Atomic Layer Deposition of a Manganese-Iron Mixed Oxide As a Bifunctional Oxygen Catalyst for Zinc-Air Batteries
Notice bibliographique
Résumé
Carbon-free energy sources will become a necessary step in order to mitigate the effects of climate change. In order to integrate these renewable energy sources, which are usually intermittent and not on-demand, into the power grid, a significant amount of energy storage will be required. The popularity of lithium-ion batteries as an energy storage option has exploded in recent years; however, a safer and less expensive alternative may be zinc-air batteries. Zinc-air batteries combine zinc, an abundant and safe metal, with oxygen from the air. Using oxygen as a battery component, however, presents issues because of the slow kinetics of both oxygen reduction and oxygen evolution, the necessary steps in battery discharge and charge, respectively [1]. To compensate, catalysts are utilized that improve reaction kinetics for both the charge and discharge reactions. The benchmark in oxygen reaction catalysis is rare and expensive noble metals, such as platinum and ruthenium, which hinders practicality. On the other hand, inexpensive and abundant transition metal oxides have also shown promising catalytic activity towards these oxygen reactions [2]. Unfortunately, transition metal oxide catalysts that improve the reaction kinetics of the oxygen reduction reaction are typically not active towards the oxygen evolution reaction, and vice versa. However, a ternary oxide of two transition metals may result in a bifunctional catalyst, which is active towards both charge and discharge reactions. The aim of this work is to produce a bifunctional, mixed oxide catalyst through atomic layer deposition (ALD). ALD employs self-terminating surface reactions of a metal-containing gaseous precursor and an oxygen co-reactant to produce very conformal and thin oxide films. Highly porous carbon paper, which typically serves as the air electrode in metal-air batteries, acts as the substrate for ALD of these transition metal oxide films. ALD can conformally coat the high surface area substrate with minimal loss in porosity. Furthermore, ALD can penetrate deeper into the pores of the electrode as compared with other deposition techniques. Together this allows ALD to yield high surface area nanostructured catalysts that maximizes the three-phase region between gaseous oxygen, liquid electrolyte, and the solid catalyst, thereby improving battery performance [3]. Previously studies have shown manganese oxides to be active towards the oxygen reduction reaction, while iron oxide is a component of many oxygen evolution catalysts [3]–[5]. Therefore, a recipe to deposit iron oxide films using ethylferrocene and an oxygen plasma is combined with an already established manganese oxide deposition procedure, with the ultimate goal of creating a mixed transition metal oxide catalyst. Electrochemical characterization techniques are employed to evaluate the bifunctional activity of synthesized transition metal oxides films. The atomic-level control of ALD means that the mixed oxide can be grown in various different proportions. An optimization process to establish the best ALD sequence is showcased, resulting in a bifunctional efficiency of 55% at 20 mA/cm2 for a 10 nm catalytic film. [1] F. Cheng and J. Chen, “Metal-air batteries: From oxygen reduction electrochemistry to cathode catalysts,” Chem. Soc. Rev., vol. 41, no. 6, pp. 2172–2192, 2012. [2] H. Osgood, S. V. Devaguptapu, H. Xu, J. Cho, and G. Wu, “Transition metal (Fe, Co, Ni, and Mn) oxides for oxygen reduction and evolution bifunctional catalysts in alkaline media,” Nano Today, vol. 11, no. 5, pp. 601–625, 2016. [3] 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. [4] M. Xiong, M. P. Clark, M. Labbe, and D. G. Ivey, “A horizontal zinc-air battery with physically decoupled oxygen evolution/reduction reaction electrodes,” J. Power Sources, vol. 393, pp. 108–118, 2018. [5] D. Aasen, M. P. Clark, and D. G. Ivey, “(Co,Fe)3O4 Decorated Nitrogen-Doped Carbon Nanotubes in Nano-Composite Gas Diffusion Layers as Highly Stable Bifunctional Catalysts for Rechargeable Zinc-Air Batteries,” Batter. Supercaps, vol. 3, no. 2, pp. 174–184, 2020.
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 | 0,000 |
| 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) | 0,001 | 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 ».