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Record W2914425272 · doi:10.1021/acs.langmuir.8b03721

Interesting Facets of Surface, Interfacial, and Bulk Characteristics of Perfluorinated Ionomer Films

2019· article· en· W2914425272 on OpenAlexafffund
Kunal Karan

Bibliographic record

VenueLangmuir · 2019
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaMitacsBallard Power Systems
KeywordsIonomerMaterials scienceChemical engineeringSurface (topology)Composite materialNanotechnologyChemistryPolymer sciencePolymerEngineering

Abstract

fetched live from OpenAlex

Ion-containing perfluorinated polymers possess unique viscoelastic properties, excellent proton conductivity, and nanophase-segregated structure all arising from the clustering of hydrophilic sulfonic acid groups within a matrix of hydrophobic fluorocarbons. When these ionomers are confined to nanothin films, a broad swathe of structural organization imparting a rich variety of surface, interfacial, and bulk characteristics can be expected. However, our understanding of perfluorinated ionomer thin film behavior is still in a rudimentary stage, and much of the research focus to date has been on its hydration-related structure and properties pertinent to electrochemical applications. Thus, many hidden gems-their interesting surface and interfacial properties-have been overlooked. In this Invited Feature Article, which is a summary of the key contributions by the author's group, including several collaborative publications on ionomer thin films, we unravel many of these facets. In addition, the article attempts to integrate knowledge acquired from a variety of investigations of different aspects of the ionomer thin films to refine and develop a consistent picture of their structure and behavior. First, we focus on the self-assembly of ionomers and show that dispersion media and hydrophobicity/hydrophilicity of the substrate can result in partial or even no coverage of substrates, shedding light on the complexity of polymer-substrate, polymer-solvent, and polymer-polymer interactions, an insight completely obscured when the spin-coating method is adopted for film creation. We demonstrate that the same ionomer can be used to create a variety of surfaces ranging from superhydrophilic to highly hydrophobic by controlling the film thickness or through the choice of substrate material. The ultrathin, hydrophilic surfaces of self-assembled Nafion ionomer films exhibit wettability switching behavior which opens the door to creating stimuli-responsive smart surfaces. The thermoresponsive behavior of the films is discussed in the context of surface (wettability) and bulk (thermal expansion) characteristics as well as a newly discovered vibrational mode. The substrate- and film thickness-dependent thermal expansion coefficients reinforce the importance of interfacial interactions and confinement on the structure/properties of these films. They also open up the potential of tuning ionomer bulk properties via substrate chemistry. The discovery of a vibrational mode that becomes thermally activated at high temperature has provided new insights into the origins of the molecular motions responsible for the α-relaxation of the Nafion ionomer as well as the underlying reason for wettability switching. Our recent neutron reflectometry study of different ionomers varying in side-chain composition/length on a platinum substrate shows that the interfacial hydration level is correlated to the side-chain length, which opens up the possibility of the controlling the interfacial electrochemistry. Finally, a systematic analysis of factors affecting proton conduction is presented to elucidate the yet-unresolved origins of the suppressed conduction of nanothin ionomer films compared to that of the bulk membrane. By revealing these interesting yet poorly understood facets of ionomer thin films, the article aims to stimulate further scientific pursuit on this topic.

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 score0.131
Threshold uncertainty score0.319

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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

Citations92
Published2019
Admission routes2
Has abstractyes

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