Charge Transport Study of in-Situ Cesium Doped Monolayer Graphene
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».