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Record W2972972992 · doi:10.3139/113.110638

Pseudo-Gemini Biosurfactants with CO<sub>2</sub> Switchability for Enhanced Oil Recovery (EOR)

2019· article· en· W2972972992 on OpenAlexaffabout
Yi Lu, Yeling Zhu, Zhenghe Xu, Qingxia Liu

Bibliographic record

VenueTenside Surfactants Detergents · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnhanced oil recoveryChemistryPetroleumExtraction (chemistry)Chemical engineeringPetroleum industryPulp and paper industryPetroleum engineeringChromatographyOrganic chemistryEnvironmental engineeringEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Abstract Novel biosurfactants with high performance are always needed in the petroleum industry for environmental sustainability. Herein, we developed a series of biosurfactants to enhance the heavy oil recovery from Canadian oil sands. Pseudo-Gemini biosurfactants were designed to be interfacially active and CO2 switchable. The strong interfacial activity of biosurfactants promotes the liberation of heavy oil from solid substrates, which was demonstrated by the liberation visualization cell. On the other hand, the separation of heavy oil from extraction fluid was also facilitated by activating the CO2 switchability of biosurfactants. Since the efficiencies in both the heavy oil liberation and the oil-water separation were improved, the total heavy oil recovery could be significantly enhanced. Therefore, these biosurfactants are believed to be promising in the application of enhanced oil recovery from oil sands ore.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.008
GPT teacher head0.217
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

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

Citations13
Published2019
Admission routes2
Has abstractyes

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