Making Photonic Crystals via Evaporation of Nanoparticle-Laden Droplets on Superhydrophobic Microstructures
Bibliographic record
Abstract
We employed a convenient evaporation approach to fabricate photonic crystals by naturally drying droplets laden with nanoparticles on a superhydrophobic surface. The final drying morphology could be controlled by the concentration of nanoparticles. A dilute droplet resulted in a torus, whereas a quasi-spherical cap with a bottom cavity was made from a concentrated droplet. Remarkably, the nanofluid droplets maintained high contact angles (≳120°) during the entire evaporation process because of inhomogeneous surface wetting. Bottom-view snapshots revealed that during evaporation the color of the contact area changed sequentially from white to red, orange, yellow, and eventually to green. Scanning electron microscopy and Voronoi analysis demonstrated that nanoparticles were self-assembled to a hexagonal pattern. Finally, based on the effects of particle size, material, and volume concentration on the reflected wavelengths, a model has been developed to successfully predict the reflected wavelength peaks from the contact area of evaporating colloidal droplets. Our model can be easily adopted as a manufacturing guide for functional photonic crystals to predict the optimal reflected color made by evaporation-driven self-assembly of photonic crystals.
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 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 | 0.000 |
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 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".