The Oxygen Reduction Reaction on Pt and Ag Catalysts in Alkaline Media in Rrde and Half-Cell Setups
Bibliographic record
Abstract
Understanding the Oxygen Reduction Reaction (ORR) in alkaline media is essential to optimize alkaline fuel cells or metal-air batteries. The catalytic activity of various ORR catalysts has usually been evaluated via Rotating (Ring) Disk Electrode (R(R)DE) measurements. However, using an electrolyte with dissolved oxygen at low KOH concentrations measured in a microampere regime at room temperature does not reflect the real cell conditions.1 Yet, RRDE is still a valuable tool for probing the reaction pathway of catalysts in alkaline media. Our study combined traditional RRDE with Gas Diffusion Electrodes (GDEs) half-cell measurements using Pt and Ag catalysts. The polarization curve of Pt-GDE exhibits a kink near 0.8 V vs. RHE leading to a significant potential drop. This specific feature can be explained by the RRDE results, which display an increased peroxide formation in that potential region due to the adsorption/desorption of hydroxyl adsorbates.2 However, Ag does not exhibit such behavior since the peroxide formation is below typical operating potentials. Depending on the operating conditions, Ag can catalyze the ORR more effectively than Pt in alkaline conditions while significantly reducing production costs. Furthermore, our study presents a general approach for evaluating ORR catalysts under experimentally challenging conditions such as elevated temperature and high KOH molarity. References: (1) Ehelebe, K.; Schmitt, N.; Sievers, G.; Jensen, A. W.; Hrnjić, A.; Collantes Jiménez, P.; Kaiser, P.; Geuß, M.; Ku, Y.-P.; Jovanovič, P.; et al. Benchmarking Fuel Cell Electrocatalysts Using Gas Diffusion Electrodes: Inter-lab Comparison and Best Practices. ACS Energy Lett. 2022, 7, 816–826. (2) Ramaswamy, N.; Mukerjee, S. Fundamental Mechanistic Understanding of Electrocatalysis of Oxygen Reduction on Pt and Non-Pt Surfaces: Acid versus Alkaline Media. Advances in Physical Chemistry 2012, 2012, 1–17. 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".