A new approach for conductive network formation in electrospun poly(vinylidene fluoride) nanofibers
Bibliographic record
Abstract
Abstract Conductive nanofibers of poly(vinylidene fluoride) (PVDF) filled with polyaniline (PANi)‐coated multi‐wall carbon nanotubes (MWCNTs) were fabricated using the electrospinning technique. PANi is an intrinsically conductive polymer. The addition of PANi‐coated MWCNTs to PVDF created short conductive strands on the surface of the nanofibers, facilitating the formation of a conductive network in the transverse direction of the nanofibers. Piezoelectricity along with electric conductivity makes these PVDF nanofibers promising for applications such as sensors and actuators. Electrospun PVDF nanofiber mats had higher piezoelectricity than melt‐processed samples produced using traditional polymer processing techniques, such as compression molding. Spectroscopic imaging techniques were employed to study the effects of the filler and processing conditions on the nanofiber structure. X‐ray diffraction, Fourier transform infrared spectroscopy and differential scanning calorimetry results indicated a large increase in the β‐phase crystals of the PVDF nanofibers. This higher content of β‐phase crystals enhanced the piezoelectricity of the nanofibers. © 2015 Society of Chemical Industry
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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".