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Record W3025008733 · doi:10.1149/ma2020-01452542mtgabs

Stereostructural Effects in the Electrochemical Properties of Self-Assembled Monolayers

2020· article· en· W3025008733 on OpenAlexaff
Antonella Badia, Fadwa Ben Amara, Éric R. Dionne, Christian Pellerin

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMonolayerSelf-assembled monolayervan der Waals forceChemistryAlkylElectrochemistryFerroceneMethyleneCrystallographyMoleculeStereochemistryNanotechnologyMaterials scienceElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Large-area metal/molecule/metal junctions based on self-assembled monolayers (SAMs) of organothiolates are of interest for the investigation of charge transport across ultrathin organic films and the fabrication of molecular electronic devices such as diodes and switches. Several reports demonstrate an odd-even dependency in the rate of charge transport across SAMs of the redox-active ferrocenylalkanethiolates Fc(CH2) n S1, 2 and insulating n-alkanethiolates CH3(CH2) n S3-6. This parity effect is attributed to small changes in the orientation of the terminal functional group (ferrocene or methyl) and in the alkyl chain packing in SAMs consisting of an odd versus even number of methylene repeat units (n odd or n even). These odd-even differences ultimately impact device performance. For instance, junctions comprising gold-supported Fc(CH2) n S SAMs (Figure 1) of n even present lower leakage currents and rectify current, while those of n odd do not.1, 2 We have sought experimental evidence for odd-even distinctions in the molecular organization of these SAMs using electrochemistry,7 infrared reflection-absorption spectroscopy (IRRAS), contact angle goniometry, and surface stress measurements8. The findings of these investigations will be presented in this talk. References: 1. Nerngchamnong, N.; Yuan, L.; Qi, D.-C.; Li, J.; Thompson, D.; Nijhuis, C. A., The Role of van der Waals Forces in the Performance of Molecular Diodes. Nat. Nanotechnol. 2013, 8, 113-118. 2. Yuan, L.; Thompson, D.; Cao, L.; Nerngchangnong, N.; Nijhuis, C. A., One Carbon Matters: The Origin and Reversal of Odd–Even Effects in Molecular Diodes with Self-Assembled Monolayers of Ferrocenyl-Alkanethiolates. J. Phys. Chem. C 2015, 119, 17910-17919. 3. Baghbanzadeh, M.; Simeone, F. C.; Bowers, C. M.; Liao, K.-C.; Thuo, M.; Baghbanzadeh, M.; Miller, M. S.; Carmichael, T. B.; Whitesides, G. M., Odd–Even Effects in Charge Transport across n-Alkanethiolate-Based SAMs. J. Am. Chem. Soc. 2014, 136, 16919-16925. 4. Thuo, M. M.; Reus, W. F.; Nijhuis, C. A.; Barber, J. R.; Kim, C.; Schulz, M. D.; Whitesides, G. M., Odd−Even Effects in Charge Transport across Self-Assembled Monolayers. J. Am. Chem. Soc. 2011, 133, 2962-2975. 5. Chen, J.; Giroux, T. J.; Nguyen, Y.; Kadoma, A. A.; Chang, B. S.; VanVeller, B.; Thuo, M. M., Understanding Interface (Odd–Even) Effects in Charge Tunneling using a Polished EGaIn Electrode. Phys. Chem. Chem. Phys. 2018, 20, 4864-4878. 6. Jiang, L.; Sangeeth, C. S. S.; Nijhuis, C. A., The Origin of the Odd–Even Effect in the Tunneling Rates across EGaIn Junctions with Self-Assembled Monolayers (SAMs) of n-Alkanethiolates. J. Am. Chem. Soc. 2015, 137, 10659-10667. 7. Feng, Y.; Dionne, E. R.; Toader, V.; Beaudoin, G.; Badia, A., Odd–Even Effects in Electroactive Self-Assembled Monolayers Investigated by Electrochemical Surface Plasmon Resonance and Impedance Spectroscopy. J. Phys. Chem. C 2017, 121, 24626-24640. 8. Dionne, E. R.; Dip, C.; Toader, V.; Badia, A., Micromechanical Redox Actuation by Self-Assembled Monolayers of Ferrocenylalkanethiolates: Evens Push More Than Odds. J. Am. Chem. Soc. 2018, 140, 10063-10066. Figure 1

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)0.001
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
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.188
Teacher spread0.181 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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