Effect of Carbon Surface on Nafion Thin Film Hygro-Expansion and Thermal-Expansion
Bibliographic record
Abstract
Polymer electrolyte fuel cell (PEFC) catalyst layers consist of carbon, platinum, and ionomer. Carbon is necessary to provide a high surface area to support catalytic platinum nanoparticles and facilitate electron transport to the reaction sites whereas ionomer is required to supply protons to the reaction site. In addition, together they contribute to the structural strength of a catalyst layer. Since ionomer and carbon make up a majority of the catalyst layer volume, the ionomer in catalyst layer mostly interfaces with carbon support. Studies have shown the suppression of ionomer properties in thin film form especially below 50 nm and is also highly dependent on the substrate. For example, Key et. al measured higher water content in 19 nm thick Nafion coated on Pt than ionomer on SiO2 [1]. Recently, Orfanidi et. al, showed that the functionalization of carbon with NHx group assists in better distribution of ionomer within catalyst layer and restructure the ionomer in such a way that oxygen transport resistance across the ionomer drops [2]. Hence, from the fuel cell application perspective, it is important to study the effect of different carbon substrates on ionomer properties. There is lack of data available on fundamental properties such as hygro-expansion and thermal-expansion of nanometer thin film Nafion on carbon in general and the effect of carbon surface characteristics, in particular on these properties. This presentation will share the effect of hydrophobic carbon and functionalized hydrophilic surfaces on the hygro- and thermal-expansion of 20 and 70 nm thick Nafion films. References: [1] H.K. Shim, D.K. Paul, and K. Karan. Macromolecules 48, 2015, 8394. [2] A. Orfanidi, P. Madkikar, H. A. El-Sayed, G. S. Harzer, , T. Kratky and H. A. Gasteiger. The J. of Electrochemical Society, 164, 2017, F418.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".