Transition Metal Oxides Anchored Onto Co-Doped Carbon Nanotubes As Bifunctional Electrocatalysts
Bibliographic record
Abstract
It has been well established that rechargeable zinc-air batteries (ZABs) are safe, environmentally friendly, and have a high theoretical energy density, making them a promising battery technology. Like many technologies there are challenges that need to be overcome. The main setback for ZABs is the slow reaction kinetics for the oxygen reduction reaction and the oxygen evolution reaction (ORR/OER) at the air electrode. Traditionally, adding precious metal catalysts to the air electrode, like Pt/Ru, will improve the reaction kinetics. However, precious metal catalysts are expensive and unstable during cycling of the discharge and charge reactions. Transition metal oxides combined with nanocarbon materials have been shown to improve the ORR and OER kinetics while staying cost effective [1], [2]. Heteroatom doping, e.g. -N, -B, and -S, of carbon materials has the ability to improve ORR performance [3]. When co-doping carbon nanotubes with sulfur and nitrogen, sulfur has the ability to replace the carbon atoms easily, creating more active sites for catalyst material [3], [4]. The purpose of this study is to investigate the effects that co-doping of carbon nanotubes with nitrogen and sulfur combined with transition metal oxides have on the reaction kinetics at the air electrode. Impregnating the combined transition metal oxides and N,S co-doped CNTs into a gas diffusion layer (GDL) made of porous carbon ensures that the catalyst is deposited throughout the GDL [2]. Previous work has been done on Zn- and Ni-based tri-metallic and tetra-metallic oxides with N-CNTs. Nickel-based oxides had high efficiency, while the Zn-based tetra-metallic oxides demonstrated poor ORR performance leading to poor overall efficiency. Based on the literature, it is predicted that using N,S-CNTs with tri-metallic and tetra-metallic oxides will improve ORR performance. Various electrochemical and microstructural characterization techniques; e.g., linear sweep voltammetry, electrochemical impedance spectroscopy, electron microscopy, and energy dispersive X-ray spectroscopy, will be used to examine each sample. The goal of this work is to improve the ORR performance of previously studied tri-metallic and tetra-metallic oxides and improve the cyclability of ZABs. [1] N. Xu et al., “Self-Assembly formation of Bi-functional Co 3 O 4 /MnO 2-CNTs hybrid catalysts for achieving both high energy/power density and cyclic ability of rechargeable zinc-Air battery,” Sci. Rep., vol. 6, no. September, pp. 1–10, 2016. [2] D. Aasen, M. Clark, and D. G. Ivey, “ A Gas Diffusion Layer Impregnated with Mn 3 O 4 ‐Decorated N‐Doped Carbon Nanotubes for the Oxygen Reduction Reaction in Zinc‐Air Batteries ,” Batter. Supercaps, vol. 2, no. 10, pp. 882–893, 2019. [3] I. M. Patil, V. Reddy, M. Lokanathan, and B. Kakade, “Nitrogen and sulphur co-doped multiwalled carbon nanotubes as an efficient electrocatalyst for improved oxygen electroreduction,” Appl. Surf. Sci., vol. 449, pp. 697–704, 2018, doi: 10.1016/j.apsusc.2017.12.124. [4] J. Wang et al., “Nitrogen and Sulfur Co-Doping of Partially Exfoliated MWCNTs as 3-D Structured Electrocatalysts for the Oxygen Reduction Reaction,” J. Mater. Chem. A, vol. 4, no. 15, pp. 5678–5684, 2016, doi: 10.1039/c6ta00490c.
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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".