Low-Fouling Characteristics of Ultrathin Zwitterionic Cysteine SAMs
Bibliographic record
Abstract
Surface fouling remains an exigent issue for many biological implants. Unwanted solutes adsorb to reduce device efficiency and hasten degradation while increasing the risks of microbial colonization and adverse inflammatory response. To address unwanted fouling in modern implants in vivo, surface modification with antifouling polymers has become indispensable. Recently, zwitterionic self-assembled monolayers, which contain two or more charged functional groups but are electrostatically neutral and form highly hydrated surfaces, have been the focus of many antifouling coatings. Reports using various compositions of zwitterionic polymer brushes have demonstrated ultralow fouling in the ng/cm2 range. These coatings, however, are thick and can hinder the target application of biological devices. Here, we report an ultrathin (8.52 Å) antifouling self-assembled monolayer composed of cysteine that is amenable to facile fabrication. The antifouling characteristics of the zwitterionic surfaces were evaluated against bovine serum albumin, fibrinogen, and human blood in real time using quartz crystal microbalance and surface plasmon resonance imaging. Compared to untreated gold surfaces, the ultrathin cysteine coating reduced the adsorption of bovine serum albumin by 95% (43 ng/cm2 adsorbed) after 3 h and 90% reduction after 24 h. Similarly, the cysteine self-assembled monolayer reduced the adsorption of fibrinogen as well as human blood by >90%. The surfaces were further characterized using scanning electron microscopy: protein-enhanced adsorption and cellular adsorption in human blood was found on untreated surfaces but not on the cysteine SAM-protected surfaces. These findings suggest that surfaces can be functionalized with an ultrathin layer of cysteine to resist the adsorption of key proteins, with performance comparable to zwitterionic polymer brushes. As such, cysteine surface coatings are a promising methodology to improve the long-term utility of biological devices.
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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) | 0.001 | 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 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".