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Record W2746497531 · doi:10.1002/cphc.201700745

Interfacial Water Drives Improved Proton Transport in Siliceous Nanocomposite Nafion Thin Films

2017· article· en· W2746497531 on OpenAlexaff
Pieter‐Jan Goossens, Brecht Vallaey, Jesse Verlinden, Johan A. Martens, Jan Rongé

Bibliographic record

VenueChemPhysChem · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsExxonMobil (Canada)
FundersVlaamse regeringFonds Wetenschappelijk OnderzoekVlaamse Overheid
KeywordsNafionNanocompositeMaterials scienceChemical engineeringConductivityMembraneProton transportProton exchange membrane fuel cellThin filmElectrolysis of waterWater transportIonomerComposite materialNanotechnologyElectrolysisChemistryElectrochemistryPolymerElectrodeCopolymer

Abstract

fetched live from OpenAlex

Abstract Nafion proton exchange membranes dehydrate when they are used in the gas phase and in high‐temperature applications, such as fuel cells and (photo)electrolysis. Retaining a high level of membrane hydration under such conditions can be achieved by using inorganic fillers, but has never been demonstrated for thin films. Herein, several types of siliceous nanoparticles were incorporated for the first time into Nafion thin films. For composite Nafion materials, increased water uptake does not always induce increased proton conductivity. Here, increased water uptake did result in higher proton conductivity due to a synergistic effect within the composite film. The nanocomposites displayed a higher water uptake than could be expected based on the water uptake of the individual materials. Excess water present at the Nafion–filler interface was found to cause the proton conductivity of nanocomposite Nafion/Ludox AS‐40 thin films to double compared with pristine Nafion at low relative humidity (from 2 to 4 mS cm −2 ). Knowledge about the properties of such interfaces will allow for the better design of self‐humidifying nanocomposite Nafion membranes, films, and catalyst layers.

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 score0.011
Threshold uncertainty score0.687

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 head0.207
Teacher spread0.201 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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