Star poly(lactide-co-glycolide) and poly(ε-caprolactone) polyurethanes with shape memory properties for biomedical applications
Bibliographic record
Abstract
• Mechanical properties of poly(ester-urethane) are modulated by addition of star PLGA. • Low amounts of 4- or 6-arms PLGA improve the shape-memory properties. • Flexural strength is decreased by addition of low amounts of star PLGA for better surgical handling. • Linear degradation profiles are obtained when mixing star and linear PLGA in poly(ester-urethane) Shape memory polyurethane (SMPU) have the capacity to alter and regain their form in reaction to a stimulus ( eg. temperature, pH) and have been investigated in biomedical applications. In this work, the shape memory properties of poly(ester-urethane)s (PEU) are studied as a function of the functionality of the poly(lactide-co-glycolide) (PLGA) and poly(e-caprolactone) (PCL) pre-polymers. Two distinct series of PEU are synthesized by reaction between linear and star-shaped PLGA polyols initiated by pentaerythritol (4-arms PLGA) or dipentaerythritol (6-arms PLGA) with PCL di-isocyanate prepolymers. The different PEUs exhibit thermally actuated shape memory properties and tunable mechanical properties with Young’s moduli reaching up to 96 MPa and elongation at break reaching 930 %. The integration of low amounts of PLGA star within the PEU structure increases the material’s shape memory properties, enhancing both fixity (from 45 % to 96 %) and recovery ratios (from 88 % to 92 %). Further exploration into potential applications led to the formulation of porous foams via the solvent casting/particles leaching (SC/PL) process. These foams exhibited high porosity ranging from 74 % to 83 % with pore sizes spanning from 100 to 300 µm. Their mechanical properties are close to the human meniscus with Young’s modulus ranging from 0.13 MPa to 0.53 MPa. Moreover, integration of PLGA star within the PEU scaffold decreases the flexural strength that is an important parameter for potential mini-invasive surgery.
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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) | 0.002 | 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".