MétaCan
Menu
Back to cohort
Record W1994717945 · doi:10.1039/b9nr00159j

Theoretical studies on structures, 13C NMR chemical shifts, aromaticity, and chemical reactivity of finite-length open-ended armchair single-walled carbon nanotubes

2009· article· en· W1994717945 on OpenAlexafffund
Lei Vincent Liu, Wei Quan Tian, Yakun Chen, Yu Zhang, Yan Alexander Wang

Bibliographic record

VenueNanoscale · 2009
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAromaticityChemical shiftReactivity (psychology)Carbon nanotubeMolecular orbitalMaterials scienceDensity functional theoryChemical physicsComputational chemistryCarbon-13 NMRCarbon fibersHydrogenAtomic orbitalCarbeneMolecular physicsCrystallographyMoleculeChemistryNanotechnologyStereochemistryOrganic chemistryCatalysisPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

The geometries, chemical shifts, aromaticity, and reactivity of finite-length open-ended armchair single-walled carbon nanotubes (SWCNTs) have been studied within density functional theory. The widely used model of capping hydrogen atoms at the open ends of a SWCNT changes the chemical activity of the SWCNT and destabilizes the frontier molecular orbitals. The edge pi-orbital of the open ends enhances both pi- and sigma-aromaticity of the first belt of hexagons of carbon atoms at the open ends. The effect of the open ends on the structure and chemical reactivity of the SWCNT reaches only the first several layers of the hexagons of carbon atoms. Additions of carbene and dichlorocarbene to the nanotube reveal that the open ends have higher reactivities than the inner regions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.281
Teacher spread0.257 · 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 designSimulation or modeling
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

Citations21
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueNanoscaleSame topicCarbon Nanotubes in CompositesFrench-language works237,207