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Record W2114908290 · doi:10.1139/cjc-2013-0103

A DFT study on electronic structure and local reactivity descriptors of pristine and carbon-substituted AlN nanotubes

2013· article· en· W2114908290 on OpenAlexvenueno aff
Mehdi D. Esrafili

Bibliographic record

VenueCanadian Journal of Chemistry · 2013
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsnot available
Fundersnot available
KeywordsReactivity (psychology)ChemistryChemisorptionFukui functionDensity functional theoryNitrideCarbon nanotubeCarbon fibersElectronic structureHydrogenNucleophileComputational chemistryNitrogenAluminiumAtomic carbonChemical physicsPhysical chemistryNanotechnologyOrganic chemistryAdsorptionElectrophileMaterials scienceLayer (electronics)Catalysis

Abstract

fetched live from OpenAlex

A density functional theory study was carried out to investigate the structural and electronic structure properties of pristine and carbon-substituted (6,0) aluminum nitride nanotubes (AlNNTs). We examine the usefulness of local reactivity descriptors to predict the reactivity of AlN atomic sites on the external surface of the tubes. The properties determined include the Fukui function f(k) and local softness s(k) on the surfaces of the investigated tubes. According to the values of f(k) and s(k) for the pristine AlNNT, the aluminum atoms are highly preferred sites for nucleophile addition. More especially, the aluminum atoms in middle portion show different reactivity pattern from those at the edge or cap regions of the nanotube. Our results indicate that the nitrogen atoms adjacent to the substituted carbon atoms are less reactive toward atomic hydrogen chemisorption than those in the pristine one. There is an acceptable correlation between chemisorption energies and reactivity indexes, indicating that f(k) and s(k) provide an effective means for rapidly and economically assessing the relative reactivities of finite-sized AlNNTs.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.021
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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