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Record W2319578185 · doi:10.1002/cjce.22474

Effect of surfactant and polymer on the characteristics of aphron‐containing fluids

2016· article· en· W2319578185 on OpenAlexvenueno aff
Milad Arabloo, Mojtaba P. Shahri

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsRheologyBiopolymerMaterials sciencePulmonary surfactantFiltration (mathematics)PolymerDrilling fluidColloidChemical engineeringComposite materialMetallurgyDrilling

Abstract

fetched live from OpenAlex

Abstract Colloidal gas aphron (CGA) consists of spherical gas microbubbles with diameters ranging from 10–100 μm. Unlike regular foams, CGAs have a thin aqueous protective shell which makes them useful as a component of drilling fluid for practical applications. This paper reports on physicochemical properties of fluids containing CGAs. To this end, various laboratory tests of CGA generation, microscopic visualization, density measurement, API filtration loss, and rheological characterization with varying concentrations of biopolymer and surfactant were carried out. Three rheological models, namely the Bingham plastic, Casson, and Power‐law, were also employed to quantitatively describe the shear flow behaviour of CGA‐based fluids. Moreover, the CGAs performance as a filtrate‐reducing component was examined thorough a standard API filter press. The results of this study are helpful for better understanding the characteristics of CGA‐based fluids.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.004
GPT teacher head0.159
Teacher spread0.155 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2016
Admission routes1
Has abstractyes

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