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Record W2765418779 · doi:10.1021/acs.langmuir.7b00929

Formation Kinetics of Mixed Self-Assembled Monolayers of Alkanethiols on GaAs(100)

2017· article· en· W2765418779 on OpenAlexafffund
Vivien Lacour, Khalid Moumanis, Walid M. Hassen, Céline Élie-Caille, Thérèse Leblois, Jan J. Dubowski

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersCentre National de la Recherche ScientifiqueNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAgence Nationale de la Recherche
KeywordsMonolayerThiolChemistrySelf-assembled monolayerKineticsFourier transform infrared spectroscopySolventEthanolChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

We report on the formation kinetics of mixed self-assembled monolayers (SAMs) comprising 16-mercaptohexadecanoic acid (MHDA) and 11-mercapto-1-undecanol (MUDO) thiols on GaAs(100) substrates. These compounds were selected for their potential in constructing highly selective and efficient architectures for biosensing applications. The molecular composition and quality of one-compound and mixed SAMs were determined by the Fourier transform infrared absorption spectroscopy measurements. The formation of enhanced-quality mixed SAMs was investigated as a function of the molecular composition of the thiol mixture and the proportion of ethanol/water solvent used during their arrangement. Furthermore, the formation of mixed SAMs has been carried out by successive immersion of MHDA SAMs in MUDO thiol solutions and MUDO SAMs in MHDA thiol solution through the process involving thiol-thiol substitution. Our results, in addition to confirming that water-ethanol-based solvents improve the packing density of single thiol monolayers, demonstrate the attractive role of water-ethanol solvents in forming superior quality mixed SAMs.

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 score0.037
Threshold uncertainty score0.262

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations25
Published2017
Admission routes2
Has abstractyes

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