Fe<sub>2</sub>O<sub>3</sub> Nanoparticle Seed Catalysts Enhance Cyclability on Deep (Dis)charge in Aprotic LiO<sub>2</sub> Batteries
Bibliographic record
Abstract
Abstract Although the high energy density of LiO2 chemistry is promising for vehicle electrification, the poor stability and parasitic reactions associated with carbon‐based cathodes and the insulating nature of discharge products limit their rechargeability and energy density. In this study, a cathode material consisting of α‐Fe2O3 nanoseeds and carbon nanotubes (CNT) is presented, which achieves excellent cycling stability on deep (dis)charge with high capacity. The initial capacity of Fe2O3/CNT electrode reaches 805 mA h g−1 (0.7 mA h cm−2) at 0.2 mA cm−2, while maintaining a capacity of 1098 mA h g−1 (0.95 mA h cm−2) after 50 cycles. The operando structural, spectroscopic, and morphological analysis on the evolution of Li2O2 indicates preferential Li2O2 growth on the Fe2O3. The similar d‐spacing of the (100) Li2O2 and (104) Fe2O3 planes suggest that the latter epitaxially induces Li2O2 nucleation. This results in larger Li2O2 primary crystallites and smaller secondary particles compared to that deposited on CNT, which enhances the reversibility of the Li2O2 formation and leads to more stable interfaces within the electrode. The mechanistic insights into dual‐functional materials that act both as stable host substrates and promote redox reactions in LiO2 batteries represent new opportunities for optimizing the discharge product morphology, leading to high cycling stability and coulombic efficiency.
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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".