Zinc and Nickel Based Oxides Anchored Onto Nitrogen-Doped Carbon Nanotubes As Bifunctional Catalysts for Zinc-Air Batteries
Bibliographic record
Abstract
Rechargeable zinc-air batteries are a promising battery technology as they are safe, environmentally friendly, low cost, and have a high theoretical energy density. However, there are several challenges with zinc-air batteries which occur mainly at the air electrode. The main challenge is the slow reaction kinetics for the oxygen evolution and reduction reactions (OER/ORR). To improve the OER and ORR precious metal catalysts have typically been added to the air electrode. However, using precious metals as catalyst materials inhibits large scale applications due to the high material cost and material scarcity. Transition metal oxides are an effective lower cost alternative. Combining transition metal oxides with nanocarbon structures can further increase OER and ORR rates and improve electrochemical stability. [1], [2] In this study, a combination of nitrogen-doped nanotubes (N-CNTs) and transition metal oxides is impregnated into a gas diffusion layer (GDL) made from porous carbon paper. This synthesis process ensures that the catalyst is present throughout the GDL, improving the reaction kinetics [2]. Zinc- and nickel-based tri-metallic and tetra-metallic oxides are investigated. Nickel-based oxides have demonstrated potential for high electrochemical activity but have not been fully studied [3]. Zinc is chosen, in part, because it appears to be incorporated into the transition metal oxide catalysts during battery cycling, so its effect on battery behavior is worth further study. The metal oxide combinations are determined from previous bi-metallic and initial tri-metallic oxide results. For each combination, different ratios are tested, since the amount of each transition metal affects the OER and ORR kinetics. Each combination is examined through various electrochemical and microstructural characterization techniques; e.g., linear sweep voltammetry, electrochemical impedance spectroscopy, electron microscopy and energy-dispersive X-ray spectroscopy. A Design of Experiments approach is used to determine the best metal ratios for each combination. The overall goal of this work is to find a catalyst that increases the efficiency, lowers the voltage gap, and improves the cyclability of zinc-air batteries. [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] J. Yi et al., “Non-noble Iron Group (Fe, Co, Ni)-Based Oxide Electrocatalysts for Aqueous Zinc-Air Batteries: Recent Progress, Challenges, and Perspectives,” Organometallics, vol. 38, no. 6, pp. 1186–1199, 2019.
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".