Degree of Crystallinity and Phase Fraction of Polyvinylidene Fluoride Nanocomposites Containing Ionic Liquid and Graphene/Carbon Nanotube
Bibliographic record
Abstract
Bucky gel actuator (BGA) is a type of electro‐active polymer that bends when stimulated by an electric field. Its operation is affected by a matrix network, which has two opposite effects on ion migration and material strength properties of the actuator. Therefore, designing a BGA with more deflection or more strength demands the study of matrix structural properties. In this paper, polyvinylidene fluoride (PVDF) was used as the polymer matrix in BGA composites, and the degree of crystallinity and the fraction of phase were calculated using X‐ray diffraction and Fourier Transform Infrared spectroscopy respectively to investigate the matrix structural properties. Furthermore, Raman spectroscopy analysis was utilized for phase characterization. Several composite films with various components including electrode and electrolyte layers of BGA were prepared by the drop‐casting method in two different conditions to study the effects of PVDF concentration in dimethylacetamide solvent, drying temperature, and additive materials on the matrix structural properties for the first time. It was observed that low concentrations of PVDF in dimethylacetamide solvent coupled with a high drying temperature in a carbon nanotube‐based BGA in contrast with using a graphene‐based BGA, had the lowest degree of crystallinity and phase fraction. POLYM. COMPOS., 39:E1208–E1215, 2018. © 2018 Society of Plastics Engineers
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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".