Heparin dopant increases the electrical stability, cell adhesion, and growth of conducting polypyrrole/poly(<scp>L</scp>,<scp>L</scp>‐lactide) composites
Bibliographic record
Abstract
Polypyrrole (PPy) is a promising conductive polymer for tissue engineering and bioelectrical applications. However, its electrical conductivity deteriorates easily in aqueous conditions. Cell adhesion to PPy is also relatively poor. The goal of this study was to simultaneously improve the electrical stability of and cell adhesion to PPy by using heparin (HE) as dopant, for HE is both a polyanion and an important glycosaminoglycan in cell membranes and extracellular matrix. PPy particles doped with HE were synthesized through emulsion polymerization using Fenton's reagent as an oxidant. X-ray photoelectron spectroscopy (XPS), infrared and scanning electron microscopy (SEM) were used to investigate the PPy particles. Conductive biodegradable membranes of 10(2) to 10(3) Omega/square were prepared from 5% (w) PPy with various amounts of HE and 95% (w) poly(L,L-lactide) (PPy/PLLA). Azure A staining was employed to quantify the HE exposed on the surface of the PPy particles and PPy/PLLA membranes. The distribution of HE on membranes was demonstrated by DAPI staining. Results showed that HE was incorporated into the PPy particles as counterions and presented on particle surface. A unique "filament"-like morphology of the PPy preparation was observed at high-HE content. The electrical stability of the PPy/PLLA membranes was tested in saline at 37 degrees C for 500 h. Human skin fibroblasts were used to test the cell adhesion capacity. The conductive membranes containing HE-doped PPy particles recorded significantly increased electrical stability, cell adhesion, and growth. The electrically more stable and cell adhesive conductive biodegradable membrane may act as a platform for various biomedical applications.
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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".