Synergistic Bifunctional Catalyst Design based on Perovskite Oxide Nanoparticles and Intertwined Carbon Nanotubes for Rechargeable Zinc–Air Battery Applications
Bibliographic record
Abstract
Advanced morphology of intertwined core-corona structured bifunctional catalyst (IT-CCBC) is introduced where perovskite lanthanum nickel oxide nanoparticles (LaNiO3 NP) are encapsulated by high surface area network of nitrogen-doped carbon nanotubes (NCNT) to produce highly active and durable bifunctional catalyst for rechargeable metal-air battery applications. The unique composite morphology of IT-CCBC not only enhances the charge transport property by providing rapid electron-conduction pathway but also facilitates in diffusion of hydroxyl and oxygen reactants through the highly porous framework. Confirmed by electrochemical half-cell testing, IT-CCBC in fact exhibits very strong synergy between LaNiO3 NP and NCNT demonstrating bifunctionality with significantly improved catalytic activities of oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Furthermore, when compared to the state-of-art catalysts, IT-CCBC outperforms Pt/C and Ir/C in terms of ORR and OER, respectively, and shows improved electrochemical stability compared to them after cycle degradation testing. The practicality of the catalyst is corroborated by testing in a realistic rechargeable zinc-air battery utilizing atmospheric air in ambient conditions, where IT-CCBC demonstrates superior charge and discharge voltages and long-term cycle stability with virtually no battery voltage fading. These improved electrochemical properties of the catalyst are attributed to the nanosized dimensions of LaNiO3 NP controlled by simple hydrothermal technique, which enables prolific growth of and encapsulation by highly porous NCNT network. The excellent electrochemical results presented in this study highlight IT-CCBC as highly efficient and commercially viable bifunctional catalyst for rechargeable metal-air battery applications.
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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".