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Record W1982264852 · doi:10.1021/la052776p

Chemical Force Titrations of Functionalized Si(111) Surfaces

2006· article· en· W1982264852 on OpenAlexafffund
Tadesse Z. Mengistu, Vishya Goel, J. Hugh Horton, Sylvie Morin

Bibliographic record

VenueLangmuir · 2006
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsQueen's UniversityYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonolayerTitrationChemistryAlkylX-ray photoelectron spectroscopyHydrosilylationSelf-assembled monolayerAmine gas treatingSurface modificationAnalytical Chemistry (journal)SiliconPolymer chemistryOrganic chemistryChemical engineeringPhysical chemistryCatalysis

Abstract

fetched live from OpenAlex

Chemical force titrations-plots of the adhesive force between an atomic force microscope tip and sample as a function of pH-were acquired on alkyl monolayer-derivatized Si(111) surfaces. Gold-coated AFM tips modified with thioalkanoic acid self-assembled monolayers (SAM) were employed. Alkyl monolayer-derivatized Si(111) surfaces terminated with methyl, carboxyl, and amine groups were produced via hydrosilylation reactions between 1-alkene reagents and H-terminated silicon. The functionalized surfaces were characterized using standard surface science techniques (AFM, FTIR, and XPS). Titration of the methyl-terminated surface using the modified (carboxyl-terminated) atomic force microscope tip resulted in a small pH-independent hydrophobic interaction. Titration of the amine-terminated surface using the same tip resulted in the determination of a surface pKa of 5.8 for the amine from the pH value from the maximum in the force titration curve. A pK(1/2) of 4.3 was determined for the carboxyl-terminated Si(111) in a similar way. These results will be discussed in relation to the modified Si(111) surface chemistry and organic layer structure, as well as with respect to existing results on Au surfaces modified with SAMs bearing the same functional groups.

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 score0.305
Threshold uncertainty score0.336

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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

Citations20
Published2006
Admission routes2
Has abstractyes

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