Experimental investigation on foam formation through deformable porous media
Bibliographic record
Abstract
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, fL < 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".