Synergistic Interface Engineering and Band Alignment Enable High-Temperature Capacitive Performance in PAEK-Based Polymer Nanocomposites
Bibliographic record
Abstract
Polymer dielectric capacitors are crucial devices of high-power electrical systems for capacitive energy storage. The large conduction loss of polymer dielectrics at elevated temperatures and electric fields is the main challenge. Herein, dielectric nanocomposites of BNNS/poly(aryl ether ketone) (PAEK) regulated by interfacial engineering and band alignment are presented, significantly restraining the conduction loss and greatly enhancing the energy storage density at high temperatures and high electric fields. Dual-functionalized BNNS with −NH 2 and −F groups (F–BNNS–NH 2 ) were prepared and incorporated into carboxylate-functionalized PAEK (PAEK–COOH) to form robust interfacial bonding via an amino-carboxyl reaction, enabling excellent thermal stability and mechanical properties of the composites. Meanwhile, the electron-withdrawing nature of the −F group regulated the BNNS band structure to achieve widened E g, which is responsible for the generation of electrons and holes trappings. At optimal conditions, a record-high breakdown strength of 600 MV/m with an energy density of 5.58 J/cm 3 and an energy density of 5.01 J/cm 3 at an efficiency of 90% is realized at 150 °C, which surpasses most reported nanocomposite dielectrics. This work establishes a paradigm for harmonizing interfacial reinforcement with electronic structure regulation in extreme-condition energy storage dielectrics.
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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".