Determining the Mechanism of Self-Discharge in Rechargeable Zn Electrodes Using a Novel but Simple Method
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».