Polymer Matrix Nanocomposites with 1D Ceramic Nanofillers for Energy Storage Capacitor Applications
Bibliographic record
Abstract
Recent developments in various technologies, such as hybrid electric vehicles and pulsed power systems, have challenged researchers to discover affordable, compact, and super-functioning electric energy storage devices. Among the existing energy storage devices, polymer nanocomposite film capacitors are a preferred choice due to their high power density, fast charge and discharge speed, high operation voltage, and long service lifetime. In the past several years, they have been extensively researched worldwide, with 0D, 1D, and 2D nanofillers being incorporated into various polymer matrixes. However, 1D nanofillers appeared to be the most effective in producing large dipole moments, which leads to a considerably enhanced dielectric permittivity and energy density of the nanocomposite. As such, this Review focuses on recent advances in polymer matrix nanocomposites using various types of 1D nanofillers, i.e., linear, ferroelectric, paraelectric, and relaxor-ferroelectric for energy storage applications. Correspondingly, the latest developments in the nanocomposite dielectrics with highly oriented, surface-coated, and surface-decorated 1D nanofillers are presented. Special attention has been paid to identifying the underlying mechanisms of maximizing dielectric displacement, increasing dielectric breakdown strength, and enhancing the energy density. This Review also presents some suggestions for future research in low-loss, high 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 | 0.001 |
| 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".