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Record W2315296413 · doi:10.1021/jp5040745

Electrochemical Identification of Molecular Heterogeneity in Binary Redox Self-Assembled Monolayers on Gold

2014· article· en· W2315296413 on OpenAlexaff
Huihui Tian, Debo Xiang, Huibo Shao, Hua‐Zhong Yu

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRedoxMonolayerIntermolecular forceChemistryElectrochemistrySelf-assembled monolayerFerroceneElectrodeChemical physicsCrystallographyMoleculePhysical chemistryInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Ferrocenylalkanethiols are excellent probes to study the structure and properties of mixed self-assembled monolayers (SAMs) on gold; in this paper, the molecular heterogeneity in binary redox-active SAMs on gold prepared via postassembly exchange and coadsorption processes is revealed electrochemically. The exchange process of single-component 11-ferrocenyl-1-undecanethiolate SAMs on gold (FcC11S–Au) with 1-undecanethiol (C11SH) is first investigated; it is shown that a single pair of redox peaks can be obtained upon prolonged immersion in C11SH/ethanol solution. For the coadsorption of FcC11SH and C11SH on gold, the splitting of the redox peak diminishes when the molar ratio FcC11SH decreases to <10%. The binary FcC11S-/C11S–Au SAMs with low surface density of ferrocene moieties prepared by these two methods are compared by fitting the cyclic voltammograms (CVs) in considering their intermolecular interactions. The essentially different distributions of the redox centers in these diluted binary SAMs, as indicated by the varied formal potentials and intermolecular repulsion forces, provide further insights in understanding molecular self-assembly processes on the surface.

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 score0.004
Threshold uncertainty score0.308

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)0.0000.000

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 head0.204
Teacher spread0.201 · 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

Citations30
Published2014
Admission routes1
Has abstractyes

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