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Record W2430413118 · doi:10.1002/mame.201600230

Cationic Hydrolytically Degradable Flocculants with Enhanced Water Recovery for Oil Sands Tailings Remediation

2016· article· en· W2430413118 on OpenAlex
Thomas R. Rooney, Sarang P. Gumfekar, João B. P. Soares, Robin A. Hutchinson

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

VenueMacromolecular Materials and Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of AlbertaQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCationic polymerizationMaterials sciencePolycaprolactoneFlocculationChemical engineeringPolyesterEmulsionPolymerizationTailingsHydrolysisMethacrylateEmulsion polymerizationPolymerPolymer chemistryOrganic chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

Micellar radical polymerization of a short‐chain polyester macromonomer, polycaprolactone choline iodide ester methacrylate (PCL n ChMA), is used to produce a new cationic flocculant that becomes more hydrophobic in response to hydrolytic degradation. The cationic tips of the comb‐like poly(PCL 3 ChMA) accelerate the settling rate of oil sands tailings, while partial hydrolysis of the polyester grafts reveals the hydrophobic segments that reduce capillary suction time by 30%. This technology combines the material properties of polyesters with the productivity of radical polymerization to make dual functional flocculants with characteristics that can be easily tuned to control flocculation performance, such as polymeric cation density, hydrophobic content, and polymer architecture. image

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 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 score0.266
Threshold uncertainty score0.610

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 head0.003
GPT teacher head0.170
Teacher spread0.167 · 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