Evaluation and Optimization of Dielectric Properties of PVDF/BaTiO<sub>3</sub> Nanocomposites Film for Energy Storage and Sensors
Bibliographic record
Abstract
Flexible nanodielectric are largely used in sensors and power sources for new generation of electronic devices. The most conventional methods used to design and manufacture these nanodielectric materials with desired properties are time-consuming and unable to determine interactions between the input parameters. In this study, a response surface methodology (RSM) is proposed to design polyvinylidene fluoride (PVDF)/barium titanate (BT) nanocomposites film prepared by ball milling process with optimized dielectric breakdown strength. Interaction effects of three individual control variables on nanocomposites dielectric strength were studied using RSM. Numerical optimization was employed to obtain the optimum factors for maximum dielectric breakdown strength. It is indicated that the optimum value of dielectric breakdown strength was 219.01 kV mm−1, when input control factors were BT size of 6 nm, BT volume fraction of 10 vol% and milling time of 43.74 min.
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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) | 0.000 | 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".