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Record W2481352282 · doi:10.1021/bk-2004-0876.ch021

Improving the Permselectivity of Commercial Cation-Exchange Membranes for Electrodialysis Applications

2004· book-chapter· en· W2481352282 on OpenAlexafffund
Sophie Tan, Alexis Laforgue, Daniel Bélanger

Bibliographic record

VenueACS symposium series · 2004
Typebook-chapter
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrodialysisMembranePolyanilineX-ray photoelectron spectroscopyChemistryChemical engineeringInterfacial polymerizationScanning electron microscopeIon exchangeAdsorptionPolymerizationInorganic chemistryNuclear chemistryMaterials sciencePolymerIonOrganic chemistryMonomerComposite material

Abstract

fetched live from OpenAlex

Cation-exchange membranes bearing sulfonate groups (Neosepta CMX) were modified by chemical polymerization of aniline at the surface of the membranes. The doped polyaniline (PANI) adsorbed at the surface of the membrane consists of a positively charged layer which acts as an electrostatic barrier for multivalent cations. The resulting composite membranes (CMX-PANI) were characterized by electrodialysis, scanning electron microscopy (SEM), exchange capacity measurements (EC) and X-ray photoelectron spectroscopy (XPS). The presence the PANI layer was shown to improve the membrane permselectivity for protons vs. bivalent cations (Zn 2+ and Cu 2+ ) by a factor of at least 20 after electrodialysis in acidic solutions. Optimization of the anilinium exchange time as well as the polymerization time were performed. It was also demonstrated that the blocking efficiency of the PANI layer depended on the thickness and uniformity of this layer.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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
Published2004
Admission routes2
Has abstractyes

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