Novel Modification of Collagen: Realizing Desired Water Solubility and Thermostability in a Conflict-Free Way
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Because of poor water solubility and low thermostability, the application of collagen is limited seriously in fields such as injectable biomaterials and cosmetics. In order to overcome the two drawbacks simultaneously, a novel bifunctional modifier based on the esterification of polyacrylic acid (PAA) with N -hydroxysuccinimide (NHS) was prepared. The esterification degree of PAA-NHS esters was increased upon increasing the NHS dose, which was confirmed by Fourier-transform infrared (FTIR) and nuclear magnetic resonance spectrascopy. FTIR results indicated that the triple helix of the modified collagens remained integrated, whereas the molecular weight became larger, as reflected by the sodium dodecyl sulfate–polyacrylamide gel electrophoresis pattern. The modified collagens displayed excellent water solubility under neutral condition, owing to lower isoelectric point (3.1–4.3) than that of native collagen (7.1). Meanwhile, denaturation temperatures of the modified collagens were increased by 4.8–5.9 °C after modification. The modified collagen displayed hierarchical microstructures, as reflected by field-emission scanning electron microscopy, while atomic force microscopy further revealed a “fishing net-like” network in the nanoscale, reflecting a unique aggregation behavior of collagen macromolecules after modification. As a whole, the PAA-NHS ester as a bifunctional modifier endowed collagen with desired water solubility and thermostability in a conflict-free manner, which was beneficial to the process and application of the water-soluble collagen.
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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".