Bicontinuous-distributed nanofibers and nanosheets facilitate anisotropic nanocomposite dielectrics to high energy density capacitors
Bibliographic record
Abstract
• Bicontinuous BST nf and BNNS arrays in anisotropic nanocomposites. • Experiments/simulations reveal bicontinuous structures enhance ɛ r and E b . • Optimized PMB-i composite achieves U e ∼18.2 J/cm³, ɛ r ∼33.6, and E b ∼550 MV/m. • Novel space structure design applicable to high-temperature energy storage. Polymer nanocomposite dielectrics with high insulation and high energy storage density ( U e ) are pivotal for advancing lightweight and integrated power devices. However, the inherent trade-off between dielectric constant ( ɛ r ) and breakdown strength ( E b ) has limited the energy storage performance of such materials. Here, we present a novel design of bicontinuous structural anisotropic nanocomposites by integrating high- ɛ r Ba 0.6 S r0.4 TiO 3 nanofiber (BST nf) arrays and oriented wide-bandgap BN nanosheets (BNNS). This architecture simultaneously enhances polarization continuity and establishes a high-specific-area carrier blocking layer, enabling the synergistic improvement of ɛ r and E b . Our optimized PVDF/ m BST nf/BNNS nanocomposite achieves an ultrahigh E b of 550 MV/m and an U e as high as 18.2 J/cm 3 , representing enhancements of 46% and 323%, respectively, over pristine PVDF. Moreover, the bicontinuous structure improves energy storage performance at elevated temperatures, providing a robust framework for designing high-performance dielectrics, which provides scientific support for the application of polymer-based energy storage devices.
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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".