MétaCan
Menu
Back to cohort
Record W4318328601 · doi:10.3389/fenvs.2022.873730

Tween 20 Stabilized Conventional Heavy Crude Oil-In-Water Emulsions Formed by Mechanical Homogenization

2022· article· en· W4318328601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Environmental Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsFisheries and Oceans CanadaUniversity of Northern British Columbia
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsEmulsionCreamingHomogenization (climate)TurbidityPulmonary surfactantSalinityChromatographyChemistryMixing (physics)Crude oilMaterials scienceChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

This study investigated the preparation of stable conventional heavy crude oil-in-water (O/W) emulsions by mechanical homogenization with the addition of a non-ionic surfactant, Tween-20. A four-factor, five-level central composite design was carried out to investigate the effects of four independent variables, including mixing intensity (4,000–10,000 rpm), mixing duration (5–45 min), water salinity (0–40 g/L), and the concentration of emulsifier (0.1–2.1 wt%) on the emulsion stability. Emulsion stability was determined by quantification of creaming index, turbidity change rate, and average oil droplet size. The results demonstrated that the salinity of 30 g/L, mixing intensity of 8,500 rpm, mixing duration of 35 min, and emulsifier concentration of 1.6 wt% led to the formation of the most stable emulsion.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.085
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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