Electrodeposited and Oxidized Mn/Co-Fe As Bi-Functional Electrocatalysts for Rechargeable Zinc-Air Batteries
Bibliographic record
Abstract
Electrochemically rechargeable zinc-air batteries are one of the best candidates to store very large amounts of electrical energy[1]. However, due to the sluggish ORR (oxygen reduction reaction) and OER (oxygen evolution reaction) at the air electrode, the efficiency of zinc-air batteries is comparatively low, leading to energy losses. In addition, the stability of the catalysts is poor because they can peel off or pulverize during battery cycling. The air electrode contains a gas diffusion layer (GDL) as the substrate and a catalyst layer. The key to increasing efficiency and stability is to properly combine catalysts with the GDL to facilitate the ORR and OER. Manganese oxides have been shown to be excellent ORR catalysts, while oxidized Co-Fe performs well in catalyzing OER[2,3]. The proper amalgamation of the two materials may be able to generate a highly efficient bi-functional catalyst that can catalyze both ORR and OER. In this study, Mn and Co-Fe were sequentially electrodeposited onto the GDL and then annealed in air to produce transition metal oxide catalysts. The fabricated material was then assembled into a zinc-air battery as the air electrode component to run battery cycling tests. Scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) results show that a porous Mn-layer was firstly formed on the GDL and then covered by another layer of Co-Fe nanoparticles (Fig. 1). The nanocomposite structure provides a high surface area for electrochemical reactions. The electrocatalytic properties of the bi-functional catalysts were studied by cyclic voltammetry (CV) in 6M KOH solution. The CV results demonstrate that the oxidized Mn/Co-Fe nanocomposite exhibits activities for both ORR and OER. Preliminary cell test results show that the discharge-recharge efficiency is increased from 50% to 58% at 10 mA/cm2 current density when the catalysts are used in the battery. The efficiency can be further improved with optimized catalysts design and fabrication. In addition, the deposited catalyst layer shows strong adhesion to the GDL and excellent stability after 40 hours of battery testing. The influence of electrodeposition conditions on the electrochemical performance is also discussed. References 1. Y. Li and H. Dai, Chemical Society Reviews, 2014, 43, 5257-5275. 2. K. Zhang, X. Han, Z. Hu, X. Zhang, Z. Tao and J. Chen, Chemical Society Reviews, 2015, 44, 699-728. 3. J. Ahmed, B. Kumar, A. M. Mugweru, P. Trinh, K. V. Ramanujachary, S. E. Lofland and A. K. Ganguli, The Journal of Physical Chemistry C, 2010, 114, 18779-18784. Fig. 1 SEM image of Co-Fe nanoparticles. Figure 1
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 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".