Nanolignin Functional Separators for Flexible Lithium–Sulfur Batteries With Enhanced Performance
Bibliographic record
Abstract
In this study, a nanolignin functionalized separator has been designed to maximize the thermo‐mechanical response of commercial separators while blocking polysulfide shuttling in lithium–sulfur batteries. A uniform, thin, and mechanically robust biobased nanocomposite functional coating, exhibiting reduced porosity compared to the commercial polypropylene separators, was produced. The nano‐composite coating, based on poly(ethylene glycol) diacrylate embedding kraft lignin nanoparticles through a waterborne, dual‐curing process, afforded excellent resistance to thermo‐oxidative and thermolytic degradation and yielded a wide temperature operating window for the separator. Furthermore, flame resistance was also markedly improved versus benchmark non‐coated polypropylene, with the modified separator exhibiting slower combustion kinetics and char formation under direct flame exposure. Such a functionalized biobased system was employed as a functional/structural component in flexible lithium–sulfur batteries pouch cells, which were shown to achieve an initial discharge capacity as high as 1128.7 mAh g −1 at 0.1 C, maintaining 541 mAh g −1 after 250 cycles. This work presents a scalable and environmentally friendly approach to separator design, offering important advances toward safer, high‐performance lithium–sulfur batteries devices for applications in portable electronics and electric vehicles.
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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".