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Record W2083859288 · doi:10.1002/cjce.5450850502

Initial Deposition of Colloidal Particles on a Rough Nanofiltration Membrane

2007· article· en· W2083859288 on OpenAlexaffvenue
Tania Rizwan, Subir Bhattacharjee

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNanofiltrationMembraneReverse osmosisParticle depositionSurface roughnessChemical engineeringColloidMembrane foulingDeposition (geology)Cross-flow filtrationPermeationFiltration (mathematics)FoulingParticle (ecology)Materials scienceDragSurface finishUltrafiltration (renal)PolystyreneChemistryChromatographyComposite materialPolymerMechanicsGeology

Abstract

fetched live from OpenAlex

Abstract The initial rate of colloid deposition onto semi‐permeable membranes is largely controlled by the coupled influence of permeation drag and particle‐membrane colloidal interactions. Recent studies show that the particle‐membrane interactions are subject to immense local variations due to the inherent morphological heterogeneity (roughness) of reverse osmosis (RO) and nanofiltration (NF) membranes. This experimental investigation reports the effect of membrane roughness on the initial deposition of polystyrene latex particles on a rough NF membrane during cross flow membrane filtration under different operating pressures and solution chemistries. Atomic force microscopy was used to characterize the roughness of the membrane and observe the structure of particle deposits. At the initial stages of fouling, the AFM images show that more particles preferentially accumulate near the “peaks” than in the “valleys” of the rough NF membrane surface.

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 score0.025
Threshold uncertainty score0.196

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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

Citations11
Published2007
Admission routes2
Has abstractyes

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