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Record W3212439307 · doi:10.1021/acs.jpclett.1c03769

Electronic Conduction through Monolayer Amorphous Carbon Nanojunctions

2022· article· en· W3212439307 on OpenAlexafffund
Nicolas Gastellu, Michael Kilgour, Lena Simine

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2022
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDelocalized electronGrapheneAtomic orbitalMonolayerHamiltonian (control theory)Condensed matter physicsFermi levelQuantum interferenceAmorphous solidMaterials sciencePhysicsQuantumChemistryNanotechnologyQuantum mechanicsCrystallographyMathematicsElectron

Abstract

fetched live from OpenAlex

In molecular electronic conduction, exotic lattice morphologies often give rise to exotic behaviors. Among 2D systems, graphene is a notable example. Recently, a stable amorphous version of graphene called monolayer amorphous carbon (MAC) was synthesized. MAC poses a new set of questions regarding the effects of disorder on conduction. In this Letter, we perform an ensemble-level computational analysis of the coherent electronic transmission through MAC nanofragments in search of defining characteristics. Our analysis, relying on a semiempirical Hamiltonian (Pariser–Parr–Pople) and Landauer theory, showed that states near the Fermi energy ( E F ) in MAC inherit partial characteristics of analogous surface states in graphene nanofragments. Away from E F, current is carried by a set of delocalized states that transition into a subset of insulating interior states at the extreme portions of MAC’s energy spectrum. Finally, we also found that quantum interference between frontier orbitals is a common feature among MAC nanofragments.

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 studies0.0000.000
Scholarly communication0.0010.000
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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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