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Record W1975956145 · doi:10.1021/ma011205a

Study on the Kinetics of Surface Migration of Surface Modifying Macromolecules in Membrane Preparation

2002· article· en· W1975956145 on OpenAlexaff
Daniel Eumine Suk, Geeta Chowdhury, Takeshi Matsuura, Roberto Narbaitz, Paul Santerre, Gerald Pleizier, Yves Deslandes

Bibliographic record

VenueMacromolecules · 2002
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsNational Research Council CanadaUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsContact angleX-ray photoelectron spectroscopyMembraneEvaporationMacromoleculeChemical engineeringSolventMaterials scienceKineticsPolymer chemistryCastingWettingChemistryFluorineAnalytical Chemistry (journal)Composite materialChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Surface modifying macromolecules (SMM) were synthesized and blended into the casting solution of poly(ether sulfone). The solution was cast to films with thickness of 0.12 and 0.24 mm. The cast films were placed in an oven with forced air circulation for periods of 3, 5, 7, and 2000 min to remove the solvent, before being immersed into water at 4 °C for gelation. The membranes so prepared were further dried and subjected to contact angle measurement and XPS (X-ray photoelectron spectroscopy) analysis. It was found that the contact angle increased as the solvent evaporation period increased. The increase in contact angle was faster when the membrane was thinner. According to the XPS analysis, after an initial time lag the surface fluorine content increased as the evaporation time increased and finally leveled off. The increase in surface fluorine content was also faster when the membrane was thinner. A kinetic model was established for the SMM surface migration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.276
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 source (direct Gemma or distilled Codex), 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

Citations115
Published2002
Admission routes1
Has abstractyes

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