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Record W4389325579 · doi:10.48550/arxiv.2312.00177

Mechanical control of quantum transport in graphene

2023· preprint· en· W4389325579 on OpenAlexaff
A. C. McRae, Gaoyu Wei, Li Huang, Serap Yiğen, V. Tayari, A. R. Champagne

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsGrapheneBilayer grapheneQuantumBallistic conductionCondensed matter physicsMaterials scienceWork (physics)ConductivityNanotechnologyScalar (mathematics)PhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Two-dimensional materials (2DMs) are fundamentally electro-mechanical systems. Their environment unavoidably strains them and modifies their quantum transport properties. For instance, a simple uniaxial strain could completely turn off the conductivity of ballistic graphene or switch on/off the superconducting phase of magic-angle bilayer graphene. Here we report measurements of quantum transport in strained graphene which agree quantitatively with models based on mechanically-induced gauge potentials. We mechanically induce in-situ a scalar potential, which modifies graphene's work function by up to 25 meV, and vector potentials which suppress the ballistic conductivity of graphene by up to 30 % and control its quantum interferences. To do so, we developed an experimental platform able to precisely tune both the mechanics and electrostatics of suspended graphene transistors at low-temperature over a broad range of strain (up to 2.6 %). This work opens many opportunities to experimentally explore quantitative strain effects in 2DM quantum transport and technologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.095
GPT teacher head0.224
Teacher spread0.129 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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