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Record W3146336771 · doi:10.1038/s41467-021-22774-0

Tunneling current modulation in atomically precise graphene nanoribbon heterojunctions

2021· article· en· W3146336771 on OpenAlexaff
Boris V. Senkovskiy, A. V. Nenashev, Seyed Khalil Alavi, Yannic Falke, Martin Hell, Pantelis Bampoulis, Dmitry V. Rybkovskiy, Dmitry Yu. Usachov, А.А. Федоров, A. I. Chernov, Florian Gebhard, Klaus Meerholz, Dirk Hertel, Masashi Arita, Taichi Okuda, K. Miyamoto, K. Shimada, Felix R. Fischer, Thomas Michely, S. D. Baranovskiǐ, Klas Lindfors, Thomas Szkopek, A. Grüneis

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersBasic Energy SciencesOffice of ScienceSaint Petersburg State UniversityRussian Foundation for Basic ResearchAlexander von Humboldt-StiftungVolkswagen FoundationDeutscher Akademischer AustauschdienstDeutsche ForschungsgemeinschaftU.S. Department of Energy
KeywordsHeterojunctionGrapheneMaterials scienceQuantum tunnellingRaman spectroscopyGraphene nanoribbonsOptoelectronicsMonolayerConductanceNanotechnologyScanning tunneling microscopeBiasingCondensed matter physicsVoltagePhysicsOptics

Abstract

fetched live from OpenAlex

Lateral heterojunctions of atomically precise graphene nanoribbons (GNRs) hold promise for applications in nanotechnology, yet their charge transport and most of the spectroscopic properties have not been investigated. Here, we synthesize a monolayer of multiple aligned heterojunctions consisting of quasi-metallic and wide-bandgap GNRs, and report characterization by scanning tunneling microscopy, angle-resolved photoemission, Raman spectroscopy, and charge transport. Comprehensive transport measurements as a function of bias and gate voltages, channel length, and temperature reveal that charge transport is dictated by tunneling through the potential barriers formed by wide-bandgap GNR segments. The current-voltage characteristics are in agreement with calculations of tunneling conductance through asymmetric barriers. We fabricate a GNR heterojunctions based sensor and demonstrate greatly improved sensitivity to adsorbates compared to graphene based sensors. This is achieved via modulation of the GNR heterojunction tunneling barriers by adsorbates.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.339
Teacher spread0.306 · 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 teacher head, 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

Citations40
Published2021
Admission routes1
Has abstractyes

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