Nanostructured Electrode Enabling Fast and Fully Reversible MnO2-to-Mn2+ Conversion in Mild Buffered Aqueous Electrolytes
Bibliographic record
Abstract
On account of their low-cost, earth abundance, eco-sustainability, and high theoretical charge storage capacity, MnO 2 cathodes have attracted a renewed interest in the development of rechargeable aqueous batteries. However, they currently suffer from limited gravimetric capacities when operating under the preferred mild aqueous conditions, which leads to lower performance as compared to similar devices operating in strongly acidic or basic conditions. Here, we demonstrate how to overcome this limitation by combining a well-defined 3D nanostructured conductive electrode, which ensures an efficient reversible MnO 2 -to-Mn 2+ conversion reaction, with a mild acid buffered electrolyte (pH 5). A reversible gravimetric capacity of 560 mA·h·g -1 (close to the maximal theoretical capacity of 574 mA·h·g -1 estimated from the MnO 2 average oxidation state of 3.86) was obtained over rates ranging from 1 to 10 A·g -1 . The rate capability was also remarkable, demonstrating a capacity retention of 435 mA·h·g -1 at a rate of 110 A·g -1 . These good performances have been attributed to optimal regulation of the mass transport and electronic transfer between the three process actors, i.e. the 3D conductive scaffold, the MnO 2 active material filling it, and the soluble species involved in the reversible conversion reaction. Additionally, the high reversibility and cycling stability of this conversion reaction is demonstrated over 900 cycles with a Coulombic efficiency > 99.4 % at a rate of 44 A·g -1 . Besides these good performances, also demonstrated in a Zn/MnO 2 cell configuration, we discuss the key parameters governing the efficiency of the MnO 2 -to-Mn 2+ conversion. Overall, the present study provides a comprehensive framework for the rational design and optimization of MnO 2 cathodes involved in rechargeable mild aqueous batteries.
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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 | 0.001 | 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".