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Nanoscale Transport during Liquid Film Thinning Inhibits Bubble Coalescing Behavior in Electrolyte Solutions

2023· article· en· W4386541211 on OpenAlexafffund
Bo Liu, Rogério Manica, Qingxia Liu, Zhenghe Xu, Evert Klaseboer, Qiang Yang

Bibliographic record

VenuePhysical Review Letters · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche ScientifiqueEast China University of Science and TechnologyFudan UniversityNational Natural Science Foundation of ChinaXi'an University of Science and TechnologyUniversity of South Australia
KeywordsElectrolyteBubbleCoalescence (physics)Materials scienceChemical physicsIonSurface tensionChemical engineeringMass transferNanotechnologyMechanicsElectrodeThermodynamicsChemistryChromatographyPhysical chemistryPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The long-standing puzzle of why two colliding bubbles in an electrolyte solution do not coalesce immediately upon contact is resolved. The water film between the bubbles needs to be drained out first before its rupture, i.e., coalescence. Experiments reveal clearly that the film thinning exhibits a rather sudden slowdown (around 30-50 nm), which is orders of magnitude smaller than similar experiments involving surfactants. A critical step in explaining this phenomenon is to realize that the solute concentration is different in bulk and at the surface. During thinning, this will generate an electrolyte concentration difference in film solution along the interacting region, which in turn causes a Marangoni stress to resist film thinning. We develop a film drainage model that explains the experimentally observed phenomena well. The underlying physical mechanism, that confused the scientific community for decades, is now finally 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.237
Teacher spread0.222 · 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 designObservational
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

Citations35
Published2023
Admission routes2
Has abstractyes

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