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Record W2742188564 · doi:10.1021/acs.nanolett.7b01745

Single Electron Transistor with Single Aromatic Ring Molecule Covalently Connected to Graphene Nanogaps

2017· article· en· W2742188564 on OpenAlexafffund
Qizhi Xu, Giovanni Scuri, Carly Mathewson, Philip Kim, Colin Nuckolls, Delphine Bouilly

Bibliographic record

VenueNano Letters · 2017
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersDivision of Materials ResearchNational Institute of General Medical SciencesOffice of Naval ResearchFonds de recherche du Québec – Nature et technologiesMicroelectronics Advanced Research CorporationNatural Sciences and Engineering Research Council of CanadaDefense Advanced Research Projects Agency
KeywordsGrapheneCovalent bondTransistorMaterials scienceRing (chemistry)MoleculeNanotechnologyElectronMolecular electronicsOptoelectronicsChemistryOrganic chemistryPhysicsVoltageElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

We report a robust approach to fabricate single-molecule transistors with covalent electrode-molecule-electrode chemical bonds, ultrashort (∼1 nm) molecular channels, and high coupling yield. We obtain nanometer-scale gaps from feedback-controlled electroburning of graphene constrictions and bridge these gaps with molecules using reaction chemistry on the oxidized graphene edges. Using these nanogaps, we are able to optimize the coupling chemistry to achieve high reconnection yield with ultrashort covalent single-molecule bridges. The length of the molecule is found to influence the fraction of covalently reconnected nanogaps. Finally, we discuss the tunneling nature of the covalent contacts using gate-dependent transport measurements, where we observe single electron transport via large energy Coulomb blockade even at room temperature. This study charts a clear path toward the assembling of ultraminiaturized electronics, sensors, and switches.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.187
Teacher spread0.180 · 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

Citations64
Published2017
Admission routes2
Has abstractyes

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