Polyurethane foams electrophoretically coated with carbon nanotubes for tissue engineering scaffolds
Bibliographic record
Abstract
Carbon nanotubes (CNTs) were deposited on the surfaces of polyurethane (PUR) foams by electrophoretic deposition (EPD). The parameters of EPD were optimized in order to obtain homogeneous CNT coatings on PUR foams and adequate infiltration of the three-dimensional (3D) porous network. The microstructure of the composites was investigated by high-resolution scanning electron microscopy (HRSEM), revealing that optimal quality of the coatings was achieved by an EPD voltage of 20 V. The thermal properties of the CNT-coated specimens, determined by thermogravimetric analysis (TGA), were correlated to the foam microstructure. In vitro tests in concentrated simulated body fluid (1.5 SBF) were performed to study the influence of the presence of CNTs on the bioactivity of PUR-based scaffolds, assessed by the formation of calcium phosphate (CaP) compounds, e.g. hydroxyapatite (HA), on the foam surfaces. It was observed that CNTs accelerate the precipitation of CaP, which is thought to be due to the presence of more nucleation centres for crystal nucleation and growth, as compared with uncoated foams. Polyurethane foams with CNT coating have the potential to be used as bioactive scaffolds in bone tissue engineering due to their high interconnected porosity, bioactivity and nanostructured surface topography.
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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".