Enhancing Lithium‐Oxygen Battery Performance by Optimizing the Interaction of Cathode Materials and Soluble FePc Redox Mediator
Bibliographic record
Abstract
Abstract Macrocyclic redox mediators (RMs) such as iron(II) phthalocyanine (FePc) can improve lithium–oxygen (Li─O 2 ) battery performance by shuttling electrons and oxygen. However, their low solubility in electrolytes due to strong π–π interaction with carbon‐based cathodes (e.g., 3D graphene) limits practical applications. Herein, non‐sp 2 ‐carbon materials (MoN, TiN, and Ti 3 C 2 T x ) are employed as cathodes to regulate cathode‐FePc interactions, thereby increasing FePc solubility and improving Li─O 2 battery performance. For cathode‐FePc coupling catalysts, the solubility of FePc rises as its adsorption strength on cathodes (3DG, MoN, TiN, and Ti 3 C 2 T x ) decreases, creating a “volcano‐shaped” correlation with cathode‐FePc@battery performances. Correspondingly, the total resistance ( R ESR = R s + R ct ) of the batteries after charging exhibits an “inverted‐volcano” trend. The optimized TiN‐FePc catalyst achieves the highest cycling stability (392 cycles). Control experiments and density functional theory (DFT) calculations demonstrate that TiN‐FePc catalyst maintains high FePc concentration in electrolyte while facilitating electron transfer and oxygen shuttling, significantly enhancing catalytic activity. This work provides an efficient strategy for designing high‐performance Li─O 2 batteries by optimizing RMs‐cathode interactions.
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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".