The effect of Ir content on the stability of Ti/IrO<sub>2</sub>‐SnO<sub>2</sub>‐Sb<sub>2</sub>O<sub>5</sub> electrodes for O<sub>2</sub> evolution
Bibliographic record
Abstract
Ternary IrO2‐SnO2‐Sb2O5 is among the best electrocatalysts for O2 evolution. Its compositions, especially its Ir content, have significant impacts on the electrochemical stability, activity, and cost of the electrode. In this paper, the effects of Ir content on the electrochemical stability and activity of the Ti/IrO2‐SnO2‐Sb2O5 electrodes were investigated. Experimental results show that the electrochemical stability initially increased with nominal Ir content until 10 mol%. From 10–30 mol%, the effect of Ir composition gives insignificant difference. Further increase in Ir content beyond 30 mol% resulted in a decrease in the electrochemical stability. The performance of the electrode depends on all the steps it was made with about 15 % variation observed at Ir content of 20 mol%, where the longest average accelerated service life was found to be 1063 h under the conditions of anodic current density of 10 000 A/m2 in 3 mol/L H2SO4 electrolyte at 70 °C. The study on electrode degradation and failure mechanism reveals that the development of cracks or pores in the coating surface, the loss of Sb and Ir contents, and crystalline structure change of the coating during the life test facilitated the deactivation of the electrode. Moreover, the non‐conductive TiO2 interlayer formation was found to be responsible for the peeling of the coating layer, leading to the failure of Ti/IrO2‐SnO2‐Sb2O5, especially with high Ir content (> 30 mol% in nominal).
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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 | 0.001 |
| 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".