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Record W2053022092 · doi:10.1021/la034499x

Effects of Surface Defects, Polycrystallinity, and Nanostructure of Self-Assembled Monolayers for Octadecanethiol Adsorbed onto Au on Wetting and Its Surface Energetic Interpretation

2003· article· en· W2053022092 on OpenAlexaff
Jun Yang, Jingmin Han, Kelvin Isaacson, Daniel Y. Kwok

Bibliographic record

VenueLangmuir · 2003
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsContact angleWettingMonolayerNanostructureAdsorptionCrystalliteSelf-assembled monolayerChemical physicsSurface tensionNanotechnologyChemistrySurface energyAdhesionMaterials scienceCrystallographyPhysical chemistryComposite materialThermodynamics

Abstract

fetched live from OpenAlex

We report low-rate dynamic contact angle data of various liquids on self-assembled monolayers (SAMs) of octadecanethiol on annealed and nonannealed gold. It was found that interpretation of solid surface tensions using contact angle data on less well-prepared polycrystalline nonannealed gold surfaces can be misleading. Our findings were supported by reflectance infrared spectra and atomic force microscopy data that the surface nanostructure, defects, and polycrystallinity can be important factors for a systematic study of wettability on SAMs in terms of surface energetics. We found that the contact angle and adhesion patterns of various liquids on SAMs of octadecanethiol adsorbed onto annealed gold substrates are consistent with recent experimental data for the relatively thick polymer-coated surfaces. The variation of surface structure in terms of surface energetics can be estimated only when a fundamental understanding of contact angles and surface tensions is known. We estimated an increase in the solid−liquid interfacial tension of 12.9 mJ/m 2 for water on octadecanethiol SAM/nonannealed Au from that of an annealed Au due to structural differences.

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 score0.008
Threshold uncertainty score0.628

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 head0.196
Teacher spread0.193 · 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

Citations31
Published2003
Admission routes1
Has abstractyes

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