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Record W2904092250 · doi:10.18178/ijcea.2018.9.1.690

Yield Stress of Alcoflood and Xanthan Polymers Solutions and Their Emulsions with Crude Oil

2018· article· en· W2904092250 on OpenAlexaff
Mamdouh T. Ghannam, Abdulrazag Y. Zekri, Mohamed Y.E. Selim, Nabil Esmail

Bibliographic record

VenueInternational Journal of Chemical Engineering and Applications · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPolysaccharides Composition and Applications
Canadian institutionsConcordia UniversityUniversité de MontréalUniversity of Saskatchewan
Fundersnot available
KeywordsXanthan gumYield (engineering)PolymerStress (linguistics)ChemistryChemical engineeringPolymer scienceMaterials scienceRheologyOrganic chemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

Experimental investigation of yield stress measurements for Alcoflood and Xanthan polymers solutions and their emulsions with crude oil were studied by using RheoStress RS100 under controlled stress mode. The yield stress study was investigated over a wide range of polymer concentration, two concentrations of crude oil, and different type of polymers. For the same polymer concentration of 104 ppm, the aqueous solution of AF1285 reported a higher resistance to flow than the aqueous solution of AF1235. The apparent yield stresses for Alcoflood aqueous solutions and their crude oil emulsions are increased by polymer concentration. Higher concentration of Xanthan aqueous solutions showed higher ascending and descending rheograms. For Xanthan concentration of 1000 ppm, very low yield stresses are found for all the Xanthan solutions and oil emulsions. For 75% crude oil emulsions, almost similar rheograms behaviors are reported for all the tested Xanthan concentrations. The yield stress analysis showed that the higher Xanthan concentration leads to a higher resistance for both of aqueous solutions and emulsions to startup.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.138

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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