Syntheses, characterization, and functionalization of poly(ester amide)s with pendant amine functional groups
Bibliographic record
Abstract
Abstract Poly(ester amide)s (PEAs) comprising α‐amino acids, diols, and diacids are promising materials for biomedical applications such as tissue engineering and drug delivery because of their tunability and potential for either hydrolytic or enzymatic degradation. Although a number of PEAs of different compositions have been reported, there is a significant need for the incorporation of amino acids with functional side chains. This will allow for the conjugation of drugs or cell signaling molecules in tissue engineering scaffolds, thus expanding the potential applications of these materials. The objective of this work was the incorporation of l‐lysine into PEAs to provide functionalizable pendant amine groups. Thus, varying percentages of lysine were incorporated into PEAs comprised of l‐phenylalanine, 1,4‐butanediol, and succinic acid by tuning the ratio of ε‐protected‐l‐lysine and l‐phenylalanine derived monomers. The polymers were characterized by nuclear magnetic resonance spectroscopy, infrared spectroscopy, size exclusion chromatography, and differential scanning calorimetry. The lysine ε‐protecting group was removed, then the reactivity of the pendant amines was demonstrated by reaction with amino acid and tri(ethylene glycol) derivatives. The degradation of thin films of polymers were studied using scanning electron microscopy and the incorporation of lysine was found to significantly accelerate both the hydrolytic and enzymatic degradation. © 2008 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 46: 6376–6392, 2008
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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".