Conductive Electrochemically Active Lubricant‐Infused Nanostructured Surfaces Attenuate Coagulation and Enable Friction‐Less Droplet Manipulation
Bibliographic record
Abstract
Abstract Micro/nanostructured materials and lubricant‐infused surfaces, both inspired from structures found in nature, are ideally suited for developing self‐cleaning and high surface area transducers for biosensing. These two classes of bio‐inspired technologies are integrated to develop lubricant‐infused electrodes designed to reduce biofouling. Chemical vapor deposition is used to create self‐assembled monolayers of fluorosilane on gold‐modified prestrained polystyrene substrates. After heat shrinking of the substrate, a lubricant is applied to produce a lubricant‐infused nanostructured gold wrinkled surface with hydrophobic properties. These electrically conductive surfaces demonstrate high water contact (≈150°) and low sliding angles (<5°). Moreover, combining these surfaces with passive magnetic actuators enables the actuation of super‐paramagnetic microdroplets in frictionless and open channel conditions without needing full droplet submersion in an immiscible fluid. The fabricated nanostructured surfaces resist protein adhesion in a human plasma coagulation assay and significantly prolong clotting times and retain electrical conductivity, which is essential for electrical sensing applications. The developed hybrid interfaces are expected to have a wide range of applications in biosensing and biological sample preparation involving complex clinical and environmental samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".