Investigation of Transition Metal-Based (Mn, Co, Ni, Fe) Trimetallic Oxide Nanoparticles on N-doped Carbon Nanotubes as Bifunctional Catalysts for Zn-Air Batteries
Bibliographic record
Abstract
Various transition metal-based bimetallic and trimetallic oxides on N-CNTs were successfully synthesized in a one-pot process. Porous gas diffusion layers (GDLs) were simultaneously impregnated with the catalysts during synthesis, resulting in an efficient and scalable preparation technique for nano-composite air electrodes. NiMnO x /N-CNT, NiFeO x /N-CNT, and (Co,Fe) 3 O 4 /N-CNT catalysts were the highest performing bimetallic oxides based on battery rate test results. Therefore, Ni-Co-Fe, Ni-Mn-Fe, and Mn-Co-Fe systems were investigated as combined trimetallic oxide/N-CNT catalysts for Zn-air batteries. Trimetallic oxides on N-CNTs exhibited improved OER activity and comparable ORR activity relative to the bimetallic oxide catalysts in linear sweep voltammetry (LSV) tests. Discharge/charge efficiencies for tri-metallic oxides on N-CNTs calculated from battery rate test measurements at a current density of 20 mA cm −2 were 60.5%, 60.7%, and 60.0% for NCFO/N-CNT, NMFO/N-CNTs and MCFO/N-CNT, respectively. Bifunctional cycling of all trimetallic oxide/N-CNT electrodes at 10 mA cm −2 for 100 h showed excellent stability, particularly the NCFO/N-CNT catalyst. Additionally, the efficiencies for NCFO/N-CNT, NMFO/N-CNT and MCFO/N-CNT samples were 58.5%, 57.9% and 57.2%, respectively, after cycling, which compare favorably with that of Pt-Ru/C (55.3%). Trimetallic oxides on N-CNTs are, therefore, excellent candidates as high performing, non-precious metal catalysts for Zn-air batteries.
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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".