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Record W2785738920 · doi:10.1021/acsami.7b19305

Bottom-up, Robust Graphene Ribbon Electronics in All-Carbon Molecular Junctions

2018· article· en· W2785738920 on OpenAlexafffund
Mustafa Supur, Colin Van Dyck, Adam Johan Bergren, Richard L. McCreery

Bibliographic record

VenueACS Applied Materials & Interfaces · 2018
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsNational Research Council CanadaNational Institute for NanotechnologyUniversity of Alberta
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaOffice of ScienceUniversity of AlbertaU.S. Department of EnergyAlberta InnovatesBasic Energy SciencesAlberta Innovates - Technology Futures
KeywordsMaterials scienceGrapheneRibbonElectronicsNanotechnologyCarbon fibersMolecular electronicsFlexible electronicsGraphene nanoribbonsOptoelectronicsComposite materialMoleculeComposite numberElectrical engineering

Abstract

fetched live from OpenAlex

Large-area molecular electronic junctions consisting of 5-carbon wide graphene ribbons (GR) with lengths of 2–12 nm between carbon electrodes were fabricated by electrochemical reduction of diazotized 1,8-diaminonaphthalene. Their conductance greatly exceeds that observed for other molecular junctions of similar thicknesses, by a factor of >1 × 10 4 compared to polyphenylenes and >1 × 10 7 compared to alkane chains. The remarkable increase of conductance of the GR nanolayer results from (i) uninterrupted planarity of fused-arene structure affording extensive π-electron delocalization and (ii) enhanced electronic coupling of molecular layer with the carbon bottom contact by two-point covalent bonding, in agreement with DFT-based simulations.

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 categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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.

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

Citations27
Published2018
Admission routes2
Has abstractyes

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