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Record W2004187616 · doi:10.1021/jp907405h

Water Structure at Solid Surfaces of Varying Hydrophobicity

2009· article· en· W2004187616 on OpenAlexafffund
Travis G. Trudeau, Kailash C. Jena, Dennis K. Hore

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWettingChemical physicsHydrogen bondSolid surfaceContact angleMaterials scienceMolecular dynamicsWetting transitionCrystallographyChemistryMoleculeComputational chemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The structure of liquid water at solid surfaces with tunable hydrophobicity has been examined by molecular dynamics simulation. Methods of analysis include water density profiles, angular distributions, tilt and twist order parameters, and hydrogen-bonding coordination. It was found that interfacial water structures could be classified according to two hydrophobic regimes, a nonwetting structure and a semi-wetting structure. A smooth transition between the two occurs at surfaces with a contact angle around 130°. The nonwetting regime is characterized by water immediately adjacent to the interface oriented such that hydrogens are directed toward the surface. The semiwetting regime has water oriented in the plane of the interface. We propose that the emergence of the wetting-type order is strongly dependent on the density profile across the interfacial region. Regions of low density, flanked by high-density areas, present fewer hydrogen bonding opportunities than are found in more dense regions. Our findings are able to provide an explanation for experimental observations that, in surface-sensitive nonlinear vibrational spectroscopy, solid surfaces must be extremely hydrophobic to display spectroscopic signatures of uncoupled OH stretching modes.

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 score0.011
Threshold uncertainty score0.312

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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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

Citations47
Published2009
Admission routes2
Has abstractyes

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