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Record W2334168955 · doi:10.1021/mz500515b

Elucidating Water Transport Mechanisms in Nafion Thin Films

2014· article· en· W2334168955 on OpenAlexfundno aff
Eric M. Davis, Christopher M. Stafford, Kirt A. Page

Bibliographic record

VenueACS Macro Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsNafionMembraneMaterials scienceSwellingDiffusionThin filmChemical engineeringWater transportIonic bondingPolarization (electrochemistry)IonomerSorptionConcentration polarizationAnalytical Chemistry (journal)Composite materialIonNanotechnologyChemistryPolymerOrganic chemistryElectrodeElectrochemistryPhysical chemistryWater flow

Abstract

fetched live from OpenAlex

Ion-exchange membranes are critical components of hydrogen fuel cells, where these ionomers can be confined to nanoscale thicknesses, altering the physical properties of these films from that of bulk membranes. Therefore, it is important to develop methods capable of measuring and elucidating the transport mechanisms under thin film confinement compared to bulk Nafion. In this study, water sorption and diffusion in a Nafion thin film were measured using time-resolved in situ polarization modulation infrared reflection absorption spectroscopy (PM-IRRAS). Interfacial mass transport limitations were confirmed to be minimal, while restricted water diffusion was observed, where the effective diffusion coefficient of water in the thin Nafion film was many orders of magnitude lower (between 4 and 5 orders of magnitude) than those reported for bulk membranes and was dependent on the initial hydration state of the Nafion. Furthermore, the response of the hydrophobic domains (Teflon backbone) to the swelling of the hydrophilic domains (ionic clusters) was shown to be orders of magnitude slower than that of bulk Nafion.

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 score0.001
Threshold uncertainty score0.002

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.164
Teacher spread0.161 · 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

Citations66
Published2014
Admission routes1
Has abstractyes

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Same venueACS Macro LettersSame topicFuel Cells and Related MaterialsFrench-language works237,207