Enzyme-induced biodegradation of polycarbonate-polyurethanes: Dependence on hard-segment chemistry
Bibliographic record
Abstract
Polycarbonate urethanes (PCNUs) have been used as a replacement for traditional biomedical polyether-urethanes due to their reported resistance to oxidative biodegradation. However, relatively little is known about their hydrolytic stability in the presence of inflammatory derived enzymes. This has in part motivated the current study relating to the effect of hard segment chemistry and the microdomain structures generated by such chemistry, on the cholesterol esterase (CE) catalyzed hydrolysis of PCNUs. The bulk structures of the studied materials were characterized using gel permeation chromatography (GPC), differential scanning calorimetry (DSC), small-angle X-ray scattering (SAXS), Fourier transform infrared spectroscopy (FTIR) for their bulk structures, and attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) for their subsurface structures. 14C-labeled PCNUs were incubated with CE (400 units/mL), for a period of 10 weeks (pH 7.0 at 37 degrees C), and radiolabel release was used to monitor the degradation. The results showed that all of the polymers synthesized in this study were susceptible to CE-catalyzed hydrolytic degradation, and that the extent of degradation was highly dependent on the nature of hard segment interactions within the polymer and at the surface. More specifically, the degree of phase separation and soft segment crystallinity were found to be less important in comparison to the hydrogen bonding among the carbonate and urethane linkages. The rank of the different chemical groups' susceptibility to hydrolysis was as follows: nonhydrogen bonded carbonate > nonhydrogen bonded urethane > hydrogen bonded carbonate > hydrogen bonded urethane. The findings suggest that the degree of hydrogen bonding, when processed into a polyurethane material could be an important parameter to consider in the design of new biostable polyurethane products.
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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".