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Record W2127588148 · doi:10.1002/app.22993

Effects of preparation conditions on the surface modification and performance of polyethersulfone ultrafiltration membranes

2006· article· en· W2127588148 on OpenAlexaff
Daniella B. Mosqueda‐Jimenez, Roberto Narbaitz, Takeshi Matsuura

Bibliographic record

VenueJournal of Applied Polymer Science · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMembraneContact anglePhase inversionPermeationUltrafiltration (renal)Chemical engineeringPolyvinylpyrrolidoneFiltration (mathematics)Materials scienceFoulingSolventSurface modificationPolymerCopolymerPolymer chemistryEvaporationX-ray photoelectron spectroscopyChemistryChromatographyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The impact that some membrane preparation steps had on ultrafiltration (UF) membrane characteristics and performance was studied. Polyethersulfone (PES) was employed as base polymer, while N ‐methyl pyrrolidone (NMP) was used as a solvent, and polyvinylpyrrolidone (PVP) was used as a nonsolvent pore‐forming additive. The manufacturing variables studied were solvent evaporation time and membrane surface modification, using a fluorine‐based copolymer referred to as surface‐modifying macromolecule (SMM). The flat sheet membranes, prepared via phase inversion, were characterized using solute transport data, X‐ray photoelectron spectroscopy (XPS), and contact angle measurements. Membrane performance was evaluated via filtration test protocol that included a 6‐day filtration of concentrated river water. The flux reduction with time was modeled using single and dual mechanisms of fouling. The pore blockage/cake filtration model described better the behavior of the permeation rate along the experiments. Increasing the solvent evaporation time decreased the size of the pores and the permeation rate. However, it did not significantly affect the removal of the organic compounds naturally present in the river water used as feed. XPS and contact angle measurements proved that the short evaporation periods did not allow enough SMM migration to the surface to provoke a significant effect on the membrane performance. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci, 2006

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)0.001
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 score0.023
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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
Published2006
Admission routes1
Has abstractyes

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