Surface etching of silicone elastomers by depolymerization
Bibliographic record
Abstract
Silicone elastomer surfaces that are rough at the nanometer to micron scales could be useful for biomaterials, but there are few efficient routes for their preparation. Silicones undergo depolymerization under equilibrating conditions. We demonstrate that surface roughness can be induced by depolymerizing silicone elastomers using triflic acid, tetrabutylammonium fluoride or KOH as catalysts. The efficiency of depolymerization, however, is decoupled from the roughness that develops. When the catalysts are dissolved in solvents that do not effectively swell silicones, the etching reaction can be mostly directed to the elastomer surface. Acid catalysis leads to slow, nearly homogenous surface erosion with surface roughnesses only increasing from 15 to about 125 nm root mean squared roughness. By contrast, once KOH partitions into the elastomer, the rate of erosion is more efficient than return of the catalyst to the solvent, leading to deep channels and roughnesses of up to ∼850 nm. The use of fluoride requires good solvents for silicone, and leads to surfaces of intermediate roughness. Thus, judicious choice of catalyst and solvent permits independent control over depolymerization and the induction of surface roughness.
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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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".