Method for Enhancing the Bifunctional Activity and Durability of Oxygen Electrodes with Mixed Oxide Electrocatalysts: Potential Driven Intercalation of Potassium
Bibliographic record
Abstract
The bifunctional oxygen reduction and evolution reaction (ORR and OER, respectively) electrocatalytic activity and durability of mixed oxides MnO 2 -LaCoO 3 and MnO 2 -Nd 3 IrO 7 , are investigated. The goal is to identify possible beneficial synergistic catalytic effects between the two oxides and to investigate the role of alkali-metal ions (Li + , Na + , K + and Cs + ) for promotion of electrocatalytic activity and durability. The combination of the two, structurally different, oxides, improves the bifunctional activity compared to the individual oxide components, as shown by either lower apparent Tafel slopes or higher exchange current densities for ORR and OER in 6 M KOH. Insertion of potassium ion in the oxide structure either by longer-term exposure to 6 M KOH or by an accelerated potential driven intercalation method, lowers further both the OER and ORR overpotentials. At constant current density of 5 mA cm −2 (or 5 A g −1 catalyst) for two hours, the OER overpotential is lowered by 110 mV and 152 mV due to potential driven potassium ion insertion in MnO 2 -LaCoO 3 and MnO 2 -Nd 3 IrO 7 , respectively. For ORR, at −2 mA cm −2 (or −2 A g −1 catalyst) the overpotential on MnO 2 -LaCoO 3 is decreased by 75 mV. In addition, the stability of the potassium ion activated catalysts is also improved. The ORR activity promotion effect is specific to potassium compared to all other investigated alkali metal hydroxides (LiOH, NaOH, CsOH), whereas for OER, cesium ion also has a beneficial effect. The electrode kinetic results are supported by surface analysis showing the presence of potassium in the catalyst.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".