(Invited) Probing the Oxygen Reduction and Hydrazine Oxidation Reactions Using Transient Amperometric Techniques
Bibliographic record
Abstract
The kinetics of electrochemical reactions affected by mass transfer is often investigated by rotating disc electrode (RDE) and rotating ring disc electrodes. However, these hydrodynamic techniques require electrodes that can be shaped into a disk or catalysts that can be drop casted on the surface of a disk electrode. Instead, transient amperometric techniques such as chronoamperometry and sampled current voltammetry (SCV) hold potential to investigate the kinetics of reactions without such requirements. In SCV, a series of chronoamperograms are recorded for a range of target potentials, and the current sampled at a fixed time or over a range of time scales. Sampled current voltammograms are obtained by plotting the selected currents vs the target potentials. 1-3 As we demonstrated recently with ferri/ferrocyanide ions and with oxygen reduction reaction (ORR) on Pt polycrystalline disk electrode, proper selection of the sampling times leads to the same mass transfer coefficients as in the RDE, and so the obtained SCV are equivalent to the linear sweep voltammograms recorded with a RDE. 4 Moreover, the kinetically controlled current can be extracted from the current transients, by plotting the inverse of the current vs the square root of time and by extrapolating to infinitely short times (large mass transfer coefficients). Tafel analysis can then be conducted. 4 In this talk, we will first present how these transient amperometric techniques were used to investigate the oxygen reduction on Au (001) thin films with different thicknesses grown on insulating MgO (001) substrates. 5 It will be shown that SCV is sensitive to the structure and to themosaicity of the thin films, and thus suitable to establish reactivity structure relationships. Then, we will show some of our recent work on the hydrazine oxidation on Au and Pt polycrystalline surfaces. 6 References [1] A. J. V and L. R. Faulkner, Electrochemical methods: Fundamentals and Applications; Wiley, 2001. [2] S.C. Perry and G. Denuault, Phys. Chem. Chem. Phys. 17, 30005 (2015). [3] L. Mignard, M. Denoual, O. Lavastre, D. Floner and F. Geneste, J. Electroanal. Chem., 689, 83 (2013). [4] C. O. Soares, O. Rodríguez, G. Buvat, G., M. Duca, S. Garbarino, D. Guay, G. Denuault and A. C. Tavares, Electrochim. Acta, 362, 136946 (2020). [5] C. O. Soares, G. Buvat, Y. G. Hernández, S. Garbarino, M. Duca, A. Ruediger, G. Denuault, A. C. Tavares and D. Guay, ACS Catal 12, 1664 (2022). [6] D. Kashyap, C. O. Soares, A. C. Tavares, G. Denuault, D. Guay, in preparation. Acknowledgements This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, Strategic Partnership program). Cybelle Oliveira Soares acknowledges the FRQNT funding by means of the PBEEE merit scholarship and an internship received from the Engineered Nickel Catalysts for Electrochemical Clean Energy project administered from Queen’s University and supported by Grant No RGPNM 477963-2015 under the NSERC Discovery Frontiers Program.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".