Alkali doping of graphene: The crucial role of high-temperature annealing
Bibliographic record
Abstract
The doping efficiency of lithium deposited at cryogenic temperatures on epitaxial and chemical vapor deposition monolayer graphene has been investigated under ultrahigh-vacuum conditions. Change of charge-carrier density was monitored by gate voltage shift of the Dirac point and by Hall measurements in low and high doping regimes. It was found that preannealing the graphene greatly enhanced the maximum levels of doping that could be achieved: doping saturated at $\mathrm{\ensuremath{\Delta}}n=2\ifmmode\times\else\texttimes\fi{}{10}^{13}\phantom{\rule{4pt}{0ex}}{e}^{\ensuremath{-}}/{\mathrm{cm}}^{2}$ without annealing, independent of sample type or previous processing; after a 900 K anneal, the saturated doping rose one order of magnitude to $\mathrm{\ensuremath{\Delta}}n=2\ifmmode\times\else\texttimes\fi{}{10}^{14}\phantom{\rule{4pt}{0ex}}{e}^{\ensuremath{-}}/{\mathrm{cm}}^{2}$.
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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".