(Invited) Experimental Quantum Transport in Strained Graphene and SWCNTs - Towards Noems
Bibliographic record
Abstract
We measure ballistic charge conductivity in strained suspended graphene and observe the theoretically predicted [1] strain-induced scalar and vector potentials. To do so, we built an experimental platform for in-situ quantum transport strain engineering in 2D materials. This instrumentation also permits low temperature (0.3 K) transport in 0 to 9 Tesla magnetic fields. The tunable uniaxial strain (up to 3%) is completely decoupled from the gate-tunable charge density, permitting quantitative understanding of strain effects. We show slippage-free mechanical clamping of high aspect-ratio graphene crystals, where atomically ordered edges are unnecessary for quantitative straintronics. We study in detail transport in a ballistic graphene channel whose length is 90 nm and width is 600 nm. By applying strain up to 0.6 % in this device, we observe that the strain-induced scalar potential shifts its low energy band structure downward by up to 30 meV. We show precise control of the gauge vector potential which reversibly suppresses the conductance. We discuss our progress towards strain engineering in SWCNTs, and calculations showing its potential in valley filtering and tuning electron-hole asymmetric quantum transistors. We conclude with an overview of another project to integrate suspended bilayer graphene devices in planar optical cavities towards achieving fully tunable nano-opto-electro-mechanical (NOEMS) carbon systems. To do so, we discuss a nitrocellulose-based dry-stamping method to manipulate and suspend individual 2D materials [2]. Using this method we assembled optical cavities able to a widely tune Raman scattering and absorption in graphene. [1] A. C. McRae, G. Wei, and A. R. Champagne, Phys. Rev. Applied 11, 054019 (2019). [2] I. G. Rebollo, F. C. Rodrigues-Machado, W. Wright, G. J. Melin, A. R. Champagne, ArXiv:2011.14166 Figure 1
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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 | 0.000 | 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".