Novel electrical conductive hybrid nanostructures based on <scp>PA</scp> 6/<scp>MWCNT<sub>COOH</sub></scp> electrospun nanofibers and anchored <scp>MWCNT<sub>COOH</sub></scp>
Bibliographic record
Abstract
Electrical conductive nanostructures made of nanofibers of poly (amide 6) (PA6) with carboxyl functionalized multiwall carbon nanotubes (MWCNT COOH ) and anchored MWCNT COOH were produced. The nanotubes were surface activated with carboxyl groups, dispersed in formic acid and added to a formic acid solution of PA6. The mixture was electrospun by applying a voltage of 30 kV; afterwards, the nanofiber's mats were immersed in an aqueous dispersion of MWCNT COOH containing a nonionic surfactant. The chemical structure, morphology, thermal stability, and electrical conductivity of the nanostructures were evaluated by UV spectroscopy, scanning (SEM), and transmission electron microscopy (TEM), thermal gravimetric analyses (TGA) and volumetric conductivity measurements. The efficiency of the functionalization was confirmed by the UV peaks in the range between 220 and 250 nm (corresponding to a carbonyl group conjugated with a carbon‐carbon double bond). SEM and TEM micrographs showed the pullout of the MWCNT COOH from the nanofibers and the formation of a stable, percolated, and anchored MWCNT COOH network on the nanofibers due to the anchoring of the MWCNT COOH from the surfactant solution on the MWCNT COOH of the nanofibers. The coated nanostructures had higher thermal stability and higher electrical conductivity than the noncoated ones, showing the efficiency of this simple procedure. POLYM. ENG. SCI., 55:1263–1272, 2015. © 2015 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".