A biocompatible polypyrrole membrane for biomedical applications
Bibliographic record
Abstract
Polypyrrole (PPy) is the most widely investigated electrically conductive biomaterial. However, because of its intrinsic rigidity, PPy has only been used either in the form of a composite or a thin coating. This work presents a pure and soft PPy membrane that is synergically reinforced with the electrospun polyurethane (PU) and poly-l-lactic acid (PLLA) fibers. This particular reinforcement not only renders the originally rather fragile PPy membrane easy to manipulate, it also prevents the membrane from deformation in an aqueous environment. Peel and mechanical tests confirmed the strong adhesion of the fibers and the significantly increased tensile strength of the reinforced membrane. Surface electrical conductivity and long-term electrical stability were tested, showing that these properties were not affected by the reinforcement. Surface morphology and chemistry were analyzed with scanning electron spectroscopy (SEM), X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FTIR). Material thermal stability was investigated with thermogravimetric analysis (TGA). Finally, the adhesion and proliferation of human skin keratinocytes on the membrane were assessed by Hoechst staining and the methylthiazolyldiphenyl-tetrazolium bromide (MTT) assay. In conclusion, this membrane proves to be the first PPy-based soft conductive biomaterial that can be practically used. Its electrical conductivity and cytocompatibility promise a wide range of 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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".