Paper-Supported Sodium Alginate Composite Separator Prepared by Polymer-Assisted Phase Separation for Lithium Ion Batteries
Bibliographic record
Abstract
Ecofriendly and renewable properties are highly desirable for separators of lithium batteries, apart from the notorious safety issues. As a natural polysaccharide material, sodium alginate (SA) has outstanding biodegradability and biocompatibility and has usually been used for the binder of electrodes due to its high ionic conductivity. Herein, SA porous separators were initially prepared by a facile polymer-assisted phase separation in which polyethylene glycol (PEG) and acetonitrile acted as a pore-forming agent and an extraction solvent, respectively. The influence of PEG content on the pore formation was systematically investigated, and the uniform and continuous pore structures were successfully realized at the PEG content of 200–500 wt %. Additionally, the cellulose-based paper support (KP) and poly(vinylidene fluoride- co -hexafluoropropylene) porous coating (PVH) were adopted for the decent mechanical integrity of SA porous membranes. The prepared SA composite separators showed excellent thermal dimensional stability, high porosity, and good electrolyte wettability. Moreover, the polar features of SA endowed the composite separators with high ionic conductivity (4.8 mS cm –1 ) and lithium ion transference number (0.62). The strong depression capacity of lithium dendrites and a comparable electrochemical performance were also observed for the SA-based separators compared with the pure KP and commercial polyolefin separators.
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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 | 0.001 |
| 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".