Ceramic Nanoparticle-Decorated Melt-Electrospun PVDF Nanofiber Membrane with Enhanced Performance as a Lithium-Ion Battery Separator
Bibliographic record
Abstract
Abstract Designing a composite separator that can withstand high temperature, deliver high capacity, and offer fast charge–discharge capability is imperative for developing a high-performance lithium-ion battery. Here, a series of ceramic nanoparticle-coated nanofiber membranes, including Al2O3/poly(vinylidene fluoride) (PVDF), SiO2/PVDF, and Al2O3/SiO2/PVDF, were prepared by melt-electrospinning and magnetron sputtering deposition. Among all of these composite separators, Al2O3/SiO2/PVDF showed several advantages including excellent thermal stability (no dimensional shrinkage at temperature up to 130 °C and an onset degradation temperature of 445 °C) and superb electrolyte compatibility (340% electrolyte uptake). In addition, the β phase of the fibrous PVDF membrane as well as the presence of polar ceramic nanoparticles on the fiber surface can synergistically improve the ion conductivity to 2.055 mS/cm at room temperature, which is more than 8 times higher than that of the commercial polyethylene (PE) separator. Performance of these ceramic nanoparticle-coated separators in a lithium-ion battery demonstrated an improved discharge capacity of 161.5 mAh/g and more than 84.3% capacity retention rate after 100 cycles. The ceramic nanoparticle-coated PVDF separators also maintained 58.4% capacity at a high current density of 8C, which is better than the 49.8% capacity for the commercial PE separator. Therefore, the ceramic nanoparticle-coated PVDF membrane proves to be a promising separator for a high-power and more secure lithium-ion battery.
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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 | 0.001 | 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".