Rechargeability of Manganese Dioxide-Zinc Batteries
Bibliographic record
Abstract
Primary alkaline batteries use electrolytic manganese dioxide (EMD) as the cathode and zinc as the anode and have been in commercial use for several decades. This chemistry is of great interest because the materials used are inexpensive, environmental friendly, and abundant. In addition, alkaline chemistry offers a better energy density and shelf life than some other aqueous batteries such as lead-acid technology. Rechargeability of EMD, however, is challenging and commercial rechargeable batteries based on this electrochemistry have faced limited success due to the limitation of cycling performance. We have investigated the causes of cycling failures using flatplate-type cathode (MnO2) and anode (Zinc) electrodes which are amenable to large scale production. Electrochemical and physiochemical characterizations were performed and will be presented. Flatplate-type electrodes were prepared by producing paste-like mixtures of cathode and anode materials and applying them onto expanded Ni (cathode) and expanded brass (anode) current collectors. The cathode mixture contained EMD, graphite, additives, SBR binder and CMC gelling agent. Anode mixtures were prepared from zinc, zinc oxide, hydrogen inhibitors, teflon binder and carbapol gelling agent. The alkaline electrolyte (9M) was prepared from DI-water and KOH salt. All the cell hardware used was developed in-house. For the cathode electrode, a number of chemical additives such as BaSO4, Sr(OH)2·8H2O, Ca(OH)2, and Bi2O3were investigated at 5 wt. %. Deep discharge cycling (to 1.1 V) reduces the initial specific capacity of 250 mAh g-1 by 50% after 20 cycles. The specific capacity reduces, although at a slower rate, to about 50 mAh g-1 after 100 cycles. Additives such as BaSO4show marginal improvement in the earlier cycles (cycles 10-50), but beyond cycle 50 no noticeable effect was observed. Deeper cycling can be combined with shallow depths of discharge (1.4 – 1.45 V) to extend the cycle life-time of the batteries. Figure 1 shows the cycling performance of RAM cells for combined deep/shallow depths of discharge at C/2 and C/10 rates. Post-cycling XRD analysis of the cathode electrode shows formation of a todorokite-like phase which could limit the cycling performance. Impedance studies show a rapid increase in the charge transfer resistance, indicating that formation of surface inactive phases also play a role in reducing the capacity upon cycling. Figure 1
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".