Bibliographic record
Abstract
Pt is a widely used catalyst for fuel cells in industry, but the irreversibility of the oxygen reduction reaction (ORR) restricts its efficiency and incurs about 40% of all irreversible energy losses1. Furthermore, the dissolution of Pt degrades the catalyst and limits its lifetime. Pt oxide is known to play a role in promoting the dissolution2 and is thought to degrade the performance of the ORR. Even though studies have been done for many years, the details of the oxide structure and the mechanism of oxide formation and reduction are still not fully resolved. In the case of the kinetics, most work has been directed at the oxide formation, and the reduction kinetics have been relatively neglected. We report here kinetic studies on the Pt oxide reduction on polycrystalline Pt by cyclic voltammetry and various potential programs that combine sweep and hold periods. Differential-equation-based models were investigated to simulate the oxide reduction and estimate kinetic parameters. The simplest sweep-hold method involves preparing the surface with potential sweeps and then holding the potential at selected potentials. Constant potential is typically better for quantitative analysis of kinetics since the rate constants are fixed. Compared to conventional large potential steps, the sweep preparation enables the initial conditions to be closely controlled, and the double-layer charging is less. Adding different negative-going sweeps after identical sweep-hold experiments allows for study of sweep rate dependence under identical coverage and potential initial conditions, which is not the case for regular cyclic voltammetry at different sweep rates. Most simple mechanisms are based on rate laws in which the rates and current at time t are only dependent on the coverages and potential at time t, and not on the history of how those coverages were prepared. In contrast, rates for nucleation-and-growth type mechanisms can depend on the history. Since the oxidized surface is a restructured surface, different histories may also produced different 3-D structures at the same coverage. By varying the preparation step to produce the same coverage at the same potential but with different histories, evidence was found that the reduction peak position and shape only depend on oxide coverage and potential, and not on the history. The results are presented and modelled with a simple adsorption mechanism, which gives reasonable agreement with experiment. Refinement will use the idea that the relationship between coverage and available Pt sites may not be linear3. References [1] S.G. Rinaldo, W. Lee, J. Stumper and M. Eikerling, Electrocatalysis, (2014) 5:262-272. [2] A.A. Topalov, I. Katsounaros, M. Auinger, S. Cherevko, J.C. Meier, S.O. Klemm, K.J.J Mayrhofer, Angew. Chem. Int. Ed. Engl. (2012) 51:12613–5. [3] P.K. Dahlstrøm, D.A. Harrington and F. Seland, Electrochim. Acta, (2012) 82:550-557.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".