Suppression of the Coffee-Ring Effect for Photonic Structures Based on the Shape of Particles
Bibliographic record
Abstract
The evaporation of colloidal sessile droplets is widely studied for the fabrication of micro- and nanoscale structures with applications in photonics, sensing, and functional coatings. When spherical particles are used, the process typically results in a coffee-ring effect (CRE), driven by capillary outward flow. A well-established strategy to counteract the CRE involves leveraging shape-dependent capillary meniscus forces (CMFs), which promote the formation of interfacial films and enable more uniform particle deposition. Despite the progress, the role of anisotropic particles in forming photonic microstructures during evaporation remains insufficiently understood. This study investigates the influence of CMFs induced by particle shape on the formation of structurally colored photonic assemblies. Unlike spherical particles, ellipsoidal particles effectively suppress the CRE and enable uniform deposition with a structural color. The photonic properties of the resulting structures are strongly dependent on the aspect ratio (α) of the ellipsoidal particles. Ellipsoidal particles with lower α values form well-ordered, in-plane-packed structures that retain vivid structural coloration. In contrast, higher α values lead to more random arrangements, diminishing the photonic characteristics. Furthermore, the inclusion of small fractions of ellipsoidal particles in a droplet primarily composed of spherical particles facilitates the formation of photonic structures. This work demonstrates the use of monodispersed ellipsoidal particles and mixtures of spherical particles with ellipsoidal particles as colloidal inks for the fabrication of photonic structures. The findings underscore the crucial role of particle shape and composition in determining the optical properties of colloidal assemblies, thereby bridging the gap between fundamental studies on CRE suppression and the practical realization of photonic crystal-based materials.
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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) | 0.000 | 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 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".