Electrospun porous conductive polymer membranes
Bibliographic record
Abstract
In this work, two methodologies were used in fabricating conductive electrospun polymer fibers with nano features. We first investigated the addition of multiwall carbon nanotubes (MWCNT) as conductive fillers at concentrations ranging from 1 to 10% into a polystyrene (PS) matrix. Electrospinning conditions were tailored to produce fibers with minimal beads. Next, we investigated the effects of coating electrospun fibers with nano structured conductive polymer. Oxidant (FeCl3) fibers were electrospun in PS and then exposed to a pyrrole (Py) monomer in a vacuum chamber. As a result, polypyrrole (PPy) was coated on the fibers creating conductive pathways. In both methods, the electrospun conductive fibers were characterized in terms of their morphologies, thermal stability and electrical conductivity. Strong correlations were found among PPy coating nanostructures, oxidant concentration and polymerization time. Electrospun fibrous membranes with conductive polymer coating exhibit much higher electrical conductivities compare to fibers with conductive fillers. Highest conductivity achieved was 9.5E-4 S/cm with 40% FeCl3/PS fibers polymerized with Py for 140 min.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".