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Record W4362704679 · doi:10.1002/anie.202302150

Not So Innocent After All: Interfacial Chemistry Determines Charge‐Transport Efficiency in Single‐Molecule Junctions

2023· article· en· W4362704679 on OpenAlexafffund
Abdalghani Daaoub, James M. F. Morris, Vanessa A. Béland, Paul Demay‐Drouhard, Amaar Hussein, Simon J. Higgins, Hatef Sadeghi, Richard J. Nichols, Andrea Vezzoli, Thomas Baumgartner, Sara Sangtarash

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsRoyal SocietyEngineering and Physical Sciences Research CouncilLeverhulme TrustCanada Foundation for InnovationYork UniversityUK Research and Innovation
KeywordsCharge (physics)Chemical physicsMoleculeChemistryNanotechnologyMaterials sciencePhysicsQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

Most studies in molecular electronics focus on altering the molecular wire backbone to tune the electrical properties of the whole junction. However, it is often overlooked that the chemical structure of the groups anchoring the molecule to the metallic electrodes influences the electronic structure of the whole system and, therefore, its conductance. We synthesised electron-accepting dithienophosphole oxide derivatives and fabricated their single-molecule junctions. We found that the anchor group has a dramatic effect on charge-transport efficiency: in our case, electron-deficient 4-pyridyl contacts suppress conductance, while electron-rich 4-thioanisole termini promote efficient transport. Our calculations show that this is due to minute changes in charge distribution, probed at the electrode interface. Our findings provide a framework for efficient molecular junction design, especially valuable for compounds with strong electron withdrawing/donating backbones.

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 score0.030
Threshold uncertainty score0.828

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)0.0010.000

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 head0.010
GPT teacher head0.223
Teacher spread0.213 · 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

Citations32
Published2023
Admission routes2
Has abstractyes

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