Tuning the Electronic Structure of Graphene by Molecular Dopants: Impact of the Substrate
Bibliographic record
Abstract
A combination of ultraviolet and X-ray photoelectron spectroscopy, X-ray absorption spectroscopy, and first principle calculations was used to study the electronic structure at the interface between the strong molecular acceptor 1,3,4,5,7,8-hexafluorotetracyano-naphthoquinodimethane (F6TCNNQ) and a graphene layer supported on either a quartz or a copper substrate. We find evidence for fundamentally different charge redistribution mechanisms in the two ternary systems, as a consequence of the insulating versus metallic character of the substrates. While electron transfer occurs exclusively from graphene to F6TCNNQ on the quartz support (p-doping of graphene), the Cu substrate electron reservoir induces an additional electron density flow to graphene decorated with the acceptor monolayer. Remarkably, graphene on Cu is n-doped and remains n-doped upon F6TCNNQ deposition. On both substrates, the work function of graphene increases substantially with a F6TCNNQ monolayer atop, the effect being more pronounced (∼1.3 eV) on Cu compared to quartz (∼1.0 eV) because of the larger electrostatic potential drop associated with the long-distance graphene-mediated Cu-F6TCNNQ electron transfer. We thus provide a means to realize high work function surfaces for both p- and n-type doped graphene.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".