Probing Interfacial (Pt/Ionomer) and Bulk Water Distribution in Ultra-Thin Films of PFSA and Pfia Ionomers on Platinum Using Neutron Reflectometry
Bibliographic record
Abstract
Over the last decade, proton conducting ionomers in ultra-thin film form have been subjected to scrutiny because of their relevance to fuel cell catalyst layers. The few nanometers thin ionomer film covering the Pt/C catalysts serve multiple functionalities: transport of reactant gases (O2 or H2) through the ionomer film to the Pt catalyst, transport of water across and along the ionomer film, and proton transport along the ionomer film. The ionomer films respond most strongly to the humid environment resulting in proportional uptake of water. The distribution of water at the Pt/ionomer interface and within the ionomer is expected to be a critical factor in controlling key transport phenomena mentioned above. The interfacial water is also expected to directly impact the electrochemical activity – both by affecting the local proton concentration and mobility as well as potentially offering an transport resistance to the reactant gases. One MD simulation study on Nafion/Pt identifies the backbone densification near Nafion/Pt interface is a root cause of high interfacial transport resistance [1]. Recent findings on low catalyst activity due to the dehydration of ionomer have pointed towards the adsorption of side chains on Pt surface which varies with the ionomer structure [2]. Thus, the description of the Pt/ionomer interface is essential in understanding how these two are transported to the reaction sites because ORR is ultimately tied to interfacial O2 transport resistance and catalyst activity. Neutron Reflectometry (NR) has been a heavily exploited as a non-invasive technique to indirectly visualize the interface and material phase distribution within a thin film. Dura et. Al applied NR to measure a 42 nm thick Nafion film coated on planar Pt [3]. Their findings revealed a 1.1 nm thick water rich region near the interface and uniform water containing bulk film at 92% RH. However, in a similar condition, based on a NR study Wood et. al, claims differed greatly and they identified a water scarce region near the interface [4]. So far only these two NR studies have elucidated the Pt/ionomer interface in wet conditions. We have investigated water sorption on bare Pt and in ~15 nm thick ionomer films coated on Pt. Three different ionomers of varying equivalent weight have been examined: Nafion (EW1100), 3M (EW725), and PFIA (EW 625). The presentation will share the results on the effect of ionomer molecular structure on interfacial morphology as well as water distribution within the each ionomer film. Some of the key findings emerging from our work are: (i) the presence of few monolayers equivalent water rich interfacial layer at Pt/ionomer interface (see Figure 1 below) but varying with ionomer EW; (ii) varying water content and water distribution for the different ionomers; (iii) temperature affects water distribution not merely content. References [1] K. Kudo, R. Jinnouchi, Y. Morimoto. Electrochimica Acta, 209 (2016) 682. [2] K. Kodama, K. Motobayashi, A. Shinohara, N. Hasegawa, K. Kudo, R. Jinnouchi, M. Osawa, and Y. Morimoto. ACS Catal., 8, (2017) 694. [3] J.A. Dura, V.S. Murthi, M. Hartman, S.K. Satija, C.F. Majkrzak. Macromolecules, 42, (2009) 4769. [4] D. L. Wood III, J. Chlistunoff, J. Majewski, and R.L. Borup. J. of American Chemical Society, 131, (2009) 18096. Figure 1
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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) | 0.001 | 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".