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Record W2466871048 · doi:10.1038/srep28342

Mesostructure of Ordered Corneal Nano-nipple Arrays: The Role of 5–7 Coordination Defects

2016· article· en· W2466871048 on OpenAlexafffund
Ken C. Lee, Qi Yu, U. Erb

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNano-Computer scienceAnatomyMedicineMaterials science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.209
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2016
Admission routes2
Has abstractyes

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