Oxidation States Regulation of Cobalt Active Sites through Crystal Surface Engineering for Enhanced Polysulfide Conversion in Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract In this work, unique Co3O4/N‐doped reduced graphene oxide (Co3O4/N‐rGO) composites as favorable sulfur immobilizers and promoters for lithium–sulfur (Li–S) batteries are developed. The prepared Co3O4 nanopolyhedrons (Co3O4‐NP) and Co3O4 nanocubes mainly expose (112) and (001) surfaces, respectively, with different atomic configurations of Co2+/Co3+ sites. Experiments and theoretical calculations confirm that the octahedral coordination Co3+ (Co3+Oh) sites with different oxidation states from tetrahedral coordination Co2+ sites optimize the adsorption and catalytic conversion of lithium polysulfides. Specially, the Co3O4‐NP crystals loaded on N‐rGO expose (112) planes with ample Co3+Oh active sites, exhibiting stronger adsorbability and superior catalytic activity for polysulfides, thus inhibiting the shuttle effect. Therefore, the S@Co3O4‐NP/N‐rGO cathodes deliver excellent electrochemical properties, for example, stable cyclability at 1 C with a low capacity decay rate of 0.058% over 500 cycles, superb rate capability up to 3 C, and high areal capacity of 4.1 mAh cm−2. This catalyst's design incorporating crystal surface engineering and oxidation state regulation strategies also provides new approaches for addressing the complicated issues of Li–S batteries.
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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) | 0.000 | 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 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".