Improvement of the piezoelectric, thermal, structural properties of PMMA/PVdF-HFP blend composite films using PZT
Bibliographic record
Abstract
Energy harvesting is the most efficient way to meet energy demand while also supplying renewable energy sources from the environment. This technology consists of recovering of electrical energy from lost energy sources, which are available everywhere, including heat, fluids, vibrations, etc. In particular, energy harvesting via piezoelectric materials, which can convert the energy of vibrations and mechanical deformations into electrical energy, has been the subject in the last decade. This energy is used in a variety of applications, including energy collectors, sensors, and actuators, among others. This study concerns the improvement and enhancement of the properties of Poly (methyl methacrylate) (PMMA). Synthesized PMMA was blended with Poly(vinylidene fluoride-co-hexafluoropropylene) (PVdF-HFP), and Lead zirconate titanate (PZT) nanoparticles at various composition ratios using solvent casting technique in Tetrahydrofuran (THF). Improvement of properties has confirmed by POM, SEM, FTIR, XRD, TGA, DSC, and tensile testing.
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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".