Bibliographic record
Abstract
Novel porous-conductive chitosan scaffolds were fabricated by incorporating conductive polypyrrole (PPy) particles into a chitosan matrix and employing a phase separation technique to build pores inside the scaffolds. Conductive polypyrrole particles were prepared with a microemulsion method using FeCl3 as a dopant. The preparation conditions were optimized to obtain scaffolds with controlled pore size and porosity. The conductivity of the scaffolds was investigated using a standard four-point probe technique. It was found that several kinds of scaffolds showed a conductivity close to 10(-3) S.cm(-1) with a low polypyrrole loading of around 2 wt.-%. The main mechanical properties, such as tensile strength, breaking elongation and Young's modulus of the scaffolds, were examined both in the dry and in the hydrated states. The results indicated that a few different kinds of scaffolds exhibited the desired mechanical strength for some tissue engineering applications. The miscibility of polypyrrole and chitosan was also evaluated using a dynamic mechanical method. The presence of significant phase separation was detected in non-porous PPy/chitosan scaffolds but enhanced miscibility in porous PPy/chitosan scaffolds was observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".