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Record W2027557128 · doi:10.1080/15567030802462622

Soapnut Extract as a Natural Surfactant for Enhanced Oil Recovery

2009· article· en· W2027557128 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.

Bibliographic record

VenueEnergy Sources Part A Recovery Utilization and Environmental Effects · 2009
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPulmonary surfactantSurface tensionEnhanced oil recoveryPetroleum engineeringFossil fuelPulp and paper industryChemistryEnvironmental scienceChemical engineeringMaterials scienceOrganic chemistryGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract Primary and secondary recovery of oil from oil reservoirs contributes approximately 30% of the total oil in place in most oil reservoirs. Since the current economic development is heavily dependent on fossil fuel, increasing the amount of oil recovery is given considerable attention. One of the most widely used methods for enhanced oil recovery is the application of various types of surfactants in order to reduce the oil-water interfacial tension. However, the surfactants being used today are usually synthetic chemicals and polymers, which are expensive, linked to fossil fuels, and detrimental to the environment. In thisarticle, a natural surfactant prepared from the pericarp shell of fruit (Sapindus mukurossi) collected from a naturally available tree was used to reduce oil-water interfacial tension. The effect of surfactant concentration with 1%, 2%, 4%, 8%, and 12% was investigated. The effect of heat on interfacial tension was also studied. The experimental results showed that extract has a great potential to be used as a surfactant for enhanced oil recovery schemes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.204
Teacher spread0.198 · 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