Dynamic Fluoroalkyl Polyethylene Glycol Co‐Polymers: A New Strategy for Reducing Protein Adhesion in Lab‐on‐a‐Chip Devices
Bibliographic record
Abstract
Non‐specific adsorption of biomolecules (or “biofouling”) is a major problem in microfluidics and many other applications. The problem is particularly pernicious in digital microfluidics (DMF, a technique in which droplets are manipulated electrodynamically on an array of electrodes coated with a hydrophobic insulator), as local increases in surface energy that arise from fouling can cause droplet movement to fail. We report a new solution to this problem: a device coating bearing a combination of fluorinated poly(ethylene glycol) functionalities (FPEG) and perfluorinated methacrylate (FA) moieties. A range of different FPEG‐FA copolymers were synthesized containing varying amounts of FPEG relative to the fluorinated backbone. Coatings with low%FPEG were found to result in significant reductions in protein adsorption and improvements in device lifetime (up to 5.5‐fold) relative to the state of the art. An analysis of surface topology and chemistry suggests that FPEG‐FA surfaces undergo a dynamic reconstruction upon activation by applying DMF driving potentials, with FPEG groups forming vertical protrusions out of the plane of the device surface. An analysis of changes in surface wettability and adhesion as a function of activation supports this hypothesis. This innovation represents an advance for digital microfluidics, and may also find use in other applications that are currently limited by biofouling.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".