Determining the Mechanism of Self-Discharge in Rechargeable Zn Electrodes Using a Novel but Simple Method
Bibliographic record
Abstract
Rechargeable zinc (Zn) electrodes for aqueous batteries are poised to play an important role in grid storage battery systems in the near future. Zn is favoured as a negative electrode material because it is cheap, non-toxic, has a low potential, fast kinetics and it has a high overpotential for hydrogen evolution. This last advantage is what allows typical primary alkaline AA batteries to last up to 10 years on the shelf, but this impressive shelf life is not shared by rechargeable batteries with Zn electrodes. For example, a PowerGenix Sub C NiZn battery has a shelf life of less than 1 year.1 Frustratingly, the rate determining step of self-discharge in rechargeable Zn electrodes has not been clearly identified in the literature since many authors have reported ambiguous and at times contradictory results in that regard.2 We have developed a simple, but novel, technique of measuring the self-discharge rate of rechargeable Zn electrodes, and have used it to elucidate the rate determining step of the Zn self-discharge mechanism. The method, deemed the charge-wait-discharge (CWD) technique, is depicted in Figure 1. It involves charging a Zn electrode, leaving it at open circuit for a set amount of time, and then discharging it to measure how much capacity remains. By performing this experiment repeatedly with increasing amounts of time left at open circuit, a plot of capacity vs open circuit time can be formed. The slope of this plot yields the self-discharge rate, as a coarse function of time. One of the principal advantages to this method is that it can easily be performed on full, unmodified cells, as opposed to the more traditional method of measuring self-discharge which involves measuring the volume of H2 gas released by the Zn electrode over a long period of time.3 Using the CWD technique, we explored the effect on self-discharge rate of KOH concentration in the electrolyte, surface area of the deposition, and current collector material. Our results indicate that only the current collector material has a strong effect on self-discharge rate, suggesting that the rate determining step of Zn self-discharge is H2 evolution on the current collector (as opposed to on the Zn deposit) as shown in Figure 2. In which case, to minimize Zn self-discharge the exposed surface area of the current collector should be minimized and the overpotential for H2evolution on the current collector material should be maximized. In the literature, several electrolyte additives have been claimed to reduce self-discharge rates, reduce dendrite growth and/or extend cycle life. The effect of these additives on self-discharge was screened using the CWD technique. These results will be presented, along with the best current collector material identified in our studies. References PowerGenix, http://powergenix.com/wp-content/uploads/2014/04/pgx_nizn_subc_datasheet2.pdf, Accessed: March 13, 2016, Last Updated: April 2009. X. G. Zhang, Corrosion and Electrochemistry of Zinc, 1st ed., Plenum Press, New York (1996). R. N. Snyder and J. J. Lander, Electrochem. Technol., 3, 5-6 (1965). 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".