Lubricant-Infused Surfaces with Built-In Functional Biomolecules Exhibit Simultaneous Repellency and Tunable Cell Adhesion
Bibliographic record
Abstract
Lubricant-infused omniphobic surfaces have exhibited outstanding effectiveness in inhibiting nonspecific adhesion and attenuating superimposed clot formation compared with other coated surfaces. However, such surfaces blindly thwart adhesion, which is troublesome for applications that rely on targeted adhesion. Here we introduce a new class of lubricant-infused surfaces that offer tunable bioactivity together with omniphobic properties by integrating biofunctional domains into the lubricant-infused layer. These novel surfaces promote targeted binding of desired species while simultaneously preventing nonspecific adhesion. To develop these surfaces, mixed self-assembled monolayers (SAMs) of aminosilanes and fluorosilanes were generated. Aminosilanes were utilized as coupling molecules for immobilizing capture ligands, and nonspecific adhesion of cells and proteins was prevented by infiltrating the fluorosilane molecules with a thin layer of a biocompatible fluorocarbon-based lubricant, thus generating biofunctional lubricant-infused surfaces. This method yields surfaces that (a) exhibit highly tunable binding of anti-CD34 and anti-CD144 antibodies and adhesion of endothelial cells, while repelling nonspecific adhesion of undesirable proteins and cells not only in buffer but also in human plasma or human whole blood, and (b) attenuate blood clot formation. Therefore, this straightforward and simple method creates biofunctional, nonsticky surfaces that can be used to optimize the performance of devices such as biomedical implants, extracorporeal circuits, and biosensors.
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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) | 0.000 | 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 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".