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Record W4385312106 · doi:10.5539/apr.v15n2p18

Revolutionizing Foam Physics: A Cutting-Edge Drainage Equation Model for Wet Foam

2023· article· en· W4385312106 on OpenAlexvenueno aff
Ahmad M. Al-Qararah

Bibliographic record

VenueApplied Physics Research · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)DrainageBubbleMechanicsField (mathematics)Materials sciencePhysicsMathematicsThermodynamicsEcology

Abstract

fetched live from OpenAlex

Foam physics is a field of study that scientists and researchers are interested in due to the vast range of uses, e.g. foam-foamed materials, oil extraction, and food processing. This study proposes a new equation for the drainage of wet foam that could add to the science of foam. To improve our comprehension of the intricate behaviour of wet foam, this model expands on a theoretical derivation. The usage of a bubble size formula that was proposed using the experimental data is one of the model's distinguishing characteristics. The size of foam bubbles can be predicted using this formula more precisely. A thorough derivation of the theoretical model is provided in the paper. Finally, this work presents a novel wet foam drainage model that has the potential to enhance the field of foam physics. The results of this work have important implications for industries. Therefore, more study is needed for developing a two dimensional drainage equation.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.636
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.258
GPT teacher head0.406
Teacher spread0.148 · 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 designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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