Activity and stability proxies for automated evaluation of IrOx electrocatalysts under variable operating conditions
Bibliographic record
Abstract
To accelerate the screening of electrocatalyst materials, it is necessary to enhance the efficiency of their performance evaluation and optimization under dynamic conditions. The activity and stability of electrocatalyst materials are two crucial metrics that are typically correlated, and thus need to be evaluated in parallel. However. assessing both activity and stability in a time-efficient, reliable and comparable manner remains a challenge. Given the rising interest in evaluating electrocatalysts under realistic fluctuating conditions, we propose an electrochemical approach that uses random sampling and Bayesian optimization to explore pulsed amperometry conditions in hydrous iridium oxides for the oxygen evolution reaction. This method provides activity and stability proxies independent of sample loading which are validated against literature data. • Novel proxies developed to evaluate IrOx activity and stability realistic, fluctuating operating condi-tions. • Bayesian optimization shown to accelerate screening of electrocatalyst performance. • Approach validated with literature data. • Methodology shown to be robust across samples and independent of catalyst loading. • Enables rapid, scalable, and automated assessment of electrocatalysts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".