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Record W4403522725 · doi:10.1021/acs.jpcb.4c03019

Surface Tension of Two Near-Ideal Binary Liquid Mixtures and the Influence of Adjacent Vapors

2024· article· en· W4403522725 on OpenAlexaff
A. Visco, Anisha Pawar, Nathaniel A. Schambach, Nabin Thapa, Yi Y. Zuo, A. W. Neumann, Zdenka Policova, Joel L. Plawsky, Shekhar Garde, Anthony E. Smart, William V. Meyer, Alexander I. Belgovskiy, J. Adin Mann, Elizabeth K. Mann

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsUniversity of Toronto
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsDivision of Materials Research
KeywordsSurface tensionPentaneChemistryDrop (telecommunication)Maximum bubble pressure methodGibbs isothermBinary numberThermodynamicsScatteringCapillary numberLight scatteringSessile drop techniqueMaterials scienceOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

The measured surface tension of a binary liquid is found to depend strongly on the constituents of the adjacent vapor and on whether equilibrium has been achieved, giving insight into the complex interfacial configuration. This dependence is quantified by three techniques that offer complementary insights: surface tension measurements with a constrained sessile drop surrounded by different vapors, surface tension measurements by surface light scattering spectroscopy in a sealed cell at equilibrium, and molecular dynamics simulations of the equilibrium surface tension and excess surface concentration. Ensuring homogeneity of the binary liquid, which is essential for surface light scattering, was found to be nontrivial and was assured by high-sensitivity Schlieren imaging. Two pairs of liquids, n -pentane with 2-methylpentane and n -pentane with n -hexane, were investigated. The first pair was motivated by the observed improvement in the effectiveness of binary fluids versus a single constituent in wickless heat pipes studied in microgravity. The second pair was used for comparison. Experimental evaluation of different volume fractions of the two liquids showed strong dependence of surface tension on the relative concentration of different molecules near the interfacial region. For the above pairs of liquids, which appear to form ideal mixtures in bulk, we present sufficiently precise surface tension measurements to indicate unexpectedly complex behaviors at interfaces.

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 score0.087
Threshold uncertainty score0.174

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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