Incorporation of Heteroatomic Fe Activates Rapid Catalytic Behaviors of Co<sub>3</sub>O<sub>4</sub> Hollow Nanoplates Toward Advanced Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) battery is a highly attractive energy storage system due to its high capacity and great affordability. However, the parasitic shuttle effect and sluggish redox kinetics are perplexing the fulfillment of efficient battery electrochemistry. To tackle these challenges, herein, a hierarchical and hollow nanoplate assembled by Fe‐doped Co3O4 nanosheets (Fe–Co3O4 HHNPs) is meticulously designed as an advanced sulfur nanoreactor. The interlaced nanosheets establish a robust and porous network for fast charge transfer and efficient active site exposure. More importantly, the heteroatomic Fe incorporation tailors the electronic structure via local structure distortion and electron redistribution, contributing to massive active sites that lower the energy barrier for sulfur conversions. The resulting sulfur adsorption and catalyzation endow the Li–S cells with minimum capacity decay of 0.08% per cycle over 500 cycles and decent rate performance up to 5 C. Moreover, a high areal capacity of 9.0 mAh cm−2 after 55 cycles is also achievable under raised sulfur loading of 11 mg cm−2 and limited electrolyte (E/S = 3.9 µL mg−1). This work provides an elaborate and instructive paradigm for designing catalytic nanoreactors toward superior Li–S electrochemistry.
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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".