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Record W2550099664 · doi:10.1111/wej.12213

Contaminated particle characteristics influence on membrane fouling

2016· article· en· W2550099664 on OpenAlexafffund
Amira Abdelrasoul, Huu Doan, Ali Lohi, Chil‐Hung Cheng

Bibliographic record

VenueWater and Environment Journal · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFoulingPolysulfoneMembraneDispersityMembrane foulingUltrafiltration (renal)SphericityParticle (ecology)Materials scienceChemical engineeringParticle sizeChromatographyChemistryComposite materialPolymer chemistryGeology

Abstract

fetched live from OpenAlex

Abstract The goal of the current research study was to critically examine the role of the shape of contaminated particles for an accurate prediction of membrane fouling phenomenon. Polycarbonate flat membranes with uniform pore sizes of 0.05 µm and 0.1 µm, in addition to Polysulfone membranes with molecular weight cut of 60,000, were used under a constant feed flow rate and a cross‐flow mode in ultrafiltration of a latex paint solution featuring a wide range of particle size distribution. The current mathematical model was developed to illustrate the effect of irregularity and polydispersity of latex particles on the mass of fouling and irreversible fouling on membranes. The results obtained indicate that the sphericity of contaminated particles had a significant influence on fouling potential at different aggregation levels.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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