Co−Ni Binary‐Metal Oxide Coated with Porous Carbon Derived from Metal‐Organic Framework as Host of Nano‐Sulfur for Lithium‐Sulfur Batteries
Bibliographic record
Abstract
Abstract Lithium‐sulfur battery is considered as a promising energy storage system because of its high energy density. The specific capacity and cycling stability of sulfur cathode, however, are impeded by intrinsic poor electrical conductivity of sulfur and dissolution of polysulfides intermediates. Herein, we demonstrate a novel strategy to overcome the two obstacles by designing a bimetallic‐organic‐framework‐derived nano‐sulfur host consisted of porous graphitic carbon and bimetallic cobalt‐nickel oxides (C/NiCo2O4), in which porous carbon and NiCo2O4 not only entrapping the polysulfides effectively through physical and chemical entrapment capability, but also serving as a highly conductive matrix for sulfur. With a sulfur content of 68.9 % in the composite, the composite cathode delivered a specific capacity of 977 mAh g−1 and maintained 673 mAh g−1 at 0.5 C over 500 cycles. Besides, the binding mechanism between NiCo2O4 and polysulfides has been explored by ex situ XRD and density functional theory(DFT)simulation. This work may provide a feasible strategy to improve the performance of lithium‐sulfur battery.
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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".