Achieving High OER Performance by Tuning the Co/Mn Content in Prussian Blue Analogues
Bibliographic record
Abstract
The need for efficient, economical, and clean energy systems is increasing, and as a result, interest in water-splitting techniques to produce green hydrogen is also increasing. However, the sluggish kinetics of the oxygen evolution reaction (OER) hinders the practical application and widespread use of water-splitting technologies; therefore, to address this challenge, it is essential to develop cost-effective and efficient OER catalysts. In this work, we have synthesized an inexpensive and tunable FeCoMn Prussian blue analogue (PBAs) as an efficient OER catalyst via a straightforward process. The ratio of the Co and Mn to optimize the electrochemical performance, and as a result, the FeCo 0.41 Mn 0.42 PBA catalyst demonstrated the best electrochemical performance (260/304 mV overpotential at 10/50 mA cm –2, a low Tafel slope of 48 mV dec –1 and a good stability of 72 h at 10 mA cm –2 ). Additionally, X-ray absorption spectroscopy (XAS) measurements and density functional theory (DFT) calculations suggest that the FeCo 0.41 Mn 0.42 PBA possesses the optimized electronic density distribution at the active site (Co), and the doping of Mn and Fe can not only increase the electricity conductivity but also activate the critical H 2 O deprotonation step.
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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".