Modeling ORR/Oxide Formation and Pt Dissolution - from Liquid Electrolyte to Polymer Electrolyte Systems: Issues and Approaches
Bibliographic record
Abstract
Recently, we presented an extension of Wang’s multi-step oxygen reduction reaction (ORR) kinetic model [1] to account for sub-surface oxide formation. Our model adequately captures three distinct oxygen electrochemistry on Pt features: ORR kinetics, oxide formation and reduction during cyclic voltammetry, and logarithmic oxide growth – and was extended to Pt dissolution [2]. Experimental data on oxygen electrochemistry on Pt and Pt dissolution are largely available for liquid electrolyte systems. Extension of the aforementioned model to fuel cell electrodes offers some challenges. The kinetic model depends on proton and water concentrations. In a liquid electrolyte system, similar to the ex-situ systems that comprise of dilute acid electrolytes to study ORR and cyclic voltammetry, the respective concentrations can be calculated in a straightforward manner. However in an actual fuel cell catalyst layer, where the liquid water is present is not clear. The concentration of liquid water can be treated to be that of the pure water but for Pt dissolution modeling, one needs to know the volume of the water pool surrounding the Pt crystal. The pH or proton concentration poses another challenge. The proton conductivity of ionomer is known to be RH dependent. However, the local proton concentration is not known and is expected to be function of local liquid water content. This is turn is expected to be a function of material composition, catalyst layer structure, RH, and operating current. This is illustrated by the trends shown in Figure 2 that highlight the effect of RH on cyclic voltammetry [3]. Furthermore these issues have a bearing on the ability to calculate platinum ion concentration within the catalyst layer in degradation studies. This presentation will discuss these important issues and some of the approaches we are taking to address the challenges. References [1] J.X. Wang, J. Zhang and R.R. Adzic J. Phys. Chem.A, 2007, 111 (49), 12702–12710. [2} Jayasankar, Barathram, Kunal Karan, and David Harvey. "Platinum degradation model in the presence of oxygen." Meeting Abstracts. No. 15. The Electrochemical Society, 2013. [3] Ruichun Jiang, H. Russell Kunz, James M. Fenton. “Investigation of membrane property and fuel cell behavior with sulfonated poly (ether ether ketone) electrolyte: Temperature and relative humidity effects”. Journal of power sources 150 (2005) 120-128
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".