Voltage Loss Analysis of Zinc-Cerium Redox Flow Batteries
Notice bibliographique
Résumé
Redox flow batteries (RFBs) are a relatively new generation of electrochemical devices suitable for large-scale energy storage applications. The separation between the electrolyte storage tanks and the electrochemical cell in RFBs simplifies the battery scale-up and facilitates the energy/power ratio tuning. In comparison to current energy storage devices, RFBs are beneficial due to their portability, flexibility and low maintenance cost [1]. Among the different types of RFBs investigated, those based on zinc and cerium are very attractive due to the large negative and positive electrode potentials in an aqueous media. Thus, zinc-cerium RFBs are capable of providing one of the highest cell voltages (~ 2.4 V) among flow batteries and a large theoretical energy density [2]. To date, Zn-Ce RFBs have primarily been investigated galvanostatically to determine their charge, voltage and energy efficiencies and attempts have been made to suppress the rate of the hydrogen and oxygen evolution side reactions [3-6]. In order to further optimize the performance of these batteries and to elucidate the future pathways to enhance their efficiency, the sources of voltage loss in the battery during discharge must be identified and the role of the positive and negative half-cells in the voltage loss determined. Toward this goal, we have conducted in situ polarization and EIS experiments on a full-cell Zn-Ce RFB with reference electrodes inserted in the system [7]. The insertion of the reference electrodes in the RFB enables us to decouple the contribution of negative and positive electrodes to the total performance loss. At low and intermediate current densities, the main contributor to the voltage loss during discharge is the kinetic overpotential of the negative Zn/Zn2+ half-cell. On the other hand, at high current densities, mass transfer limitations at the positive Ce3+/Ce4+ half-cell cause a large potential drop in the system. From in situ kinetic studies, we have measured an exchange current density of ∼ 7.4×10−3 A cm−2 for Zn oxidation and ∼ 24.2×10−3 A cm−2 for Ce4+ reduction, which is consistent with our previous findings from battery operation that the kinetics of the negative electrode reaction is slow compared to that of the positive electrode at low-to-intermediate current densities. The use of an alternative mixed methanesulfonate-chloride negative electrolyte to reduce the kinetic overpotential of the negative half-cell reaction and the influence of the flow rate on the mass-transfer rate of the positive half-cell reaction have also been investigated and will be discussed in this presentation. [1] De Leon, C. P., Frías-Ferrer, A., González-García, J., Szánto, D. A., & Walsh, F. C. (2006). Redox flow cells for energy conversion. Journal of power sources, 160(1), 716-732. [2] Walsh, F. C., Ponce de Léon, C., Berlouis, L., Nikiforidis, G., Arenas‐Martínez, L. F., Hodgson, D., & Hall, D. (2015). The development of Zn–Ce hybrid redox flow batteries for energy storage and their continuing challenges. ChemPlusChem, 80(2), 288-311. [3] Leung, P. K., Ponce-de-León, C., Low, C. T. J., & Walsh, F. C. (2011). Zinc deposition and dissolution in methanesulfonic acid onto a carbon composite electrode as the negative electrode reactions in a hybrid redox flow battery. Electrochimica Acta, 56(18), 6536-6546. [4] Nikiforidis, G., Berlouis, L., Hall, D., & Hodgson, D. (2014). An electrochemical study on the positive electrode side of the zinc–cerium hybrid redox flow battery. Electrochimica Acta, 115, 621-629. [5] Amini, K., & Pritzker, M. D. (2018). Electrodeposition and electrodissolution of zinc in mixed methanesulfonate-based electrolytes. Electrochimica Acta, 268, 448-461. [6] Amini, K., & Pritzker, M. D. (2019). Improvement of zinc-cerium redox flow batteries using mixed methanesulfonate-chloride negative electrolyte. Applied Energy, 255, 113894. [7] Amini, K., & Pritzker, M. D. (2020). In situ polarization study of zinc–cerium redox flow batteries, Journal of Power Sources, 471, 228463
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 |
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 ».