Charge and Peptide Concentration as Determinants of the Hydrogel Internal Aqueous Environment
Bibliographic record
Abstract
Self-assembling peptides are a promising class of biomaterials with desirable biocompatibility and versatility. In particular, the oligopeptide (RADA)₄, consisting of arginine (R), alanine (A), and aspartic acid (D), self-assembles into nanofibers that develop into a three-dimensional hydrogel of up to 99.5% (w/v) water; yet, the organization of water within the hydrogel matrix is poorly understood. Importantly, peptide concentration and polarity are hypothesized to control the internal water structure. Using variable temperature deuterium solid-state nuclear magnetic resonance (²H NMR) spectroscopy, we measured the amount of bound water in (RADA)₄-based hydrogels, quantified as the non-frozen water content. To investigate how peptide polarity affects water structure, five lysine (K) moieties were appended to (RADA)₄ to generate (RADA)₄K₅. Hydrogels at 1 and 5% total peptide concentration were prepared from a 75:25 (w/w) blend of (RADA)₄:(RADA)₄K₅ and similarly analyzed by ²H NMR. Interestingly, at 5% peptide concentration, there was lower mobile water content in the lysinated versus the pristine (RADA)₄ hydrogel. Regardless of the presence of lysine, the 5% peptide concentration had higher non-frozen water content at temperatures as low as 217 ± 1.0 K, suggesting that bound water increases with peptide concentration. The bound water, though non-frozen, may be strongly bound to the charged lysine moiety to appear as immobilized water. Further understanding of the factors controlling water structure within hydrogels is important for tuning the transport properties of bioactive solutes in the hydrogel matrix when designing for biomedical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.006 | 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; both teacher heads agree on what is shown here.
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".