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Record W3024131305 · doi:10.1002/cjce.23785

Experimental investigation on foam formation through deformable porous media

2020· article· en· W3024131305 on OpenAlexvenueno aff
Raffaele Graziano, Valentina Preziosi, Giovanna Tomaiuolo, Karl Braeckman, Stefano Guido

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsPorous mediumMaterials sciencePorosityWettingWork (physics)BubbleComposite materialChemical engineeringMechanical engineeringMechanicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Foam formation in porous media is a topic of growing scientific and industrial interest due to its range of applications, from daily life consumer products to oil recovery. Despite the work done so far on foams flowing through complex structures, such as rigid porous media, this subject still needs to be fully elucidated. An additional complexity to the problem arises when the porous medium is deformable, a situation which has only been faced, to our knowledge, from a modelling point of view. In this work, the investigation of foam formation in deformable porous media is carried out by using commercial sponges as a deformable porous media system, with special emphasis on the effect of confinement on foam bubble size distribution. Foam is formed by wetting the sponge with an aqueous surfactant solution and then squeezing the sponge either between two glass cover slides or between a plastic net and a cover slide. Our experimental data reveal that the latter system allows the formation of drier foams (ie, with lower liquid fraction, f L < 0.3), more similar to the ones obtained in dish‐washing applications. Moreover, the effect of sponge type, in terms of material and microstructure, on final foam is presented. Our results are of potential interest for the optimization of foams in complex structures, such as in deformable porous media.

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 score0.014
Threshold uncertainty score0.187

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 head0.033
GPT teacher head0.214
Teacher spread0.181 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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