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Record W3116334169 · doi:10.1149/ma2020-02231694mtgabs

Atomic Layer Deposition of a Manganese-Iron Mixed Oxide As a Bifunctional Oxygen Catalyst for Zinc-Air Batteries

2020· article· en· W3116334169 on OpenAlexaff
Matthew Labbe, M.P. Clark, Ken Cadien, Douglas G. Ivey

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCatalysisBifunctionalChemistryBifunctional catalystOxygenAtomic layer depositionInorganic chemistryOxygen evolutionZincMaterials scienceChemical engineeringNanotechnologyLayer (electronics)ElectrodeElectrochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.223
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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