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Record W2285302094 · doi:10.1149/ma2015-02/37/1317

Nafion Nanothin Films

2015· article· en· W2285302094 on OpenAlexaff
Kunal Karan

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIonomerThin filmNafionMaterials scienceGrazing-incidence small-angle scatteringChemical engineeringNanostructureWettingPolymerNanotechnologyComposite materialScatteringCopolymerChemistryOpticsElectrochemistryPhysical chemistrySmall-angle neutron scattering

Abstract

fetched live from OpenAlex

In the conventional catalyst layer of polymer electrolyte fuel cells, the ionomer (e.g. Nafion) exists as an ultra-thin film (4-10 nm thick) coating the Pt/C catalyst aggregates. Recent studies [1-12], including those from our group, have begun to investigate the properties and structure of such thin ionomer films. Thickness-dependent properties including proton conduction (7, 11), wettability of free-surface(5), water uptake (2, 6) and water diffusivity (9) of such ultra-thin ionomer films have been observed. The influence of substrate and processing method has also been observed (12). A number of the properties of the thin ionomer films differ significantly from the much thicker counterpart, i.e. the free film or bulk ionomer membrane. The nanostructure of these films are less understood. And, although indirect methods such as x-ray reflectivity (1) and grazing incidence (2) small angle x-ray scattering (GISAXS) have been employed a clear picture of the nanostructure of these thin films has not emerged. The presentation will summarize the current understanding of ionomer thin film structure and properties. Results of recent and ongoing work on direct imaging of thin Nafion films to probe the nano-features of the phase-segregated morphology will be presented. Results of thermal behavior of thin films investigated using in-situ thermal ellipsometry will also be presented. Reference: A. Dura et al., Macromolecules 42 (2009), 4769-4774. M.A. Modestino et al., Macromolecules 2013, 46 (3), pp 867–873, 2013 M. Bass et al., Macromolecules 44 (2011), 2893–2899. Dishari, K. S.; Hickner, A. M. ACS Macro Lett., 1 (2012), 291-295. D.K Paul et al., Macromolecules, 46 (2013), 3461–3475 A. Kongkanand J. Phys. Chem. C, 115 (2011) 11318–11325. D.K. Paul, D. K Electrochem. Comm., 13 (2011), 774-777. A. Modestino et al., Macromolecules, 45 (2012), 4681–4688. S.A. Eastman, Macromolecules, 45 (2012), 7920−7930. D.K. Paul and K. Karan J. Phys. Chem. C, 118 (2014), 1828–1835. D.K. Paul et al., J. Electrochem. Soc., 161 (2014), F1395-F1402 A. Kusoglu et al Adv. Funct. Mater. 2014, 24 (30), 4763-4774

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.016
GPT teacher head0.212
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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