Local Electrocatalytic Activity of Electrodeposited Ni-Fe Particles
Bibliographic record
Abstract
NiFe electrocatalysts have received increasing attention for the electrocatalysis of the oxygen evolution reaction.[1] Recently, using bipolar electrochemistry for the electrodeposition of nickel, we have demonstrated the formation of nickel particle gradients, which vary in size and particle density as a function of electrode position.[2] In this work, low ionic strength aqueous solutions containing Ni(II) and Fe(II) salts, without supporting electrolyte, were used for the electrodeposition of NiFe particles onto a conducting fluorine doped tin oxide substrate. In this talk, I will present our recent results exploring the effect of composition, size and morphology of NiFe particles on the local electrocatalytic activity towards the oxygen evolution reaction, measured using scanning electrochemical cell microscopy.[3] [1] Song, F., Bai, L., Moysiadou, A., Lee, S., Hu, C., Liardet, L., Hu, X. Transition Metal Oxides as Electrocatalysts for the Oxygen Evolution Reaction in Alkaline Solutions: An Application-Inspired Renaissance (2018) Journal of the American Chemical Society, 140 (25), pp. 7748-7759. [2] Beugré, R., Dorval, A., Lavallée, L.L., Jafari, M., Byers, J.C. Local electrochemistry of nickel (oxy)hydroxide material gradients prepared using bipolar electrodeposition (2019) Electrochimica Acta, 319, pp. 331-338. [3] Bentley, C.L., Kang, M., Unwin, P.R. Nanoscale Surface Structure-Activity in Electrochemistry and Electrocatalysis (2019) Journal of the American Chemical Society, 141 (6). pp. 2179-2193
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".