Low Concentration Slurry Electrodes for Redox Flow Batteries
Notice bibliographique
Résumé
Iron redox flow batteries are a promising option for utility scale energy storage. In redox flow batteries (RFB), the power and energy storage capacities are decoupled, making them highly scalable 1,2 . Due to its high abundance, low cost, and low toxicity, iron is very attractive as a reactive species for both the positive and negative half cells in large scale redox flow batteries 3 . The Fe(II)/Fe(III) reaction is utilized at the positive electrode while the Fe(0)/Fe(II) reaction is used at the negative electrode. Unfortunately, the Fe(II) reduction reaction used in the negative cell involves plating solid iron onto the electrode during charge. This plating reaction limits the battery’s capacity based on the spatial constraints of the flow cell, coupling the power and storage capacities of the flow battery and limiting its scalability 2 . Slurry electrodes, consisting of a dispersion of conductive particles in the electrode, have been proposed as solution for this issue 4 . By having the metal deposit onto the mobile dispersion of particles, as in Figure 1B, instead of the stationary electrode as in Figure 1A, the power and storage capacities of a hybrid flow battery can be decoupled. Slurry electrodes have also been proposed in a number of other applications such as water deionization and supercapacitors 5 . Their use has also been studied for use in fully soluble RFB chemistries, such as all-vanadium 6,7 . However, nearly all of the previous work in slurry electrodes has been in highly concentrated slurries in order to take advantage of the conductivity of the percolated particle network. Unfortunately, these highly loaded slurries can be viscous and can cause clogs and failures in a flowing system such as an RFB 4,7 . In this work, the electrochemical behavior of slurries below the percolation threshold are investigated via voltammetry in a custom flow cell. The percolation threshold of a slurry is identified and the modified behavior of the Fe (II)/Fe (III) reaction is measured as a function of slurry concentration and flow rate. The results suggest that significant enhancement of the electrochemically active surface area can be achieved below the percolation threshold. (1) Dinesh, A.; Olivera, S.; Venkatesh, K.; Santosh, M. S.; Priya, M. G.; Inamuddin; Asiri, A. M.; Muralidhara, H. B. Iron-Based Flow Batteries to Store Renewable Energies. Environ. Chem. Lett. 2018 , 16 (3), 683–694. https://doi.org/10.1007/s10311-018-0709-8. (2) Weber, A. Z.; Mench, M. M.; Meyers, J. P.; Ross, P. N.; Jeffrey, T.; Liu, Q. Redox Flow Batteries , a Review Environmental Energy Technologies Division , Lawrence Berkeley National Laboratory , Department of Mechanical , Aerospace and Biomedical Engineering , University of Tennessee , Department of Chemical Engineering , McGill Un. 1–72. (3) Petek, T. J. Enhancing the Capacity of All-Iron Flow Batteries: Understanding Crossover and Slurry Electrodes. Ph.D. Thesis 2015 , No. May. (4) Petek, T. J.; Hoyt, N. C.; Savinell, R. F.; Wainright, J. S. Slurry Electrodes for Iron Plating in an All-Iron Flow Battery. J. Power Sources 2015 , 294 , 620–626. https://doi.org/10.1016/j.jpowsour.2015.06.050. (5) Mourshed, M.; Niya, S. M. R.; Ojha, R.; Rosengarten, G.; Andrews, J.; Shabani, B. Carbon-Based Slurry Electrodes for Energy Storage and Power Supply Systems. Energy Storage Mater. 2021 , 40 (April), 461–489. https://doi.org/10.1016/j.ensm.2021.05.032. (6) Percin, K.; van der Zee, B.; Wessling, M. On the Resistances of a Slurry Electrode Vanadium Redox Flow Battery. ChemElectroChem 2020 , 7 (9), 2165–2172. https://doi.org/10.1002/celc.202000242. (7) Lohaus, J.; Rall, D.; Kruse, M.; Steinberger, V.; Wessling, M. On Charge Percolation in Slurry Electrodes Used in Vanadium Redox Flow Batteries. Electrochem. commun. 2019 , 101 (March), 104–108. https://doi.org/10.1016/j.elecom.2019.02.013. Figure 1
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 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 | 0,000 | 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 | 0,000 |
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».