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Record W2554055454 · doi:10.1002/aelm.201600351

Monitoring of Energy Conservation and Losses in Molecular Junctions through Characterization of Light Emission

2016· article· en· W2554055454 on OpenAlexafffund
Oleksii Ivashenko, Adam Johan Bergren, Richard L. McCreery

Bibliographic record

VenueAdvanced Electronic Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersAlberta Innovates - Technology Futures
KeywordsMaterials scienceKelvin probe force microscopeMolecular electronicsLayer (electronics)Chemical physicsLight emissionOptoelectronicsMoleculeMolecular physicsNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Emission of visible light from large area molecular junctions provides a direct measure of the energy of carriers when they encounter a conducting contact and stimulate photon emission. For carbon/molecule/carbon molecular junctions containing aromatic molecular layers with thicknesses less than 5 nm, transport is elastic, and the maximum emitted photon energy (i.e., “cut‐off” energy, hvco) is equal to eVapp, where Vapp is the bias across the molecular junction. hvco increases monotonically with Vapp, is symmetric with polarity, but is weakly dependent on the nature of the contact material. Light emission from molecular junctions containing oligomeric films of anthraquinone, nitroazobenzene, naphthalene diimide, and bis‐thienyl benzene with thicknesses of 4.5–59 nm is observed as a function of bias. For layers thicker than 5–7 nm, hvco < eVapp, indicating loss of energy and therefore inelastic transport. The energy loss depends strongly on molecular structure and is linear with molecular layer thickness. When the molecular layer thickness exceeds 5–7 nm, the results provide strong evidence for a transition from elastic to inelastic transport and for stepwise, activationless transport up to 65 nm molecular layer thicknesses. Such information proves valuable for determining transport mechanisms and ultimately designing molecular junctions with desirable electronic properties.

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 communication0.0000.000
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 head0.003
GPT teacher head0.201
Teacher spread0.198 · 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

Citations21
Published2016
Admission routes2
Has abstractyes

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