Gas Diffusion Electrode Design and Conditioning with a Manganese(III/IV) Oxide Catalyst for Reversible Oxygen Reduction/Evolution Reactions
Bibliographic record
Abstract
Significant efforts have been made to develop cost-efficient, nonprecious metal catalysts for reversible oxygen electrodes [i.e., oxygen reduction/evolution reactions (ORR/OER)]. However, typically, their fast degradation during cycling between the OER and ORR potential domains (i.e., battery charge/discharge) has been a critical challenge for rechargeable metal-air batteries or reversible fuel cells. Herein, we used gas diffusion electrodes (GDEs) with a core–shell Mn/Mn 3 O 4 (referred to as MnO x ) nanocatalyst, and we investigated the impact of the gas diffusion layer properties (teflonation and HNO 3 pretreatment) and MnO x catalyst layer composition on the bifunctional activity and durability under alkaline conditions. Raman spectroscopy corroborated by X-ray photoelectron spectroscopy (XPS) results and average oxidation state calculations showed that the performance degradation during galvanostatic cycling is due to phase transition and oxidation of Mn 3 O 4 to γ-MnO 2, which can further oxidize at high anodic potentials (≥1.5 V RHE ) to catalytically inactive MnO 4 – . We found that the incorporation of carbon additives in the catalyst layer as electronic conductivity boosters has an additional beneficial effect on the cycling durability of the MnO x GDEs. The mixture of graphene and Vulcan XC 72 (1:1 w/w) in the catalyst layer improved 6-fold the galvanostatic cycling durability in accelerated degradation experiments. Another degradation mode occurs when the electrode is cycled to high reduction current densities generating inactive Mn(II) species. Electrode activation protocols based on cyclic voltammetry can further improve the bifunctional activity and stability of the MnO x GDEs.
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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 | 0.000 |
| 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".