Vertical Graphene Growth on LDH Nanosheets and Carbon Cloth Nanofibers with NiCo Nanoparticles as a Freestanding Host for High-Performance Lithium–Sulfur Batteries
Bibliographic record
Abstract
Lithium–sulfur batteries have been recognized as one of the excellent candidates for next-generation energy storage batteries because of their high energy density and low cost and low pollution. However, lithium–sulfur batteries have been challenged by low conductivity, low sulfur utilization, poor cycle life, and the shuttle effect of polysulfides. To address these problems, we report here an independent mixed sulfur host. First, NiCoAl-layered double hydroxide (LDH) nanosheets were uniformly grown on carbon cloth (CC) by a hydrothermal method. Then, vertical graphene (VG) was uniformly vertically grown on the composite structures to form VG@LDH/CC by a plasma enhanced chemical vapor deposition (PECVD) method. Graphene and LDH nanosheets forming a three-dimensional mesh structure can effectively physically block lithium polysulfides, store singlet sulfur, and improve the conductivity of the cathode. In addition, during the growth of graphene, the Ni and Co ions in the LDH nanosheets are reduced to NiCo nanoparticles, which can enhance the chemical adsorption of polysulfides, thus effectively mitigating the “shuttle effect” and improving the electrical conductivity of the material. The lithium sulfur batteries with derived sulfur anodes (VG@LDH/CC-S) exhibited excellent electrochemical properties, including excellent rate performance (780.8 mAh g –1 at 3C) and impressive cycling stability (capacity decay of about 0.0755% per cycle after 750 cycles at 0.5C).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".