Charge Transport Study of in-Situ Cesium Doped Monolayer Graphene
Bibliographic record
Abstract
Modifying graphene with surface adsorbates is a widely studied way of modifying graphene’s electronic properties. Different graphene/adatom systems have shown that it is possible to open a band gap in graphene1, engineer topological insulators2 and create magnetic moments3. The atomic thickness of graphene maximizes the effect of adsorbates, enabling chemical sensors that achieve single molecule sensitivity4. Alkali metal adatoms are a particularly efficient dopant for graphene due to their high electronegativity. Lithium/graphene systems have been studied for in the context of low-dimensional superconductivity5 and achieving ultrafast diffusion of lithium6 for battery applications. However, doping graphene with alkali metal adatoms bears challenges. Experiments with alkali metals must be conducted in an ultra-high vacuum or inert gas environment due to the high reactivity of the alkali. Alkali metal adsorbed graphene is not air stable, which imposes limits on the available characterization methods. An impurity free graphene surface must be achieved prior to adsorption to achieve efficient adsorption and avoid unwanted parasitic reactions. This problem is particularly acute for graphene samples prepared by the use of polymer handles. Alkali metal atoms can also intercalate under the graphene layer depending on substrate and sample preparation conditions. Moreover, it is possible for adatoms to form clusters on graphene surface7which negatively impacts both doping uniformity and efficiency. In this work, we report a new method of alkali doping of graphene to reach ultra-high doping (~1014cm-2) for charge transport studies beyond the limit of ionic liquid gating. We work with chemical vapor deposition grown graphene transferred onto quartz substrates. Quartz is the substrate of choice due to its chemical inertness and optical transparency, with the latter enabling non-invasive Raman spectroscopy through the substrate. Micron scale graphene devices were prepared using lithography-based methods and the samples were thermally annealed in a nitrogen glove box environment to desorb water prior to alkali doping. The graphene was subsequently exposed to cesium vapor at different temperatures using a flip-chip method that allows hermetic sealing of the air sensitive samples in an inert gas environment with a liquid cesium source of cesium vapour. We measured the in-situ variation of graphene resistivity as cesium atoms are adsorbed onto the graphene surface, and resistivity is modulated by charge transfer doping. Evidence of ultra-high doping is shown via Raman spectroscopy performed through the quartz substrate window, with G-peak shifts from 1589 cm-1 up to 1608 cm-1. Hall measurements in-situ further confirm the strong doping. We measured the electronic transport properties of cesium doped graphene at temperatures as low as 1.2 K and under magnetic fields up to 7 T. Weak-localization, magnetoresistance and Hall resistance are measured and analyzed. 1. Elias, D. C.; Nair, R. R.; Mohiuddin, T. M.; Morozov, S. V.; Blake, P.; Halsall, M. P.; Ferrari, A. C.; Boukhvalov, D. W.; Katsnelson, M. I.; Geim, A. K.; Novoselov, K. S., Control of graphene's properties by reversible hydrogenation: evidence for graphane. Science 2009, 323 (5914), 610-3. 2. Weeks, C.; Hu, J.; Alicea, J.; Franz, M.; Wu, R., Engineering a Robust Quantum Spin Hall State in Graphene via Adatom Deposition. Physical Review X 2011, 1 (2). 3. Hong, X.; Zou, K.; Wang, B.; Cheng, S. H.; Zhu, J., Evidence for spin-flip scattering and local moments in dilute fluorinated graphene. Phys Rev Lett 2012, 108 (22), 226602. 4. Schedin, F.; Geim, A. K.; Morozov, S. V.; Hill, E. W.; Blake, P.; Katsnelson, M. I.; Novoselov, K. S., Detection of individual gas molecules adsorbed on graphene. Nat Mater 2007, 6 (9), 652-5. 5. Profeta, G.; Calandra, M.; Mauri, F., Phonon-mediated superconductivity in graphene by lithium deposition. Nat Phys 2012, 8 (2), 131-134. 6. Kühne, M.; Paolucci, F.; Popovic, J.; Ostrovsky, P. M.; Maier, J.; Smet, J. H., Ultrafast lithium diffusion in bilayer graphene. Nat Nanotechnol 2017, 12, 895. 7. Fan, X.; Zheng, W. T.; Kuo, J. L.; Singh, D. J., Adsorption of single Li and the formation of small Li clusters on graphene for the anode of lithium-ion batteries. ACS Appl Mater Interfaces 2013, 5 (16), 7793-7.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".