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Does power ultrasound affect hydrocarbon Ionomers?

2021· article· en· W3158006158 on OpenAlexafffund
Michael Adamski, Nicolas Peressin, Emmanuel Balogun, Bruno G. Pollet, Steven Holdcroft

Bibliographic record

VenueUltrasonics Sonochemistry · 2021
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaSimon Fraser UniversityNorges Teknisk-Naturvitenskapelige Universitet
KeywordsIonomerSonicationPolymerMaterials scienceHydrocarbonCatalysisCarbon blackChemical engineeringPolymer chemistryChemistryComposite materialOrganic chemistryCopolymerChromatography

Abstract

fetched live from OpenAlex

The effect of low-frequency high-power ultrasound on hydrocarbon-based ionomers, cation exchange sulfonated phenylated polyphenylene (sPPB-H+) and anion exchange hexamethyl-p-terphenyl poly(benzimidazolium) (HMT-PMBI), was studied. Ionomer solutions were subjected to ultrasonication at fixed ultrasonic frequencies (f = 26 and 42 kHz) and acoustic power (Pacous = 2.1 – 10.6 W) in a laboratory-grade ultrasonication bath, and a probe ultrasonicator; both commonly employed in catalyst ink preparation in research laboratory scale. Power ultrasound reduced the polymer solution viscosity of both hydrocarbon-based ionomers. The molecular weight of sPPB-H+ decreased with irradiation time. Changes in viscosity and molecular weight were exacerbated when ultrasonicated in an ice bath; but reduced when the solutions contained carbon black, as typically used in Pt/C-based catalyst inks. Spectroscopic analyses revealed no measurable changes in polymer structure upon ultrasonication, except for very high doses, where evidence for free-radical induced degradation was observed. Ionomers subjected to ultrasound were used to prepare catalyst layers and membrane electrode assemblies (MEA)s. Despite the changes in the ionomer described above, no significant differences in electrochemical performance were found between MEAs prepared with ionomers pre-subjected to ultrasound and those that were not, suggesting that fuel cell performance is tolerant to ionomers subjected to ultrasound.

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 score0.114
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 head0.003
GPT teacher head0.184
Teacher spread0.181 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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