Novel biodegradable polyurethanes reinforced with green nanofibers for applications in tissue engineering. Synthesis and characterization
Bibliographic record
Abstract
Abstract A new class of polyurethane (PU) biocomposites reinforced with green biocellulose nanofibers (BC) were designed and synthesized. These newly introduced non‐cytotoxic and biodegradable composites were synthesized with different ratios of hard to soft segments of the linear, aliphatic hexamethylene diisocyanate (HDI) and polycaprolactone diol (PCL), respectively. The porosity was introduced in the polyurethane matrix using a combination of salt leaching and thermally induced phase separation (TIPS). BC contents were in the range of 0–15 % of the final PU by weight. FTIR spectra showed complete conversion of HDI through the disappearance of the isocyanate and imine characteristic bands (at 2260 cm−1and 1635 cm−1, respectively) and appearance of carbonyl group band in PU (at 1730 cm−1). The hard to soft segment (i.e., HDI:PCL) ratios in the final PU polymer were quantified from1H Nuclear Magnetic Resonance (NMR) spectra by comparing the proton peaks arising for CH2CO at 2.25 ppm or OCH2at 3.9 ppm to CH2N at 2.9 ppm. Results showed that the ratios in the final product were consistent with the amounts added initially during the synthesis. Scanning electron microscope (SEM) images showed that porosity (57–75 %) were formed (pore size in the range of 125–355 µm), with an increase in pore content with the decrease in HDI:PCL content.
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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) | 0.000 | 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".