Perovskite–Nitrogen‐Doped Carbon Nanotube Composite as Bifunctional Catalysts for Rechargeable Lithium–Air Batteries
Bibliographic record
Abstract
Developing an effective bifunctional catalyst is a significant issue, as rechargeable metal-air batteries are very attractive for future energy systems. In this study, a facile one-pot process is introduced to prepare an advanced bifunctional catalyst (op-LN) incorporating nitrogen-doped carbon nanotubes (NCNTs) into perovskite La0.5 Sr0.5 Co0.8 Fe0.2 O3 nanoparticles (LSCF-NPs). Confirmed by half-cell testing, op-LN exhibits synergistic effects of LSCF-NP and NCNT with excellent bifunctionality for both the oxygen reduction reaction and the oxygen evolution reaction. Furthermore, op-LN exhibits comparable performances in these reactions to Pt/C and Ir/C, respectively, which highlights its potential for use as a commercially viable bifunctional catalyst. Moreover, the results obtained by testing op-LN in a practical Li-air battery demonstrate improved and complementary charge/discharge performance compared to those of LSCF-NP and NCNT, and this confirms that simply prepared op-LN is a promising candidate as a highly effective bifunctional catalyst for rechargeable metal-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) | 0.001 | 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".