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Record W2906007456 · doi:10.1002/app.47344

Sulfonated poly(phenylene ether ether sulfone) membrane tailored with layer‐by‐layer self‐assembly of poly(diallyldimethylammonium chloride) and phosphotungstic acid for DMFC applications

2018· article· en· W2906007456 on OpenAlexaff
Sivasubramaniyan Neelakandan, A. Muthumeenal, Dipak Rana, Noel Jacob Kaleekkal, A. Nagendran

Bibliographic record

VenueJournal of Applied Polymer Science · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMembranePhosphotungstic acidPhenyleneEtherPolymer chemistryPolyelectrolyteMaterials scienceChemical engineeringChemistryPolymerOrganic chemistryComposite materialCatalysis

Abstract

fetched live from OpenAlex

ABSTRACT Poly(diallyldimethylammonium chloride) (PDDA) and phosphotungstic acid (PTA) were used as cationic and anionic polyelectrolyte layers, respectively, in an alternating fashion to enhance the methanol barrier property and oxidative stability of sulfonated poly (phenylene ether ether sulfone) (SPEES) proton exchange membranes (PEMs). The multilayer PEMs were characterized by AFM, FTIR, and AC impedance spectroscopy. Methanol permeability of the multilayered membranes was found to be much lower than the bare SPEES membrane. The multilayered membranes displayed significantly improved oxidative stability and dimensional stability compared to pristine SPEES membrane. Conversely, the water uptake (%) and proton conductivity (S cm −1 ) of the prepared membranes decrease to some extent with increasing the PDDA/PTA bilayers in comparison to the pristine SPEES membrane. The maximum relative selectivity (2.23 × 10 4 S cm −3 s) and retained weight (88.9%) were observed for SPEES‐[PDDA/PTA] 5 multilayered membrane. The obtained results exposed the possibility of SPEES‐[PDDA/PTA] 5 multilayered membrane to serve as high‐performance PEMs in direct methanol fuel cells. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136 , 47344.

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 score0.033
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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