Mesostructure of Ordered Corneal Nano-nipple Arrays: The Role of 5–7 Coordination Defects
Bibliographic record
Abstract
Corneal nano-nipple structures consisting of hexagonally arranged protrusions with diameters around 200 nm have long been known for their antireflection capability and have served as biological blueprint for solar cell, optical lens and other surface designs. However, little is known about the global arrangement of these nipples on the ommatidial surface and their growth during the eye development. This study provides new insights based on the analysis of nano-nipple arrangements on the mesoscale across entire ommatidia, which has never been done before. The most important feature in the nipple structures are topological 5- and 7-fold coordination defects, which align to form dislocations and interconnected networks of grain boundaries that divide the ommatidia into crystalline domains in different orientations. Furthermore, the domain size distribution might be log-normal, and the domains demonstrate no preference in crystal orientation. Both observations suggest that the nipple growth process may be similar to the nucleation and growth mechanisms during the formation of other crystal structures. Our results are also consistent with the most recently proposed Turing-type reaction-diffusion process. In fact, we were able to produce the key structural characteristics of the nipple arrangements using Turing analysis from the nucleation to the final structure development.
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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".