Metal–Organic Frameworks Reinforce the Carbon Nanotube Sponge-Derived Robust Three-Dimensional Sulfur Host for Lithium–Sulfur Batteries
Bibliographic record
Abstract
Sulfur is a prospective material for next-generation batteries with high theoretical capacity, but its drawbacks hinder its commercialization. To overcome the low conductivity of natural sulfur and the shuttle effect of lithium polysulfide, the study proposes a novel sulfur film coated with three-dimensional nitrogen and cobalt-codoped polyhedral carbon wrapped on a multiwalled carbon nanotube sponge (3D-S@NCoCPC sponge) composite as a high-performance cathode material for rechargeable lithium–sulfur batteries. The interconnected conductive carbon network with abundant pores provides more room for the homogeneous distribution of sulfur within the composite and creates a favorable pathway for electrolyte permeability and lithium-ion diffusion. Moreover, the strong interaction between cobalt and lithium polysulfides leads to efficient suppression of the shuttle effect. In addition, the homogeneous distribution of sulfur and cobalt within the composite enhances electronic transfer for the conversion reaction of sulfur. As expected, the cathode with a high sulfur content of 77.5 wt % in the composite achieved a high initial discharge capacity of 1192 mA h g –1 and high Coulombic efficiency of 99.98% after 100 cycles at 100 mA g –1 current density. Stable performance was achieved with 92.9% capacity retention after 200 cycles at 1000 mA/g current density.
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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".