Tunable Multiplanar Nanowrinkled Surface Platform
Bibliographic record
Abstract
Abstract Wrinkled surfaces have extraordinary characteristics, such as enlarged surface area and enhanced adhesion, which form the basis of several emerging applications in electrical, mechanical, and biological processes. Furthermore, wrinkles at a nanoscale display additional ultrahigh functions offering enhanced Raman scattering and superhydrophobicity; however, artificial nanowrinkled morphology is mostly limited to simple planar surfaces. Here, a microfluidics‐based approach to create tunable nanowrinkles on nonplanar surfaces is developed. In this approach, polyethylene glycol microposts using photolithography in a polydimethylsiloxane microfluidic channel are first fabricated. A syringe pump is then used with a washing solvent, ethanol, to remove the excess unpolymerized monomer from the microfluidic channel. In combination with nonuniform photopolymerization, this washing postprocess results in microposts with a fully cured inner layer and a partially cured outer layer. The partially cured layer spontaneously forms nanowrinkles upon plasma treatment. Their wavelengths are tunable by controlling the thickness of the partially cured layer during the washing process. The effect of the flow rate and duration of the washing fluid on the wavelength of nanowrinkle formed is investigated.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".