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Record W4386697545 · doi:10.5539/apr.v15n2p98

Exploring Foam Drainage in Fiber-Foam: A Review

2023· review· en· W4386697545 on OpenAlexvenueno aff
Ahmad M. Al-Qararah

Bibliographic record

VenueApplied Physics Research · 2023
Typereview
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsDrainageMaterials scienceFiberComposite materialEcology

Abstract

fetched live from OpenAlex

The foam drainage is extremely important in many situations where fiber foams are used. Enhancing wet foam stability requires a complete understanding of the mechanisms and factors affecting wet foam drainage. Investigation of the drainage behavior of fiber foams has been studied in this review. The mechanics behind fiber foam drainage are discussed in detail, along with the influence of surfactant concentration, fiber consistency, and other variables. It also investigated adding additives, such as chemi-thermo-mechanical pulp (CTMP), affects foam drainage. Highlighting the most recent developments in experimental and theoretical methods for describing and forecasting foam drainage behavior are presented. This review acts as a reference to offer useful understanding of the essential factors of foam drainage in fiber foams solution.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.008

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 head0.701
GPT teacher head0.504
Teacher spread0.197 · 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.

Study designNot applicable
Domainnot available
GenreReview

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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