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Record W2892878319 · doi:10.1002/cssc.201801965

Sulfophenylated Terphenylene Copolymer Membranes and Ionomers

2018· article· en· W2892878319 on OpenAlexafffund
Thomas J. G. Skalski, Michael Adamski, Benjamin Britton, Eric M. Schibli, Timothy J. Peckham, Thomas Weissbach, Takashi Moshisuki, Sandrine Lyonnard, Barbara J. Frisken, Steven Holdcroft

Bibliographic record

VenueChemSusChem · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsCopolymerMembranePolymerElectrolyteProton exchange membrane fuel cellMonomerPolymer chemistryMembrane electrode assemblyMaterials scienceChemical engineeringIonomerElectrochemistryChemistryElectrodeComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The copolymerization of a prefunctionalized, tetrasulfonated oligophenylene monomer was investigated. The corresponding physical and electrochemical properties of the polymers were tuned by varying the ratio of hydrophobic to hydrophilic units within the polymers. Membranes prepared from these polymers possessed ion exchange capacities ranging from 1.86 to 3.50 meq g −1 and exhibited proton conductivities of up to 338 mS cm −1 (80 °C, 95 % relative humidity). Small‐angle X‐ray scattering and small‐angle neutron scattering were used to elucidate the effect of the monomer ratios on the polymer morphology. The utility of these materials as low gas crossover, highly conductive membranes was demonstrated in fuel cell devices. Gas crossover currents through the membranes of as low as 4 % (0.16±0.03 mA cm −2 ) for a perfluorosulfonic acid reference membrane were demonstrated. As ionomers in the catalyst layer, the copolymers yielded highly active porous electrodes and overcame kinetic losses typically observed for hydrocarbon‐based catalyst layers. Fully hydrocarbon, nonfluorous, solid polymer electrolyte fuel cells are demonstrated with peak power densities of 770 mW cm −2 with oxygen and 456 mW cm −2 with air.

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 categoriesInsufficient payload (model declined to judge)
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 score0.097
Threshold uncertainty score1.000

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)0.0010.000

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 head0.004
GPT teacher head0.173
Teacher spread0.169 · 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.

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

Citations58
Published2018
Admission routes2
Has abstractyes

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