Exploring Molecular and Electronic Property Predictions of Reduced Graphene Oxide Nanoflakes via Density Functional Theory
Bibliographic record
Abstract
In this research, we perform a theoretical interpretation of molecular and electronic properties of reduced graphene oxide (rGO) nanoflakes through the density functional theory. Here, two pristine graphene nanoflake systems were passivated by hydrogen atoms at their edges, armchair (C58H20) and zigzag (C54H20); besides, we implemented 12 rGO systems with a range of low oxide coverage (1, 3, and 4%). Computational calculations were carried out employing the functional hybrid B3LYP and the basis 6-31G(d, p) and 6-311G(d, p) levels of theory. We brought the proposed molecular structures to a stable minimum. We determined the global reactivity descriptors through chemical potential, hardness, softness, and index of electrophilicity. Besides, the maps of electrostatic potential were generated. We found that the hydroxyl and epoxy functional groups dope the graphene molecule in p-type and n-type forms, respectively. In addition, we could attribute the increases of the oxide coverage and the chemical potential to the softness of the molecule. These results suggest that structures with this type of doping can help in developing advanced electronics of sensors and devices.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".