Amphiphilic thermoset elastomers from metal-free, click crosslinking of PEG-grafted silicone surfactants
Bibliographic record
Abstract
The hydrophobicity of silicone elastomers can compromise their utility in some biomaterials applications. Few effective processes exist to introduce hydrophilic groups onto a polysiloxane backbone and subsequently crosslink the material into elastomers. This problem can be overcome through the utilization of metal-free click reactions between azidoalkylsilicones and alkynyl-modified silicones and/or PEGs to both functionalize and crosslink silicone elastomers. Alkynyl-functional PEG was clicked onto a fraction of the available azido groups of a functional polysiloxane, yielding azido reactive PDMS-g-PEG rake surfactants. The reactive polymers were then used to crosslink alkynyl-terminated PDMS of different molecular weights. Using simple starting materials, this generic yet versatile method permits the preparation and characterization of a library of amphiphilic thermoset elastomers that vary in their composition, crosslink density, elasticity, hydrogel formation, and wettability. An appropriate balance of PEG length and crosslink density leads to a permanently highly wettable silicone elastomer that demonstrated very low levels of protein adsorption. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2015, 53, 1082–1093
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".