A DFT study on electronic structure and local reactivity descriptors of pristine and carbon-substituted AlN nanotubes
Bibliographic record
Abstract
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".