Modification of glass fiber separators with a ternary V2O5/Cys/FeNi-LDH composite to enhance the performance of lithium-sulfur batteries
Bibliographic record
Abstract
• A novel V 2 O 5 /Cys/FeNi-LDH composite was developed for separator modification. • The composite mitigates the polysulfide shuttle effect in Li-S batteries. • Batteries with the modified separator show a low decay rate of 0.039% per cycle. • Enhanced performance is due to synergistic effects of V 2 O 5 , Cys, and FeNi-LDH. Lithium-sulfur (Li-S) batteries are promising candidates for next-generation energy storage systems due to their high theoretical energy density and low cost. However, their practical application is hindered by the polysulfide shuttle effect, which leads to poor cycling stability and low Coulombic efficiency. To address this challenge, we developed a novel separator modifier, V 2 O 5 /Cys/FeNi-LDH (VCFN), by uniformly depositing V 2 O 5 nanoparticles onto cysteine-modified FeNi-LDH nanodisks. Electrochemical performance tests revealed that the VCFN-modified separator significantly enhanced the cycling stability and capacity retention of Li-S batteries, delivering an initial discharge capacity of 1035.2 mAh g −1 at 0.5C and maintaining 920.1 mAh g −1 after 300 cycles with a low capacity decay rate of 0.039% per cycle. High sulfur loading cells (up to 4.5 mg cm −2 ) also exhibited stable performance. The enhanced performance results from the synergistic effects of V 2 O 5 ’s catalytic properties, the high surface area of FeNi-LDH, cysteine’s function as a linker, the polysulfide adsorption ability of all three components, and the unique porous structure of VCFN, which facilitates efficient Li-ion transport. This work demonstrates that the VCFN-modified separator effectively mitigates the polysulfide shuttle effect, offering a promising approach for high-performance 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".