Iron Oxide As an Oxygen Evolution Catalyst for Zinc-Air Batteries Synthesized Via Atomic Layer Deposition
Bibliographic record
Abstract
Implementation of renewable energy into the power grid is a necessary step for a more sustainable future. However, this process is strongly dependent on the capacity of practical energy storage technologies available. Zinc-air batteries show great promise for energy storage, boasting high theoretical energy density, inexpensive electrode materials, and excellent safety. Current development of zinc-air batteries is, however, stifled by the sluggish oxygen kinetics occurring both during discharge and charge. While precious metals such as platinum and ruthenium are considered to be good catalysts, more abundant, inexpensive transition metal catalysts have been found to perform just as well and with better cycling stability [1], [2]. This work focuses on the development of transition metal oxide catalysts using atomic layer deposition (ALD). ALD is a gas phase deposition technique that generates conformal thin films through the use of self-terminating surface reactions. In particular, iron oxide catalysts, which promote the oxygen evolution reaction, are synthesized via ALD using an ethylferrocene precursor. Iron oxide growth from this precursor is enabled by an oxygen plasma reactant. Recipe development is showcased, demonstrating saturating behaviour for the growth of iron oxide films. The synthesized catalytic films are deposited directly onto a porous carbon substrate (gas diffusion layer or GDL), which is used as the air electrode in metal-air batteries. The high surface area GDL takes full advantage of ALD’s conformal nature to maximize the surface area of the deposited catalytic film, optimizing catalytic performance. As well, compared with other deposition techniques, ALD increases the depth of catalytic loading into the pores of the electrode. This increases the three-phase boundary volume consisting of gaseous oxygen, aqueous hydroxide ions, and solid catalytic active sites, thereby improving battery performance [3]. Deposited iron oxide films are tested through various electrochemical techniques to quantify catalytic activity. Iron oxide is combined with other transition metal oxides with the goal of creating a bifunctional catalyst, which is active for both the charge and discharge reactions in a zinc-air battery. [1] 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. [2] D. Aasen, M. Clark, and D. G. Ivey, “A Gas Diffusion Layer Impregnated with Mn3O4 -Decorated N-Doped Carbon Nanotubes for the Oxygen Reduction Reaction in Zinc-Air Batteries,” Batter. Supercaps, vol. 2, pp. 1–13, 2019. [3] Y. Li and H. Dai, “Recent advances in Zinc-air batteries,” Chem. Soc. Rev., vol. 43, no. 15, pp. 5257–5275, 2014.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".