Domain Size Dependence of the Oxygen Reduction Reaction on (100)-Oriented Au Thin Films
Bibliographic record
Abstract
Due to its importance for energy conversion and storage, the oxygen reduction reaction (ORR) is one of the most studied electrochemical reactions. It has been reported that, in alkaline media, Au(100) single crystal is the most active electrocatalyst for the ORR 1,2 . Different reasons have been proposed to explain this high activity 3,4 . Understanding this remarkable activity can guide researchers in the design of more active electrocatalysts for practical applications using nanomaterials with specific shape and size. Since the ORR is a structure-sensitive reaction 5–7 the manipulation of the structural properties of the materials is a powerful tool to elucidate the reaction mechanism responsible for the ORR and increase the activity of materials towards the reaction. In this work, (100)-oriented Au thin films were deposited on (100) MgO. Thin films with different thicknesses were prepared by pulsed laser deposition and were used as model surfaces to study the ORR activity. Pulsed Laser Deposition (PLD) is a versatile method to prepare thin films with different surface orientations (epitaxial thin films) 8,9 , 10 . The structure and microstructure of the films were assessed by X-Ray Reciprocal Space Mapping (RSM) and Atomic Force Microscopy (AFM). There results were correlated with those obtained from Cyclic Voltammetry (CV) and Sampled Current Voltammetry (SCV). The ORR activity was observed to be dependent on the film thickness, which is reflected by the shift of the onset and half-wave potentials of the ORR towards more positive potentials for the thicker films. When correlating the ORR activity with the structural and microstructural properties, Fig.1, the evidences suggest that larger lateral coherent lengths are responsible for the enhancement of the ORR catalysis on (100)-oriented Au thin films. The reasons underlying this behavior will be presented and discussed. Figure 1: Plot showing the variation of the onset and half-wave potential, and the variation of the coherence length for thin films of various thicknesses. References (1) Stamenkovic, V. R.; Strmcnik, D.; Lopes, P. P.; Markovic, N. M. Nature Materials . 2016, pp 57–69. (2) Markovic, N. M.; Tidswell, I. M.; Ross, P. N. Langmuir 1994, 10 (1), 1–4. (3) Duan, Z.; Henkelman, G. ACS Catal. 2019, 9 , 5567–5573. (4) Schneider, O. Size Dependent Electrocatalysis of Gold Nanoparticles ; Elsevier, 2018. (5) Adžić, R. R.; Marković, N. M.; Vešović, V. B.; Adzic, R. R.; Markovic, N. M.; Vesovic, V. B. J. Electroanal. Chem. 1984, 165 (1–2), 105–120. (6) Markovic, N. M.; Gasteiger, H. A.; Ross, P. N. J. Phys. Chem. 1995, 99 (11), 3411–3415. (7) Schmidt, T. J.; Stamenkovic, V.; Arenz, M.; Markovic, N. M.; Ross, P. N. Electrochim. Acta 2002, 47 (22–23), 3765–3776. (8) Sacré, N.; Hufnagel, G.; Galipaud, J.; Bertin, E.; Hassan, S. A.; Duca, M.; Roué, L.; Ruediger, A.; Garbarino, S.; Guay, D. J. Phys. Chem. C 2017, 121 (22), 12188–12198. (9) Garbarino, S.; Imbeault, R.; Sacré, N.; Guay, D. In Encyclopedia of Interfacial Chemistry: Surface Science and Electrochemistry ; 2017. (10) Imbeault, R.; Reyter, D.; Garbarino, S.; Roué, L.; Guay, D. J. Phys. Chem. C 2012, 116 (8), 5262–5269. Figure 1
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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".