Graphene and graphane functionalization with hydrogen: electronic and optical signatures
Bibliographic record
Abstract
Abstract We proved the possibility of electron gap tuning of graphene‐based materials, using extensive first principles modelling of the structural, electronic and optical properties of partially hydrogenated graphene. Optical tools were proposed to characterize the hydrogenation process. Sub‐monolayer hydrogen passivated graphene and various hydrogen induced superstructures were considered. Electron and optical DFT LDA gaps between 0.2 and 1.8 eV, suitable for microelectronic application, were obtained for low hydrogen coverage structures. For such systems, hydrogen clustering (by saturating neighbouring C dangling bonds at opposite sides of the graphene sheet) is energetically most favourable and generally produces a larger gap. A more homogeneous H distribution with one‐side bonding to C‐host atoms is, in contrast, less energetically favourable and even structurally unstable. In addition, such H configurations generally produce lower electron band gap. Hydrogen at low coverage not only locally buckles six‐fold carbon rings, it leads to dimerization and subsequent electron localization for neighbouring carbon atoms, which are not H bonded. Such structural and electronic processes are responsible for gap opening and its magnitude. Calculated linear optical response indicates that the optics is not only gap sensitive, but, combined with experimental spectra, can also provide access to microscopic properties of 2D nano‐sheets such as symmetry, hydrogen induced structure, degree of hydrogenation, chemical bonding, and others (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".