Artificial Compound Eye with Tunable Properties for Enhancement in Fluorescence Imaging and Raman Detection
Bibliographic record
Abstract
Abstract Artificial compound eye (CE) draws inspiration from nature, offering advanced imaging capabilities and an expansive field of view. In this work, an innovative technique is developed for the creation of CE with tunable dimensions. A solution‐based process is employed that involves in situ polymerization of surface nanodroplets prior to soft lithography to produce CE consisting of millions of ommatidia. The fabricated CE comprised of a densely arranged array of microwells, each with a base radius of 5 µm. Situated on a millimeter‐sized spherical dome, the CE can be tailored to arbitrary dimensions, enhancing its adaptability with a wide angular field of view up to 118°. The CE is used to enhance signal detection in fluorescent compounds, reaching a detection limit of 107 times lower concentration than that without using CEs in bulk solution. The signal enhancement capabilities are further utilized for surface‐enhanced Raman spectroscopy by using a portable handheld device, with an enhancement factor of 2. The fabrication technique underscores the advantages of the approach in simplicity, reproducibility, and efficiency in creating CE. The potential applications of CE may be extended to various domains, such as optical sensing, light‐dependent signal enhancement, motion perception, and medical endoscopy.
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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".