Stability and Electronic Properties of Nitrogen Nanoneedles and Nanotubes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".