Modular Biodegradable Biomaterials from Surfactant and Polyelectrolyte Mixtures
Bibliographic record
Abstract
Polymeric assemblies are used in many biomaterials applications, ranging from drug-bearing nanoparticles to macroscopic scaffolds. Control over their biodegradation rates is usually achieved through synthetic modification of their molecular structure. As a simpler alternative, we exploit the associative phase separation in mixtures of bioderived surfactants and polyelectrolytes. The gel fiber scaffolds are formed via phase inversion, using a homologous series of fatty acid salts-sodium caprate (NaC10), laurate (NaC12), and myristate (NaC14), and a water-soluble chitosan derivative, N-[(2-hydroxy-3-trimethylammonium)propyl] chitosan chloride (HTCC). Their dissolution times are modulated through the selection of the fatty acid molecule and vary in a predictable manner from minutes (for NaC10-HTCC), to hours (for NaC12-HTCC), to days (for NaC14-HTCC). These variations are linked to differences in surfactant-polyelectrolyte binding strength and scale with the equilibrium binding constants of their mixtures. These fibers were found to be both cytocompatible and cell-adhesive using neural stem/progenitor cells, suggesting their potential for utility in biomedical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".