The electronic properties and nonlinear optical responses of the intermediate structures in rolling graphene to carbon nanotubes
Bibliographic record
Abstract
From the same piece of finite size graphene (F-graphene) sheet through different directions (zigzag edge or armchair edge), (4, 4) and (8, 0) carbon nanotube clips form. The electronic properties of the intermediate structures in the two rolling processes 44 (zigzag) and 80 (armchair) have been investigated using quantum chemistry method. The magnetism of the F-graphene sheet disappears with the rolling operation in 44, while it is maintained throughout the whole rolling operation in 80. Furthermore, the highest occupied molecular orbital (HOMO) α and HOMO β gradually extend to the whole framework from the zigzag edges with the rolling operation in 44, and they gradually localize to the lower and upper half of the framework in 80. Oxygen passivation along the opening of the intermediate structures effectively improves the nonlinear optical (NLO) response of the intermediate structures in both the zigzag and the armchair processes. Oxygen passivation along the armchair opening in 80 enhances the βtot value, yet does not bring essential changes to the electron transitions contributed to the NLO response. Oxygen passivation along the zigzag opening in 44 is able not only to enhance the βtot value but also to change the transition nature of electron excitations with a major contribution to the NLO response.
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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) | 0.000 | 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".