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Surface Tension Determination From Regression Between Hydrostatic Pressure Variation and Interfacial Curvature

2025· article· en· W4410270804 on OpenAlexaboutno aff
Carrie E. Perlman, Bret A. Brandner, Stephen B. Hall

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHydrostatic pressureSurface tensionCurvatureVariation (astronomy)Surface (topology)Tension (geology)Hydrostatic equilibriumRegression analysisRegressionComposite materialMechanicsGeometryStatisticsThermodynamicsPhysicsMaterials scienceCompression (physics)

Abstract

fetched live from OpenAlex

Abstract RATIONALE: Gravity deforms gas-liquid interfaces such that interfacial curvature varies with height. Surface tension, T, can be determined from the variation in curvature. The most widely-used existing method for determining T from curvature variation is axisymmetric drop shape analysis (ADSA), which requires complex parametric integration and optimization. Here, we test an alternative and intuitive T-determination method. At any point along the interface, the Young-Laplace relation specifies that Δ P = T (k 1 + k 2 ), where Δ P is pressure drop across the interface and k 1 and k 2 are the principle interfacial curvatures. Thus if T were constant along the interface, then T could be determined as the slope of the linear regression between interfacial pressure drop and curvature. METHODS: We image saline droplets or captive bubbles with deposited monlayers of dipalmitoyl phosphatidylcholine. We identify edge pixels by Canny edge detection and sequence the edge pixels to generate an edge curve. At eight equally-spaced altitudes along the edge curve, we determine Δ P by hydrostatic offset from Δ P 0 at the top of the curve and use tangents constructed from interfacial points separated by arclength interval ds K to determine k 1 + k 2. Plotting Δ P – Δ P 0 vs. k 1 + k 2 yields a line with a slope equal to T (Figure). Values of T are exquisitely sensitive to interfacial curvature and the T value obtained from a single regression is not reliable. Thus, we run repeated regressions for a range of ds K values. From the multiple regression runs, we retain those with coefficients of determination, R2, exceeding a threshold value. We calculate T as the average of the slopes of the retained regression runs. For DPPC films compressed to different extents, we compare T values obtained by this regression method to those obtained by software (A.W. Neumann, Univ. of Toronto) that implements ADSA. RESULTS: Over a T range of 2-70 mN/m, the difference between T values determined by the alternative methods averages 0.48 ± 0.31 (SD) mN/m and is, at most, 1.05 mN/m. CONCLUSION: Linear regression between Δ P and interfacial curvature can yield accurate surface tension values.

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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)0.001
metaresearch head score (Gemma)0.004
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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.321
Teacher spread0.302 · 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
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

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