Influence of adsorbates on the electronic and magnetic properties of graphane with H-vacancy defects
Bibliographic record
Abstract
The influence of adsorbates on the electronic and magnetic properties of graphane possessing the H-vacancy defects was investigated using the quantum-chemistry methods. We found that the H vacancies in graphane significantly improve its chemical reactivity to environment due to the unpaired electron left on the vacancy. Therefore, depending on the type of molecule adsorbed, the interaction between the adsorbate and the vacancy can occur with or without the formation of the bond. In case of the bond formation the unpaired electron supplied by the vacancy is removed and the electronic and magnetic properties of graphane become similar to that of pure defect-free graphane. Moreover, adsorption leads to degradation or vanishing of magnetism induced by the H vacancies in graphane, except in the case when the ${\text{CO}}_{2}$ molecule is attached, which is found to generate localized state with the unpaired electron after bonding with the H vacancy. Stability of ferromagnetism induced by the ${\text{CO}}_{2}$ molecules bound to the vacancies is found to be low. There are two reasons for low stability: localization of the spin density of the localized state entirely on adsorbate that minimize the interference of the spin tails of these states and the possibility of pairing of two neighboring ${\text{CO}}_{2}$ molecules to ${\text{C}}_{2}{\text{O}}_{4}$.
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) | 0.000 | 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".