Surface Modification of Poly(dimethylsiloxane) with a Perfluorinated Alkoxysilane for Selectivity toward Fluorous Tagged Peptides
Bibliographic record
Abstract
Poly(dimethylsiloxane) (PDMS) and similar polymers have proved to be of widespread interest for use in microfluidic and similar microanalytical devices. Surface modification of PDMS is required to extend the range of applications for devices made of this polymer, however. Here we report on the grafting of perfluorooctyltriethoxysilane via hydrolysis onto an oxidized PDMS substrate in order to form a fluorinated microchannel. Such a fluorinated device could be used for separating fluorous tagged proteins or peptides, similar to that which has been recently demonstrated in a capillary electrophoresis system or in an open tubular capillary column. The modified polymer is characterized using chemical force titrations, contact angle measurements, and X-ray photoelectron spectroscopy (XPS). We also report on a novel means of performing electroosmotic measurements on this material to determine the surface zeta potential. As might be expected, contact angle and chemical force titration measurements indicate the fluorinated surface to be highly hydrophobic. XPS indicates that fluorocarbon groups segregate to the surface of the polymer over a period of days following the initial surface modification, presumably driven by a lower surface free energy. One of the most interesting results is the zeta potential measurements, which show that significant surface charge can be maintained across a wide range of pH on this modified polymer, sufficient to promote electroosmotic flow in a microfluidic chip. Matrix-assisted time-of-flight mass spectrometry (MALDI-TOF MS) measurements show that a fluorous-tagged peptide will selectively adsorb on the fluorinated PDMS in aqueous solution, demonstrating that the fluorinated polymer could be used in devices designed for the enrichment or enhanced detection of fluorous-labeled proteins and peptides.
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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".