MétaCan
Menu
Back to cohort
Record W2591409729 · doi:10.1080/09593330.2017.1297490

The effect of contaminated particle sphericity and size on membrane fouling in cross flow ultrafiltration

2017· article· en· W2591409729 on OpenAlexafffund
Amira Abdelrasoul, Huu Doan, Ali Lohi, Chil‐Hung Cheng

Bibliographic record

VenueEnvironmental Technology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMembraneMembrane foulingFoulingUltrafiltration (renal)Particle sizePolysulfoneChromatographyDispersityChemical engineeringChemistryAmmonium bromideParticle (ecology)SphericityMaterials sciencePulmonary surfactantComposite materialPolymer chemistry

Abstract

fetched live from OpenAlex

The goal of the current research was to critically examine the role of the shape and the size of contaminated particles for an accurate prediction of membrane fouling phenomenon. Polycarbonate flat membranes (PC) with uniform pore sizes of 0.05 and 0.1 µm, in addition to Polysulfone membranes (PS) with molecular weight cut off (MWCO) of 60,000 kDa 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 experimental results established that the sphericity of contaminated particles had a critical effect on the membrane fouling and prediction of transmembrane pressure and total mass of fouling using the homogenous pore size membranes. The Cetyltrimethyl Ammonium Bromide (CTAB) was implemented as a cationic surfactant so as to facilitate the aggregation of latex particles. The results obtained indicated that the particle size 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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.699

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 studies0.0000.002
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 head0.242
Teacher spread0.237 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental TechnologySame topicMembrane Separation TechnologiesFrench-language works237,207