemistryNanotechnologyComposite numberMetallurgyOrganic chemistryChemistryAdsorption

Abstract

fetched live from OpenAlex

The aim of this work is to study the behaviour of Vapour Growth Carbon Fibres (VGCF) with different treatments after the intake and outgassing of hydrogen takes place. The VGCFs are prepared via catalyzed decomposition of a mixture of hydrogen and methane. The catalyst was minute particles of iron. The fibre thickness was about 10 to 20 microns and they have a trunk-tree inner structure. Two different treatments are tested to improve the properties of the as grown samples: a heat treatment under vacuum conditions and an acid etching with nitric acid. The desorption kinetics of the three type of samples is studied measuring the electrical resistivity during the hydrogen outgassing. As a result, the surface treatment with acid etching seems to be an efficient method to improve the kinetics of the hydrogen sorption in the first hydrogenation cycle.

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)0.026
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 score0.022
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.367
Teacher spread0.332 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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