Tailoring α-MnO2 gas diffusion electrodes for enhanced oxygen reduction in aluminum-air batteries
Bibliographic record
Abstract
An optimization strategy for gas diffusion electrodes is presented by tuning the α-MnO 2 -to-Vulcan ratio, significantly enhancing oxygen reduction reaction (ORR) performance and demonstrating improved efficiency in a prototype Al-O 2 cell. α-MnO 2 is a promising, inexpensive, and readily producible catalyst for the oxygen reduction reaction (ORR) in alkaline media, but its application is limited by low electronic conductivity. In this study, we enhance the performance of α-MnO 2 electrodes by systematically varying the α-MnO 2 -to-Vulcan ratio within the catalyst layer. Electrodes are evaluated in a gas diffusion electrode (GDE) half-cell, where an optimized catalyst layer composition leads to significantly improved ORR performance. By fine-tuning both the α-MnO 2 -Vulcan ratio and the α-MnO 2 loading, the electrode outperforms a commercial MnO 2 -based electrode and approaches the performance of the Pt/C benchmark. The improvement is attributed to the presence of a three-dimensional (3D) Vulcan network electronically connecting catalytically active α-MnO 2 sites with the substrate. Additionally, the optimized electrodes are employed in a prototype Al-O 2 flow cell. Under constant oxygen flow, power densities exceed 250 mW cm −2 , which is significantly higher than that of conventional Al-air batteries. Electrochemical impedance spectroscopy combined with distribution of relaxation times (DRT) analysis enables the separation of anode and cathode charge transfer impedances without the need for an additional reference electrode. The analysis reveals that the anode contributes more than twice as much impedance as the cathode, highlighting the need for further anode optimization. This work demonstrates a transferable approach for catalyst layer screening under technically relevant conditions in the GDE half-cell. Subsequent measurements in an Al-O 2 flow cell validate the approach. The methodology is widely applicable to the development of advanced electrodes for a variety of metal-air battery technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".