Ir Decorated Fractal Ni Catalysts for the Oxygen Evolution Reaction
Bibliographic record
Abstract
Porous and fractal Ni electrode synthesized by the Dynamic Hydrogen Bubbling Template (DHBT) method (Ni DHBT electrode) have shown very promising performances for the oxygen evolution reaction (OER) in alkaline electrolyte. This was thought to arise as a result of the extended electrochemical active surface area and its superaerophilic properties that facilitates the release of oxygen bubbles[1]. Herein, we report on a method to decorate the Ni DHBT electrode with Ir atoms via a galvanic replacement (GR) reaction. The resulting electrode has very low loadings of Ir (0.26 atom % as determined by EDX). As shown in Figure 1, micrometer-sized pores are observed at the surface of deposits, with pore walls exhibiting a highly porous cauliflower-like secondary structure, with much smaller pore diameter (typically less than 500 nm). The structure seen in Figure 1 was observed over the entire 0.32 cm2 geometric surface area of the deposits. The morphology of the Ir-modified electrode is not changed with respect to Ni DHBT electrode, indicating that the GR reaction is occurring only at the surface of the deposit. Voltammetry cyclic tests in 1M KOH solution showed hydrogen adsorption/desorption (Hupd) peaks of Ir as well as the redox peaks of Ni(OH)2 which suggested the coexistence of Ir and Ni content at electrode/electrolyte interface. The activity for the OER of Ni DHBT electrode was further improved by decoration with Ir, resulting in an overpotential of 195mV at 10 mA/cm2 and a Tafel slope of 46 mV/dec. To the best of our acknowledgment, this is one of the best performing Ni based OER catalysts reported so far. The interaction of oxygen bubbles with the electrode surface will also be assessed using both optical imaging and acoustic emission characterization. [1] M. Hao et al., “Hydrogen Bubble Templating of Fractal Ni Catalysts for Water Oxidation in Alkaline Media,” ACS Appl. Energy Mater., vol. 2, no. 8, pp. 5734–5743, Aug. 2019. Figure 1
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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) | 0.000 | 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 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".