Functionalization of single-walled (<i>n</i>,0) carbon and boron nitride nanotubes by carbonyl derivatives (<i>n</i> = 5, 6): a DFT study
Bibliographic record
Abstract
By using density functional theory calculations, the chemical functionalization of finite-sized (5,0) and (6,0) carbon nanotubes (CNTs) and boron nitride nanotubes (BNNTs) by different carbonyl derivatives –COX (X = H, CH3, OCH3, OH, and NH2) is studied in terms of geometrical and electronic structure properties. Also, the benefits of local reactivity descriptors is studied to characterize the reactive sites of the external surface of the tubes. These local reactivity descriptors include the electrostatic potential VS(r) and average local ionization energy ĪS(r) on the surfaces of these nanotubes. The estimated ĪS(r) values show that the functionalized CNTs tend to activate the surface toward electrophilic/radical attack. Results show that the chemical functionalization of CNTs leads to the reduction of VS(r) values and therefore enhances the surface reactivity. On the other hand, BNNTs resist chemical functionalization due to the negligible decrease in the VS,min and ĪS,min values. Generally, in contrast to BNNTs, the chemical functionalization of CNTs can considerably improve their surface reactivity. To verify the surface reactivity pattern based on the chosen reactivity descriptors, the reaction energies for the interaction of an H + ion or hydrogen radical with external surface of the functionalized CNTs and BNNTs are calculated. A general feature of all studied systems is that stronger potentials are associated with regions of higher curvature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".