Nanoisland Formation on the Low Index Planes of Pt By Repeated Electro-Oxidation and-Reduction
Bibliographic record
Abstract
Pt-based electro-catalysts are the most common cathode material in proton exchange membrane fuel cells given their high oxygen reduction reaction activity and stability. However, practical applications are limited by catalyst performance loss and degeneration. A main cause for this is nanoscale Pt surface restructuring, which occurs upon formation and reduction of a Pt surface oxide. During oxidation, a fraction of the Pt surface atoms are extracted in a process known as “place-exchange” from the electrode and are bound at specific sites in an oxide about 1 – 4 Å above the surface [1]. Upon subsequent oxide reduction, these atoms as well as the vacancies from which they originate can agglomerate, leading to the nucleation of adatom and vacancy islands. If this process is repeated, nanoislands with a well-defined lateral size of about 20 – 60 Å are grown [2-6]. Detailed studies of this growth process have been performed on the Pt(111) electrode [2-6], revealing two distinct growth regions which are characterized by different vertical scaling exponents [3]. For the development of catalysts with superior long-term stability, a more detailed knowledge of the restructuring of the other low index planes of Pt is required. In our recent study of the Pt oxides on Pt(111) and Pt(100) we already showed a strong dependence of the structural stability of the surface on differences in the atomic-scale oxide structure formed on those two surface orientations [1]. However, the influence of surface structure on the nanoisland growth after repeated oxidation and reduction is largely unclear. We present a comparative operando grazing incidence small angle X-ray scattering (GISAXS) study of the nanoisland formation on Pt(111), Pt(100) and Pt(110) in acidic electrolyte. The GISAXS measurements of the nanoisland growth were performed at a high photon energy of >70 keV, which lowered background scattering from the electrolyte, enabling us to resolve the islands after the very first ox./red. cycle. Differences in island shape and growth behaviour on the low index planes will be discussed. We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft via project no. 418603497, MA 1618/23-1 and the BMBF via 05K19FK3. References Fuchs et al., Nat. Catal. 3 , 754–761 (2020) Sugawara and K. Itaya, J. Chem. Soc., Faraday Trans. 185 , 1351-1356 (1989) Jacobse et al., Nat. Mater. 17 (3), 277-282 (2018) J. Rost, et al., Nat. Commun. 10 , 5233 (2019) Ruge et al., J. Am. Chem. Soc. 139 , 4532 (2017) Jacobse, et al. ACS Cent. Sci. 5 (12), 1920–1928 (2019)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".