Ceramic Nanoparticle-Decorated Melt-Electrospun PVDF Nanofiber Membrane with Enhanced Performance as a Lithium-Ion Battery Separator
Why this work is in the frame
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Bibliographic record
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.003 |
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 it