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Record W1993163728 · doi:10.1021/la904594p

Methylene Blue Incorporation into Alkanethiol SAMs on Au(111): Effect of Hydrocarbon Chain Ordering

2010· article· en· W1993163728 on OpenAlexafffund
Doris Grumelli, Lucila P. Méndez De Leo, Cecilia Bonazzola, Vlad Zamlynny, Ernesto J. Calvo, R. C. Salvarezza

Bibliographic record

VenueLangmuir · 2010
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsAcadia University
FundersSecretaría de Ciencia y Técnica, Universidad de Buenos AiresAgencia Nacional de Promoción Científica y TecnológicaNatural Sciences and Engineering Research Council of CanadaUniversidad de Buenos AiresConsejo Nacional de Investigaciones Científicas y Técnicas
KeywordsChemistryMonolayerMethylene blueSelf-assembled monolayerScanning tunneling microscopeHydrocarbonMoleculeMethyleneInfrared spectroscopyThiolElectrochemistryCrystallographyStereochemistryPhotochemistryNanotechnologyOrganic chemistryPhysical chemistryMaterials scienceCatalysisPhotocatalysis

Abstract

fetched live from OpenAlex

A detailed polarization modulation infrared reflection absorption spectroscopy, scanning tunneling microscopy, and electrochemical study on methylene blue (MB) incorporation into alkanethiolate self-assembled monolayers (SAMs) on Au(111) is reported. Results show that the amount of MB incorporated in the SAMs reaches a maximum for intermediate hydrocarbon chain lengths (C10-C12). Well-ordered SAMs of long alkanethiols (C > C12) hinder the incorporation of the MB molecules into the SAM. On the other hand, less ordered SAMs of short alkanethiols (C < or = C6) are not efficient to retain the MB incorporated through the defects. For C12 the amount of incorporated MB increases as the SAM disorder is increased. This information is essential to the design of efficient thiol-based Au vectors for transport and delivery of molecules as well as thiol-based Au devices for molecular sensing.

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 score0.021
Threshold uncertainty score0.461

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 head0.199
Teacher spread0.196 · 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

Citations54
Published2010
Admission routes2
Has abstractyes

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