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Interaction of Amphiphilic Polymers with Medium-Chain Fatty Alcohols to Enhance Rheological Performance and Mobility Control Ability

2019· article· en· W2952183011 on OpenAlexaff
Yao Lu, Ziyu Meng, Kai Gao, Jirui Hou, Hairong Wu

Bibliographic record

VenueEnergy & Fuels · 2019
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsRheologyViscoelasticityPolymerChemical engineeringBrineChemistryIntermolecular forceFatty alcoholPolyacrylamideAmphiphileMaterials sciencePolymer chemistryOrganic chemistryComposite materialMoleculeCopolymer

Abstract

fetched live from OpenAlex

To improve the efficiency of mobility control for polymer flooding, a novel thickening system was formed by adding medium-chain fatty alcohols to hydrophobically modified polyacrylamide (HMPAM) brine solutions. The interaction of HMPAM with fatty alcohols was investigated in terms of rheology properties, aggregate microstructure, and mobility control ability. The results show that n-octanol (C8OH) generates the best synergistic behavior with HMPAM compared to other fatty alcohols. More hydrophobic groups initially existing in either intramolecular or intermolecular association reassemble into larger aggregation structures due to the presence of C8OH. 750 mg·L–1 C8OH could lead to an increase of viscosity by four times as that of individual HMPAM at low polymer concentration (1000 mg·L–1). Owing to the enhancement of the association structure, the HMPAM + C8OH system displays stronger shear-thinning behavior, higher viscoelasticity, and better resistance to salt and temperature. Polymer–brine displacement tests indicate that compared to other systems HMPAM + C8OH exhibits higher effective viscosity and moderate polymer retention in porous media, showing promising applicability in efficient mobility control for low-concentration polymer flooding.

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 score0.050
Threshold uncertainty score0.691

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 head0.224
Teacher spread0.219 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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