Enhancing lithium-sulfur battery performance through the synergistic effects of Congo Red and SnO2 nanoparticles in separator modification layers
Bibliographic record
Abstract
• A Congo Red/SnO 2 nanocomposite is utilized to modify the battery separator. • The modified separator markedly improves lithium–sulfur battery performance. • The composite effectively inhibits polysulfide shuttling and enhances redox kinetics. • An initial capacity of 1377 mAh g ⁻¹ is achieved with 91 % retention after 300 cycles. The performance of lithium-sulfur batteries (LSBs) is hindered by polysulfide shuttling and sluggish redox kinetics, resulting in rapid capacity fading and poor cycle life. This study presents a new strategy for improving LSB performance by modifying the separator with a Congo Red (CR)-tin dioxide (SnO 2 ) nanocomposite (CR-SnO 2 ), leveraging its synergistic interactions to enhance polysulfide immobilization and lithium-ion transport. Scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmission electron microscopy (TEM) confirm the uniform dispersion of SnO 2 nanoparticles within the CR matrix, creating a stable and functional separator coating. LSBs employing glass fiber (GF) separators modified with CR-SnO 2 /SP/PVP, a composite of CR-SnO 2 , Super P (SP), and polyvinylpyrrolidone (PVP), achieve an initial specific capacity of 1377 mAh g⁻¹ at 0.1C and demonstrate remarkable cycle stability, retaining 91 % of capacity after 300 cycles at 0.5C. Additionally, the modified separator significantly enhances rate performance by reducing charge-transfer resistance and improving redox kinetics. Compared to cells with SnO 2 /SP/PVP or CR/SP/PVP-modified separators, which show limited performance improvements, the CR-SnO 2 -modified separator enables superior polysulfide adsorption and enhanced ionic conductivity, effectively mitigating the shuttle effect. This work advances surface and interface science by demonstrating how the functionalization of separator materials with a synergistic organic-inorganic composite can simultaneously regulate polysulfide conversion and facilitate efficient ion transport. The findings provide new insights into interfacial engineering strategies for improving LSB performance and offer a promising approach for developing high-energy, long-cycle LSBs.
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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) | 0.001 | 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".