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Record W1702979767 · doi:10.1139/cjc-2013-0553

A theoretical study on surface reactivity of fluorinated (<i>n</i>,0) and (<i>n</i>,<i>n</i>) carbon nanotubes (<i>n</i> = 3–6)

2014· article· en· W1702979767 on OpenAlexvenueno aff
Mehdi D. Esrafili, Roghaye Nurazar

Bibliographic record

VenueCanadian Journal of Chemistry · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsFluorineChemistryReactivity (psychology)ZigzagCarbon nanotubeDensity functional theoryIonizationElectrophileCarbon fibersGrapheneComputational chemistryIonization energyChemical physicsPhysical chemistryMolecular physicsNanotechnologyOrganic chemistryIonGeometryMaterials scienceComposite materialCatalysis

Abstract

fetched live from OpenAlex

Density functional theory calculations are performed to investigate the surface reactivity of pristine as well as fluorine-terminated zigzag (n,0) and armchair (n,n) carbon nanotubes (n = 3–6). The properties determined include the electrostatic potential V(r) and average local ionization energy Ī(r) on the surfaces of the investigated tubes. A general feature of all of the systems studied is that stronger potentials are associated with regions of higher curvature. The results indicate that both V(r) and Ī(r) detect the effects of fluorine-terminated regions in which significant effects are observed for those atoms in the vicinity of the fluorine-terminated regions. Comparison with the Ī(r) of the pristine nanotube indicates correctly that in the fluorine-terminated models, the fluorine atoms tend to deactivate the surface toward electrophilic/radical attack.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2014
Admission routes1
Has abstractyes

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