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Record W2021537293 · doi:10.1002/cjce.21613

Micellar enhanced ultrafiltration: A comparative study

2011· article· en· W2021537293 on OpenAlexvenueaboutno aff
S. Jadhav, Kumudini V. Marathe

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPolysulfoneMembraneUltrafiltration (renal)MicelleChemical engineeringChromatographyMicroporous materialMaterials sciencePhase inversionAmmonium bromideChemistryAqueous solutionPulmonary surfactantComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract A pilot plant study was carried out to evaluate the performance of continuous cross flow micellar enhanced ultrafiltration (MEUF) method for the simultaneous separation of phenol and ortho‐cresol from the aqueous phase using a cationic surfactant, cetyltrimethyl ammonium bromide. The membranes used in this study are polysulfone tubular hollow fibre membrane with 6 kDa and a ceramic membrane having pore diameter 50 nm with an active layer of microporous zirconia (ZrO 2 ) supported on α‐alumina, respectively. The work includes comparing the effectiveness of membranes based on pollutants structural parameters, membrane properties and membrane modules. The characteristic parameters such as the distribution coefficient ( D ), micelle binding constant ( K p ) and micelle loading ( L m ) were obtained at optimal experimental conditions. High initial rejections were observed (above 95%) for both the membranes and the permeate flux remained almost constant at 17 LMH. It was found that the polysulfone material membrane suited better for MEUF with higher and steady rejections over a period of time. High micelle leakage was observed in ceramic membrane. The energy consumption for hollow fibre polysulfone membrane was less than the ceramic membrane. © 2011 Canadian Society for Chemical Engineering

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 score0.030
Threshold uncertainty score0.733

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)0.0010.000

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 head0.025
GPT teacher head0.213
Teacher spread0.188 · 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

Citations13
Published2011
Admission routes2
Has abstractyes

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