Fe-based hybrid electrocatalysts for nonaqueous lithium-oxygen batteries
Bibliographic record
Abstract
Abstract Lithium–oxygen batteries promise high energy densities, but are confronted with challenges, such as high overpotentials and sudden death during discharge–charge cycling, because the oxygen electrode is covered with the insulating discharge product, Li 2 O 2 . Here, we synthesized low–cost Fe–based nanocomposites via an electrical wire pulse process, as a hybrid electrocatalyst for the oxygen electrode of Li–O 2 batteries. Fe 3 O 4 -Fe nanohybrids–containing electrodes exhibited a high discharge capacity (13,890 mA h g c −1 at a current density of 500 mA g c −1 ), long cycle stability (100 cycles at a current rate of 500 mA g c −1 and fixed capacity regime of 1,000 mA h g c −1 ), and low overpotential (1.39 V at 40 cycles). This superior performance resulted from the good electrical conductivity of the Fe metal nanoparticles during discharge–charge cycling, which could enhance the oxygen reduction reaction and oxygen evolution reaction activities. We have demonstrated the increased electrical conductivity of the Fe 3 O 4 -Fe nanohybrids using electrochemical impedance spectroscopy.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".