Polymer Microarray with Tailored Morphologies through Condensed Droplet Polymerization for High‐Resolution Optical Imaging Applications
Bibliographic record
Abstract
Nature-inspired functional surfaces with micro- and nanoscale features have garnered interest for potential applications in optics, imaging, and sensing. Traditional fabrication methods, such as lithography and self-assembly, face limitations in versatility, scalability, and morphology control. This study introduces an innovative technology, condensed droplet polymerization (CDP), for fabricating polymeric micro- and nano-dome arrays (PDAs) with readily tunable geometric properties-a challenging feat for conventional methods. The CDP process leverages free-radical polymerization in condensed monomer droplets, allowing rapid production of PDAs with targeted sizes, radii of curvature, and surface densities by manipulating a key synthesis parameter: the temperature of a filament array that activates initiators. This work systematically unravels its effects on polymerization kinetics, viscoelastic properties of the polymerizing droplets, and geometric characteristics of the PDAs. Utilizing in situ digital microscope, this work reveals the morphological evolution of the PDAs during the process. The resulting PDAs exhibit distinct optical properties, including magnification that enables high-resolution imaging beyond the diffraction limit of conventional microscopes. This work demonstrates the ability to magnify and focus light, enhancing imaging of subwavelength structures and biological specimens. This work advances the understanding of polymerization mechanisms in nano-sized reactors (i.e., droplets) and paves the way for developing compact optical imaging and sensing technologies.
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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".