Characterization, degradation, and mechanical strength of poly(<scp>D,L</scp>‐lactide‐<i>co</i>‐ϵ‐caprolactone)‐poly(ethylene glycol)‐poly(<scp>D,L</scp>‐lactide‐<i>co</i>‐ϵ‐caprolactone)
Bibliographic record
Abstract
A series of three biocompatible P(CL-co-LA)-PEG-P(CL-co-LA) copolymers were synthesized using ring-opening polymerization and characterized by 1H-NMR, gel permeation chromatography, DSC, dynamic-mechanical analysis, and X-ray diffraction. The number of monomer units was kept constant, while the D,L-LA fraction was varied so as to constitute 0, 30, or 70% of the end segments. The molecular weights were sufficiently high to eventually permit 3D scaffold preparation. A degradation study was carried out over 26 weeks, and the effect of monomer composition on the rate of degradation as well as on changes in mechanical strength was investigated. Pure polycaprolactone (PCL)-poly(ethylene glycol) (PEG)-PCL copolymer, P(100/0), was a crystalline material displaying no measurable mass loss, a 30% reduction in mean molecular weight (Mn), and only very slight changes in tensile strength. The random incorporation of 30 and 70% D,L-LA into the end sections of the polymer chain, produced more and more amorphous materials, exhibiting increasingly high rates of degradation, mass loss, and loss of tensile strength. Compared with random P(CL-co-LA), the presence of the PEG block was found both to improve hydrophilicity and thus the rate of degradation and to infer a stabilizing quality, thereby pacing the decrease in tensile strength during degradation. The tested copolymers range from materials exhibiting low mechanical strength and high rate of degradation to slow-degrading materials with high mechanical strength suitable, e.g., for three-dimensional scaffolding.
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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".