Atomic Layer Deposition of Highly Stable Manganese-Iron Oxide Bifunctional Catalysts for Zinc-Air Batteries
Notice bibliographique
Résumé
Incorporating intermittent renewable energy sources into the power grid will require large amounts of grid-scale energy storage. Electrochemical batteries are a versatile and scalable energy storage option and, hence, Li-ion batteries have been widely adopted to store excess wind and solar energy [1]. Li-ion batteries, however, have a relatively low energy density and serious safety concerns. An alternative electrochemical battery option lies with zinc-air batteries. This technology uses lower cost materials and is overall much safer. Furthermore, zinc-air batteries have a much larger theoretical energy density than Li-ion batteries [2]. The major impediment to wide-scale adoption of zinc-air batteries is the low energy efficiency because of the poor reaction kinetics at the air electrode. Both the charge and discharge reactions at the air electrode are sluggish and require the use of catalysts to obtain practicable performance. However, many catalysts active towards the charge reaction are not active towards the discharge reaction, and vice versa. The development of a catalyst active towards both the charge and discharge reactions, known as a bifunctional catalyst, is therefore a high priority [3]. Furthermore, catalysts employed in zinc-air batteries often show instability, with performance degradation evident after a few cycles. Ultimately, a highly stable bifunctional zinc-air battery catalyst is of the utmost importance. The aim of this work is to develop highly stable bifunctional catalysts for zinc-air batteries using atomic layer deposition (ALD). With ALD, extremely conformal catalyst coatings can be deposited directly on the air electrode of a zinc-air battery. The self-limiting surface reactions of ALD ensure that electrode porosity is maintained while maximizing the total coating surface area [4]. Since ALD operates in the gas phase, catalytic coatings can be deposited deep within the pores of the air electrode. This will help maintain the three-phase boundary necessary for the discharge reaction and ultimately improve the stability of a zinc-air battery [5]. To create a bifunctional catalyst, two ALD processes, one for manganese oxide and another for iron oxide, is combined into one ALD supercycle, depositing a mixed manganese-iron oxide. Since manganese oxide is a well-established discharge catalyst [6], and iron oxide demonstrates activity towards the charge reaction [7], this mixed manganese-iron oxide exhibits bifunctional activity in a zinc-air battery. An optimized supercycle process will be discussed and full-cell battery test results showcased. Specifically, the bifunctional efficiency of a zinc-air battery can be improved by more than 10% by using the mixed manganese-iron oxide catalyst. In addition, the high stability of the manganese-iron oxide catalyst is demonstrated, where bifunctional efficiency can be maintained at over 95% of the initial value over 200 cycles. Materials characterization of the mixed manganese-iron oxide, deposited through ALD, is also included. [1] L. Trahey, F. R. Brushett, N. P. Balsara, G. Ceder, L. Cheng, Y. M. Chiang, N. T. Hahn, B. J. Ingram, S. D. Minteer, J. S. Moore, K. T. Mueller, L. F. Nazar, K. A. Persson, D. J. Siegel, K. Xu, K. R. Zavadil, V. Srinivasan, and G. W. Crabtree, “Energy Storage Emerging: A Perspective from the Joint Center for Energy Storage Research,” Proc. Natl. Acad. Sci. U. S. A. , vol. 117, no. 23, pp. 12550–12557, 2020. [2] J. Fu, R. Liang, G. Liu, A. Yu, Z. Bai, L. Yang, and Z. Chen, “Recent Progress in Electrically Rechargeable Zinc – Air Batteries,” Adv. Mater. , vol. 31, no. 31, p. 1805230, 2019. [3] E. Davari and D. G. Ivey, “Bifunctional electrocatalysts for Zn – air batteries,” Sustain. Energy Fuels , vol. 2, no. 1, pp. 39–67, 2018. [4] C. Detavernier, J. Dendooven, S. Pulinthanathu Sree, K. F. Ludwig, and J. A. Martens, “Tailoring nanoporous materials by atomic layer deposition,” Chem. Soc. Rev. , vol. 40, no. 11, pp. 5242–5253, 2011. [5] M. P. Clark, M. Xiong, K. Cadien, and D. G. Ivey, “High Performance Oxygen Reduction/Evolution Electrodes for Zinc − Air Batteries Prepared by Atomic Layer Deposition of MnOx,” ACS Appl. Energy Mater. , vol. 3, no. 1, pp. 603–313, 2020. [6] M. P. Clark, T. Muneshwar, M. Xiong, K. Cadien, and D. G. Ivey, “Saturation Behavior of Atomic Layer Deposition MnOx from Bis(Ethylcyclopentadienyl) Manganese and Water: Saturation Effect on Coverage of Porous Oxygen Reduction Electrodes for Metal-Air Batteries,” ACS Appl. Nano Mater. , vol. 2, no. 1, pp. 267–277, 2019. [7] M. Labbe, M. P. Clark, Z. Abedi, A. He, K. Cadien, and D. G. Ivey, “Atomic layer deposition of iron oxide on a porous carbon substrate via ethylferrocene and an oxygen plasma,” Surf. Coatings Technol. , vol. 421, p. 127390, 2021.
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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 ».