MétaCan
Menu
Back to cohort
Record W2911587569 · doi:10.1021/acs.chemmater.8b05302

Stability of Hydrocarbon Fuel Cell Membranes: Reaction of Hydroxyl Radicals with Sulfonated Phenylated Polyphenylenes

2019· article· en· W2911587569 on OpenAlexafffund
Thomas F. Holmes, Thomas J. G. Skalski, Michael Adamski, Steven Holdcroft

Bibliographic record

VenueChemistry of Materials · 2019
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRadicalChemistryPolymerPolymer chemistryMembranePhotochemistryElectrolyteOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

The perceived poor durability of hydrocarbon polymer electrolyte membranes remains a significant hurdle for the integration of nonfluorous, solid polymer electrolytes into electrochemical systems such as fuel cells. In order to elucidate the mechanism of free radical degradation in a promising class of hydrocarbon polymer electrolyte membranes based on sulfonated phenylated polyphenylenes (sPPP), we synthesized and studied the degradation of a structurally analogous oligophenylene model compound in the presence of hydroxyl radicals using NMR spectroscopy and mass spectrometry. Degradation is demonstrated to be initiated by the oxidation of pendant phenyl rings to carboxylic acids, which form fluorenone substructures via intramolecular reaction with a juxtaposed phenyl ring. Upon further oxidation, these substructures can lead to ring-opening of a core phenyl ring which, if occurring in sPPP, leads to chain-scission of the polymer backbone. In keeping with this hypothesis, molecular weights of sPPP are found to decrease when subject to hydroxyl radicals. Although degraded polymer NMR spectra remain unchanged, resonances consistent with the elimination of sulfobenzoic acid emerge.

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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.166
Teacher spread0.162 · 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

Citations64
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueChemistry of MaterialsSame topicFuel Cells and Related MaterialsFrench-language works237,207