A Two-Dimensional Transient Model to Investigate the Influence of Flow Field Design on Zinc Deposition and Performance in a Zinc-Iodide Flow Battery
Notice bibliographique
Résumé
Zinc-based flow batteries are attractive because they have high aqueous solubility and hence high energy density, as well as rapid kinetics [1,2]. Among the aqueous redox flow batteries (RFBs), the zinc-iodide flow battery has been reported to have very high discharge energy density [1]. However, aqueous zinc batteries suffer from dendrite formation resulting in capacity fading and a decreased battery performance. In our previous study [3], we evaluated a novel flow field design for the zinc-iodide flow battery. When compared to a conventional flow-by (FB) mode, significantly higher charge and energy efficiencies were obtained with the novel arrangement. In the novel flow design, part of the was drawn through the porous electrode towards the current feeder, and this was found to enhance the effective surface area and decrease the ohmic loss. This design, called flow-by-through (FBT), with a component of flow in the direction of the current feeder, can prevent the blockage of the mass/ion transport pathway and enhance the flow distribution in hybrid flow batteries [4]. Based on X-ray computed tomography and SEM characterization, a denser zinc deposit, with more zinc deposition inside the felt were observed with the FBT mode, which may explain the increased discharge capacity and cycle life of the battery [3]. A mathematical model of the zinc-iodide RFB can help us better understand the battery performance in the different flow modes. Models of RFBs have been used to investigate the impact of the flow field on performance since 1980 [5]. In this study, we have used a two-dimensional transient model with the COMSOL multi-physics software package to study the galvanostatic charge/discharge behavior of the zinc-iodide RFB in FB and FBT modes. The model considers the electrochemical kinetics, mass transport and the current and potential distribution in a single cell zinc-iodide flow battery. This is the first reported modeling study of the influence of the FBT design on the performance of an RFB. The galvanostatic charge-discharge of a zinc-iodide battery was modelled at a current density of 20 mA cm -2 , and a charge duration of 20000 s, corresponding to a state of charge of 25.7%. These conditions were chosen to enable comparison and validation with experimental data are shown in Figure 1. The rate of zinc ion consumption and the amount of zinc deposition on the graphite felt electrode for both FB and FBT arrangements were evaluated using the model. The distribution of zinc deposition, potential and current, will be presented, which provide new insights into the impact of the flow arrangement on the battery performance. The model will be applied to predict cell voltage and efficiency, maximum discharge capacity and maximum discharge power density of the zinc-iodide RFB for FB and FBT arrangements. [1] Li, B., Nie, Z., Vijayakumar, M., Li, G., Liu, J., Sprenkle, V., & Wang, W. (2015). Ambipolar zinc-polyiodide electrolyte for a high-energy density aqueous redox flow battery. Nature communications , 6 (1), 1-8. [2] Higashi, S., Lee, S. W., Lee, J. S., Takechi, K., & Cui, Y. (2016). Avoiding short circuits from zinc metal dendrites in anode by backside-plating configuration. Nature communications , 7 (1), 1-6. [3] ShakeriHosseinabad, F., Daemi, S. R., Momodu, D., Brett, D. J., Shearing, P. R., & Roberts, E. P. (2021). Influence of Flow Field Design on Zinc Deposition and Performance in a Zinc-Iodide Flow Battery. ACS Applied Materials & Interfaces , 13 (35), 41563-41572. [4] Zhou, X., Lin, L., Lv, Y., Zhang, X., Fan, L., & Wu, Q. (2020). Elucidating effects of component materials and flow fields on Sn–Fe hybrid flow battery performance. Journal of Power Sources , 450 , 227613. [5] Trainham, J. A., & Newman, J. (1981). A comparison between flow-through and flow-by porous electrodes for redox energy storage. Electrochimica Acta , 26 (4), 455-469. Figure 1
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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