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 LiO 2 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‐Fe 2 O 3 nanoseeds and carbon nanotubes (CNT) is presented, which achieves excellent cycling stability on deep (dis)charge with high capacity. The initial capacity of Fe 2 O 3 /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 Li 2 O 2 indicates preferential Li 2 O 2 growth on the Fe 2 O 3 . The similar d ‐ spacing of the (100) Li 2 O 2 and (104) Fe 2 O 3 planes suggest that the latter epitaxially induces Li 2 O 2 nucleation. This results in larger Li 2 O 2 primary crystallites and smaller secondary particles compared to that deposited on CNT, which enhances the reversibility of the Li 2 O 2 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 LiO 2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".