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Record W2079224521 · doi:10.1002/app.31739

Key factors affecting the manufacture of hydrophobic ultrafiltration membranes for surface water treatment

2010· article· en· W2079224521 on OpenAlexaff
Đặng Thị Thanh Huyền, Dipak Rana, Roberto Narbaitz, Takeshi Matsuura

Bibliographic record

VenueJournal of Applied Polymer Science · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsContact angleMembraneUltrafiltration (renal)Materials scienceWettingPermeationCastingChemical engineeringFourier transform infrared spectroscopyDifferential scanning calorimetryScanning electron microscopeChromatographyPolymer chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract As part of the development of poly(ether sulfone) (PES) membranes whose surface is modified by the incorporation of a newly synthesized hydrophobic surface modifying macromolecule (nSMM) additive, this study investigates the impact of four key membrane preparation factors. They are concentration of PES, concentration of nSMM, casting thickness, and casting speed. The synthesis and characterizations of nSMM by nuclear magnetic resonance, gel permeation chromatography, differential scanning calorimeter, and elemental analysis have been presented. The changes in characteristics and performance of the membranes have been evaluated via Fourier transform infrared spectroscopy, contact angle analysis, scanning electron microscopy, and solute transport tests. The addition of 0.5 wt % of nSMM increased the contact angle of PES membranes by 20°; however, higher nSMM concentrations did not increase the hydrophobicity any further. Only the additive concentration had a statistically significant impact on flux reduction and dissolved organic carbon rejection. Even though other factors such as membrane thickness may alter the pore characteristics, their effect on membrane performance was marginal. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010

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 score0.002
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.238
Teacher spread0.228 · 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
Published2010
Admission routes1
Has abstractyes

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