Zn‐Based Oxides Anchored to Nitrogen‐Doped Carbon Nanotubes as Efficient Bifunctional Catalysts for Zn‐Air Batteries
Bibliographic record
Abstract
Abstract Zn−Co oxide (ZnCoOx), Zn−Mn oxide (ZnMnOx), Zn−Mn−Co oxide (ZMCO), and Zn−Co−Fe oxide (ZCFO) nanoparticles were successfully synthesized on nitrogen‐doped carbon nanotubes in a one‐pot process. Porous carbon paper was simultaneously impregnated with the catalysts during synthesis and used as air electrodes for Zn‐air batteries. ZnMnOx/N‐CNT catalysts had the best ORR performance in half‐cell LSV experiments with a more positive onset potential than that of Pt−Ru/C (−0.067 V and −0.078 V vs Hg/HgO, respectively). ZCFO/N‐CNT catalysts had the best activity towards OER among the Zn‐based oxide catalysts in half‐cell linear sweep voltammetry (LSV) testing with an onset potential of 0.62 V vs Hg/HgO. Round‐trip efficiencies from battery rate tests at a current density of 20 mA cm−2 were 55.3 %, 57.5 %, 58.7 %, and 58.3 % for ZnCoOx/N‐CNT, ZnMnOx/N‐CNT, ZMCO/N‐CNT, and ZCFO/N‐CNT, respectively. Bifunctional cycling of the catalysts was done in a homemade Zn‐air battery at a current density of 10 mA cm−2 for 200 cycles. The final round trip efficiencies for ZnCoOx/N‐CNT, ZnMnOx/N‐CNT, ZMCO/N‐CNT, and ZCFO/N‐CNT were 55.8 %, 56.6 %, 54.2 %, and 55.0 %, respectively. All catalysts except ZCFO/N‐CNT compared favorably with Pt−Ru/C in terms of round‐trip efficiency after cycling (55.3 %). Incorporation of Zn into the metal oxide particles showed improved catalytic activity for ZnCoOx/N‐CNT and ZnMnOx/N‐CNT compared with MnOx/N‐CNT and CoOx/N‐CNT catalysts prepared via the same technique.
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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) | 0.000 | 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".