Combined Ultra High Vacuum Raman and Electronic Transport Characterization of Large‐Area Graphene on SiO<sub>2</sub>
Bibliographic record
Abstract
An original experimental setup which allows for simultaneous sample characterization by Raman spectroscopy and electronic tranport in ultra‐high vacuum at low temperatures is presented. We show the applicability of this setup for the case of graphene that is transferred from an Ir(111) single crystal onto SiO2. The transfer of graphene is carried out using a water‐promoted electrochemical bubbling technique which is applied to graphene/Ir for the first time. The characterization prior to the transfer includes electron diffraction, photoemission spectroscopy and Raman spectroscopy using ultraviolet excitation. Following the transfer procedure, the graphene layer is electrically contacted and mounted onto a special sample carrier. This carrier allows for combined Raman and transport measurements inside an ultra high vacuum (UHV) system. UHV Raman mapping reveals a large area homogeneous graphene quality over several mm2 characterized by a D/G intensity ratio less than 0.1. UHV electrical characterization of transferred graphene in a field effect transistor geometry yields a carrier mobility of 675 cm2 V−1 s−1. Upon alkali metal doping in UHV conditions using a Cs getter, a decrease of the 4‐point resistance from above 2500 Ω to below 10 Ω is observed. The presented approach paves the way for future combined UHV Raman and transport characterization of two‐dimensional materials that are doped into superconducting or charge‐density‐wave ground states.
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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".