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Record W2081806238 · doi:10.1149/1.1508552

Chemical Modification of a Sulfonated Membrane with a Cationic Polyaniline Layer to Improve its Permselectivity

2002· article· en· W2081806238 on OpenAlexaff
Sophie Tan, Valérie Viau, Daphné Cugnod, Daniel Bélanger

Bibliographic record

VenueElectrochemical and Solid-State Letters · 2002
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPolyanilineElectrodialysisMembraneCationic polymerizationSurface modificationElectrochemistryMaterials scienceChemical engineeringInorganic chemistryProtonationSelectivityIon exchangePolyaniline nanofibersLayer by layerLayer (electronics)ChemistryPolymer chemistryElectrodeIonOrganic chemistryPolymerNanotechnologyPhysical chemistryComposite material

Abstract

fetched live from OpenAlex

The surface of a cation-exchange membrane (Neosepta) bearing sulfonate groups was modified by a thin polyaniline layer to produce a thin positively charged layer at its surface. This modification induces a slight modification of the surface morphology as evidenced by scanning electron microscopy and a decrease in ion exchange capacity. The presence of the polyaniline layer was confirmed by X-ray photoelectron spectroscopy. The selectivity of the modified and unmodified membranes toward protons vs. metallic cations such as and were determined after electrodialysis in a two-compartment electrochemical cell. The data indicate that the transport of the metallic cations is decreased significantly following the modification of the membrane with the cationic polyaniline layer. The latter allows the transport of protons from the catholyte to the anolyte compartment with a much improved selectivity since the divalent cations are excluded from the membrane due to the electrostatic barrier created by the thin protonated polyaniline layer. © 2002 The Electrochemical Society. All rights reserved.

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)0.000
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 score0.630

Codex and Gemma teacher scores by category

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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

Citations22
Published2002
Admission routes1
Has abstractyes

Explore more

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