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Record W2026426944 · doi:10.1139/p09-040

A boroxol ring doped zigzag boron nitride nanotube: a computational DFT study of the quadrupole coupling constant

2009· article· en· W2026426944 on OpenAlexvenueno aff
Asadollah Boshra, Ahmad Seif

Bibliographic record

VenueCanadian Journal of Physics · 2009
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsBoron nitrideZigzagRing (chemistry)DopantBoronQuadrupoleDensity functional theoryNanotubeHeteroatomMolecular physicsDopingComputational chemistryChemical physicsAtomic physicsMaterials scienceCrystallographyPhysicsNanotechnologyCondensed matter physicsChemistryCarbon nanotubeOrganic chemistryGeometry

Abstract

fetched live from OpenAlex

Based upon density functional theory, we investigate the influence of oxygen dopant atoms that make a boroxol ring on the electrostatic properties of a zigzag (10, 0) boron nitride nanotube in which three of the nitrogen atoms are replaced by oxygen dopant atoms. The electric field gradient (EFG) tensors at the sites of 11B and 14N nuclei were calculated and converted to quadrupole coupling constants (CQ) in the two models of a perfect and a boroxol ring O-doped (10, 0) single-walled boron nitride nanotube (BNNT). Our calculations showed that the CQ values of the boron and nitrogen nuclei along the length of a perfect BNNT are divided into layers. Among the layers the mouth layers have the largest CQ magnitudes. In the doped model, in addition to the mouth layers, the CQ values of those nitrogen nuclei which directly bond to the boroxol ring are increased. However, the CQ values of the boron nuclei that make the boroxol ring and directly bond to the boroxol ring are decreased.

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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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