Prediction of highly stable 2D carbon allotropes based on azulenoid kekulene
Bibliographic record
Abstract
Abstract Despite enormous interest in two-dimensional (2D) carbon allotropes, discovering stable 2D carbon structures with practically useful electronic properties presents a significant challenge. Computational modeling in this work shows that fusing azulene-derived macrocycles – azulenoid kekulenes (AK) – into graphene leads to the most stable 2D carbon allotropes reported to date, excluding graphene. Density functional theory predicts that placing the AK units in appropriate relative positions in the graphene lattice opens the 0.54 eV electronic bandgap and leads to the appearance of the remarkable 0.80 eV secondary gap between conduction bands – a feature that is rare in 2D carbon allotropes but is known to enhance light absorption and emission in 3D semiconductors. Among porous AK structures, one material stands out as a stable narrow-multigap (0.36 and 0.56 eV) semiconductor with light charge carriers (me = 0.17 m0, mh = 0.19 m0), whereas its boron nitride analog is a wide-multigap (1.51 and 0.82 eV) semiconductor with light carriers (me = 0.39 m0, mh = 0.32 m0). The multigap engineering strategy proposed here can be applied to other carbon nanostructures creating novel 2D materials for electronic and optoelectronic applications.
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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".