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Record W1843183515 · doi:10.1063/1.3160271

Modification of Polyethersulfone Hollow Fiber Membranes by Novel Charged Surface Modifying Macromolecule (cSMM) Blends for Water Application

2009· article· en· W1843183515 on OpenAlexaff
Nurmin Bolong, Ahmad Fauzi Ismail, Mohd Razman Salim, Dipak Rana, Takeshi Matsuura, M. Rusop, Tetsuo Soga

Bibliographic record

VenueAIP conference proceedings · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMacromoleculeMembraneMaterials scienceFiberSurface modificationChemical engineeringComposite materialChemistryEngineering

Abstract

fetched live from OpenAlex

The analysis on modification of the polyethersulfone hollow fiber membranes by blending with Surface Modifying Macromolecules (SMMs) with end‐groups of Aromatic sulfonate or aromatic benzoate for introducing charges at membrane surface is presented. The charged SMMs (cSMM) was synthesized and blended into the dope solution of polyethersulfone (PES) membranes. The synthesized cSMM was dissolved into the dope solution which consists of PES using N‐methyl‐2‐pyrrolidone (NMP) as solvent. The membranes are prepared by phase inversion technique with the dry‐wet hollow fiber spinning condition. The morphology of the fabricated membranes was observed from the Field Emission Scanning Electron Microscopy (FESEM). The FESEM observation did not show significant difference due to cSMM modification indicated the base polymer remained relatively unchanged. The miscibility and existence of cSMM in PES membrane was then confirmed via Fourier transform infra‐red (FTIR) scan. The increase of pure water permeation performance of the modified membranes than the unmodified membranes was also observed.

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)0.000
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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.028
GPT teacher head0.260
Teacher spread0.233 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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