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Record W2141986876 · doi:10.1139/s08-038

Protein fouling of ultrafiltration membranes — investigation of several factors relevant for tertiary wastewater treatment

2008· article· en· W2141986876 on OpenAlexafffundvenue
Jens Haberkamp, Mathias Ernst, Gladys Makdissy, Peter M. Huck, Martin Jekel

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaTechnische Universität BerlinUniversity of WaterlooTechnische Universiteit Eindhoven
KeywordsFoulingMembraneMembrane foulingUltrafiltration (renal)Isoelectric pointMicrofiltrationChemical engineeringZeta potentialChromatographyNanofiltrationChemistryBiofoulingMaterials scienceOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

The fouling of two different ultrafiltration membranes by solutions of three globular proteins with different molecular weights and isoelectric points was investigated at low concentrations relevant to secondary effluents (10 mg·L –1 ). The results reveal the increased fouling potential of macromolecules being small enough to enter the membrane pores due to the resulting blockage and (or) constriction of the pores, compared to a moderate increase of the filtration resistance due to gel layer formation by larger compounds. Differences between the applied membranes in terms of fouling behaviour, protein retention, as well as attachment of macromolecules to membrane surface and pore walls demonstrate the impact of pore size distribution, zeta potential, surface roughness, and hydrophobicity of the membranes on the fouling mechanism. Comparative ultrafiltration experiments using two different pumps indicate the influence of type and dimensioning of the pumping system on membrane fouling due to structural changes of solutes by pump-induced shear forces.

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 score0.059
Threshold uncertainty score0.286

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 head0.013
GPT teacher head0.197
Teacher spread0.184 · 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

Citations29
Published2008
Admission routes3
Has abstractyes

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