An aqueous rechargeable Fe//LiMn2O4 hybrid battery with superior electrochemical performance beyond mainstream Fe-based batteries
Bibliographic record
Abstract
Aqueous rechargeable batteries (ARBs) are generally safer than non-aqueous analogues, they are also less-expensive, and more friendly to the environment. However, the inherent disadvantage of the narrow electrochemical window of H 2 O seriously restricts the energy density and output voltage of ARBs, especially aqueous rechargeable Fe-based batteries. Herein, we introduce a new battery system: the anode contains C@Fe/Fe 2 O 3 composite, which is interfaced with an alkaline electrolyte; the cathode contains LiMn 2 O 4 in contact with a neutral electrolyte. A Li + -conducting membrane is carefully selected to decouple the electrode-electrolyte, which effectively widens the electrochemical window to above 2.65 V, thereby enables an aqueous rechargeable iron battery. Its average output voltage is 1.83 V and its energy density is 235.3 Wh/kg at 549 W/kg. In this work, we propose the energy storage mechanism with the aid of density functional theory (DFT). The calculated reduction potential of the anode agrees with the experimental value. Furthermore, this battery system demonstrates long cycle lifespan of approximately 2500 cycles at 2 A/g, corresponding to a capacity retention of 82.1%. These results are very far superior than those of mainstream aqueous rechargeable Fe-based batteries, which guarantee future investigation for storing electricity energy.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".