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Record W1964990026 · doi:10.1021/ci0601113

Stability and Electronic Properties of Nitrogen Nanoneedles and Nanotubes

2006· article· en· W1964990026 on OpenAlexaff
Jenna L. Wang, Gerald H. Lushington, Paul G. Mezey

Bibliographic record

VenueJournal of Chemical Information and Modeling · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsNanostructureCarbon nanotubeNitrogenNanoneedleNanotechnologyElectronic structureLone pairMaterials scienceSymmetry (geometry)Charge (physics)Quantum dotElectronChemical physicsChemistryComputational chemistryMoleculePhysics

Abstract

fetched live from OpenAlex

The electronic structures and stability of nitrogen nanostructures, nanotubes, and fiberlike nanoneedles of various diameters, formed by units N2m (m = 2-6), were studied by quantum chemistry computational modeling methods. The geometrical structures with various cross-sections and terminal units, their energetic stability, and their rather peculiar electron density distributions were investigated. The tightest nitrogen nanoneedle (NNN) studied theoretically in this work is the structure (N4n with D2h symmetry, whereas the nitrogen nanotube (NNT) with the largest diameter discussed here is the structure (N12)n with D2 symmetry. These families of NNNs and NNTs can be considered as nanostructures not only for potential applications as devices in nanotechnology or as possible scaffold structures but also as ligands in synthetic chemistry and high-energy density materials (HEDMs). As a consequence of the lone-pair electrons present around the walls of these NNNs and NNTs, these nitrogen nanostructures and the nitrogen nano-bundles (NNB) formed by aligning and combining them using intermediate carbon atoms, can have highly variable electronic properties controlled by the changing charge environment. In particular, for extended systems based on the units studied here, the band gaps of each of these systems can be affected greatly by the local charge of the environment.

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 score0.042
Threshold uncertainty score0.198

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 communication0.0000.001
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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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

Citations15
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Chemical Information and ModelingSame topicCarbon Nanotubes in CompositesFrench-language works237,207