A Study of the Catalytic Interface for O<sub>2</sub> Electroreduction on Pt: The Interaction between Carbon Support Meso/Microstructure and Ionomer (Nafion) Distribution
Bibliographic record
Abstract
The catalytic activity of PEM fuel cell electrodes is determined by the complex physicochemical interactions among the components of the electrocatalytic interface: precious metal catalyst, support, and ionomer. In the present study, the effect of the carbon support meso- and microporosity was investigated in relation with both the Nafion content and distribution in the catalyst layer. The ionomer load was between 0.09 and 1.1 mg cm −2 in the catalyst layers prepared either by the Nafion-coated or Nafion-mixed techniques, while the Pt load was kept constant at 0.1 mg cm −2 . Three supports were investigated: Vulcan XC-72R, Denka, and graphitized carbon (GC). Employing both the BET (Brunauer−Emmett−Teller) and the BJH (Barrett−Joyner−Halenda) surface measurement techniques, a complete characterization of the support and supported catalyst (Pt/C) pore volume distribution and surface area in the micro- and mesopore size ranges was carried out. It was found that Pt nanoparticles (mean diameter between 4.1−4.9 nm by XRD) reduced the micropore volume of the carbon supports. Therefore, the supports with high BJH mesoporous area (Vulcan XC-72R and GC) yielded Pt/C catalysts with the highest electrochemically active Pt area as well. For the Denka support, characterized by the lowest BJH area, the Nafion-coated procedure gave about 7% larger electrochemically active area compared to the Nafion-mixed method. Regarding the oxygen electroreduction, the effective oxygen permeability in the catalyst layer, the intrinsic kinetic current density, and the area and mass-specific activities at 0.9 V RHE were determined as a function of support type, Nafion incorporation method, and load.
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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".