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Record W4409048608 · doi:10.1021/acs.langmuir.5c00030

Liquid Droplets Impacting on a Liquid Pool Covered with a Thin Polymeric Liquid Layer

2025· article· en· W4409048608 on OpenAlexafffund
Akash Chowdhury, Sirshendu Misra, Surjyasish Mitra

Bibliographic record

VenueLangmuir · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of Canada
KeywordsLiquid liquidLayer (electronics)Chemical engineeringMaterials scienceThin layerChemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, we examine the process of filament thinning that occurs when a liquid droplet impacts a thin polymeric interfacial liquid layer floating in a water pool. We used partially cured polydimethylsiloxane solutions and polybutene gel mixed with polybutene liquid solutions in varying volumes for our experiments. The interfacial dynamics showed that the droplet drags the interfacial liquid into the pool, creating an air cavity, followed by its closer. Thereafter, a filament-thinning phase occurs. Initially, the thinning process was rapid due to crater interface retraction caused by the droplet impact. After the crater receded, a slender filament of the interfacial liquid remained, undergoing a slower thinning stage. This stage exhibited linear kinematics for Newtonian liquids (such as polydimethylsiloxane solution before the gel point transition) and exponential kinematics for viscoelastic liquids (such as polydimethylsiloxane solution during the gel point transition and polybutene solution). These findings improve our understanding of the interfacial dynamics in the liquid-liquid encapsulation process involving polymeric liquids, which are widely used commercially and in everyday life. This knowledge is vital for the process optimization and commercialization of this technique.

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 score0.304
Threshold uncertainty score0.958

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 head0.217
Teacher spread0.211 · 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

Citations6
Published2025
Admission routes2
Has abstractyes

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