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Record W2028792177 · doi:10.1371/journal.pone.0008281

Tension at the Surface: Which Phase Is More Important, Liquid or Vapor?

2009· article· en· W2028792177 on OpenAlexafffund
Andrew Prpich, Yuebiao Sheng, Wei Wang, M.E. Biswas, P. Chen

Bibliographic record

VenuePLoS ONE · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSurface tensionTension (geology)Materials scienceMechanicsPhysicsThermodynamicsComposite materialCompression (physics)

Abstract

fetched live from OpenAlex

Tension at the surface is a most fundamental physicochemical property of a liquid surface. The concept of surface tension has widespread implications in numerous natural, engineering and biomedical processes. Research to date has been largely focused on the liquid side; little attention has been paid to the vapor--the other side of the surface, despite over 100 years of study. However, the question remains as to whether the vapor plays any role, and to what extent it affects the surface tension of the liquid. Here we show a systematic study of the effect of vapor on the surface tension and in particular, a surprising observation that the vapor, not the liquid, plays a dominant role in determining the surface tension of a range of common volatile organic solutions. This is in stark contrast to results of common surfactants where the concentration in the liquid plays the major role. We further confirmed our results with a modified adsorption isotherm and molecular dynamics simulations, where highly structured, hydrogen bonded networks, and in particular a solute depletion layer just beneath the Gibbs dividing surface, were revealed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.037
GPT teacher head0.293
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations11
Published2009
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicSpectroscopy and Quantum Chemical StudiesFrench-language works237,207