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Record W2896674764 · doi:10.1021/acs.nanolett.8b03503

Diradical Character as a Guiding Principle for the Insightful Design of Molecular Nanowires with an Increasing Conductance with Length

2018· article· en· W2896674764 on OpenAlexafffund
Thijs Stuyver, Tao Zeng, Yuta Tsuji, Paul Geerlings, Frank De Proft

Bibliographic record

VenueNano Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsCarleton University
FundersJapan Society for the Promotion of ScienceVrije Universiteit BrusselNatural Sciences and Engineering Research Council of CanadaFonds Wetenschappelijk OnderzoekCarleton University
KeywordsDiradicalConductanceNanowireOhmic contactCharacter (mathematics)Condensed matter physicsNanotechnologyLength scaleMaterials sciencePhysicsQuantum mechanicsMathematicsLayer (electronics)

Abstract

fetched live from OpenAlex

In recent years, a considerable interest has grown in the design of molecular nanowires with an increasing conductance with length. The development of such nanowires is highly desirable because they could play an important role in future molecular-scale circuitry. Whereas the first experimental observation of this nonclassical behavior still has to be realized, a growing number of candidate wires have been proposed theoretically. In this Letter, we point out that all the wires with an anti-Ohmic increasing conductance with length proposed so far share a common characteristic: their diradical character increases with length. The conceptual connection between diradical character and conductance enables a systematic design of such anti-Ohmic wires and explains the difficulty in their syntheses. A strategy is proposed to balance the stability and conductance so that this nonclassical phenomenon can be observed.

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: none
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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.225
Teacher spread0.209 · 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

Citations83
Published2018
Admission routes2
Has abstractyes

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