Hydrolysis of silicone polymers in aqueous systems
Bibliographic record
Abstract
Silicon-29 and 'H NMR spectroscopy were used to investigate the hydrolysis \nof methyl silicones in a variety of aqueous environments. Dilute acid, dilute base and \none or more of the dissolved constituents of blood plasma were found to catalyse the \ndegradation of polydimethylsiloxane (PDMS)-and its hydrojty- and methoxyterminated \nderivatives-along with the interior and shell of a silicone mammary \nprosthesis. Dimethylsilanediol (DMSD) was the principal hydrolysis product, \nalthough in many instances (most notably after long decomposition periods) the dimer \n(tetramethyldisiloxanediol) and/or certain cyclodimethylsiloxane species were also \ndetected. Only for hexamethyldisiloxane was silicic acid detected as an additional \nhydrolysis product after long-term interaction with blood plasma. Alcohols and \ncertain aliphatic polyols, when added to solution, typically caused alkoxylation of \nDMSD and the dimer. \nThe presence of electron withdrawing hydroxy and methoxy end groups greatly \nincreased the rate of degradation of PDMS. Both the shell and interior of the \nmammary prosthesis also hydrolysed faster than pure PDMS, which would suggest \nthat there is considerably less than 100 % trimethylsilyl capping of their constituent \npolymer chains.
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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) | 0.001 | 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 | 0.001 |
| 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".