Facile Functionalization of PDMS Elastomer Surfaces Using Thiol–Ene Click Chemistry
Bibliographic record
Abstract
A variety of methods have been developed for polydimethylsiloxane (PDMS) elastomer surface functionalization, particularly for the improvement of hydrophilicity. However, in addition to difficulties in avoiding undesired physical changes to the modified surface, including surface cracking, "hydrophobic recovery" frequently leads hydrophilically modified surfaces to completely return over time to their hydrophobic nature, with accompanying loss of accessible functional groups. Thiol-ene chemistry provides a mild and robust technology for synthetic elaboration. We demonstrate the introduction of thiol groups onto the PDMS surface via base-catalyzed equilibration of MTS ((MeO)3Si(CH2)3SH). Thiols in the product elastomer were shown to be located primarily at the air interface using EDX, XPS, and fluorescence labeling initially, and after extended periods of time: total thiol concentrations at the surface and in the bulk were established by complementary chemical titrations with DTDP (4,4'-dithiodipyridine) and iodine titrations in different solvents. The surface density of thiols was readily controlled by reaction conditions: the rate of hydrophobic recovery, which led to incomplete loss of accessible functional groups, was determined. Thiol-ene click chemistry was then used to introduce a variety of hydrophilic moieties onto the surface including a silicone surfactant and maleic anhydride, respectively. In the latter case, molecular functionalization with both small (fluorescent labels) and polymeric nucleophiles (poly(ethylene glycol), chitosan) could be subsequently induced by simple ring-opening nucleophilic attack leading to permanently functional surfaces.
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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 | 0.000 |
| 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".