Enhancement of piezoelectric <scp>β‐polymorph</scp> formation and properties of graphene oxide and <scp>PZT</scp>‐incorporated in <scp>PVDF‐HFP</scp> matrix for energy harvesting applications
Bibliographic record
Abstract
Abstract This paper presents and compares films made using the solution casting method with a mixture of poly (vinylidene fluoride‐co‐hexafluoropropylene) (PVDF‐HFP), graphene oxide (GO), and lead zirconate titanate (PZT). The Hummers' method synthesized GO. Scanning electron microscopy (SEM), Fourier‐transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), and tensile testing were realized. The developed composite films were found to have a coherent distribution of PZT and GO in PVDF‐HFP. After that, a gradual improvement, such as an increase in the quantity of β phase, produces high piezoelectric performance. Also, the PVDF‐HFP polymer's thermal stability improved. When 0.1 wt% of PZT/GO was added, the melting temperature increased from 140 to 143°C, and the crystallization temperature from 109 to 113°C. PVDF‐HFP elastic modulus and tensile strength were also considerably reduced as PZT/GO increased. As a result, this has enabled us to develop composite films with important properties that can be used as piezoelectric materials for energy harvesting.
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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".