Micropatterned biofunctional lubricant-infused surfaces promote selective localized cell adhesion and patterning
Bibliographic record
Abstract
Micropatterned biofunctional surfaces provide a wide range of applications in bioengineering. A key characteristic which is sought in these types of bio-interfaces is prevention of non-specific adhesion for enhanced biofunctionality and targeted binding. Lubricant-infused omniphobic coatings have exhibited superior performance in attenuating non-specific adhesion; however, these coatings completely block the surfaces and do not support targeted adhesion or patterning. In this work, we introduce a novel lubricant-infused surface with biofunctional micropatterned domains integrated within an omniphobic layer. This new class of micropatterned lubricant-infused surfaces simultaneously promotes localized and directed binding of desired targets, as well as repellency of undesired species, especially in human whole blood. Furthermore, this modification method is easily translatable to microfluidic devices offering a wider range of applications and improved performance for immunoassays in whole blood and inhibition of clot formation in microfluidic channels. The biofunctional micropatterned lubricant-infused surfaces were created through a bench-top straight forward process by integrating microcontact printing, chemical vapor deposition (CVD) of self-assembled monolayers (SAMs) of fluorosilanes, and further infusion of the SAMs with a bio-compatible fluorocarbon-based lubricant layer. The developed surfaces, patterned with anti-CD34 antibodies, yield enhanced adhesion and controlled localized binding of target biomolecules (e.g. antibodies) and CD34 positive cells (e.g. HUVECs) inside microfluidic devices, outperforming conventional blocking methods (e.g. bovine serum albumin (BSA) or poly(ethylene glycol) (PEG)) in buffer and human whole blood. These surfaces offer a straightforward and effective way to enhance blocking capabilities while preserving the biofunctionality of a micropatterned system in complex biological environments such as whole blood. We anticipate that these micropatterned biofunctional interfaces will find a wide range of applications in microfluidic devices and biosensors for enhanced and localized targeted binding while preventing non-specific adhesion.
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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) | 0.001 | 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".