Microscale Polarization Color Pixels from Liquid Crystal Elastomers
Bibliographic record
Abstract
Abstract Liquid crystal elastomers (LCEs) attract burgeoning research interests due to their programmable 3D shape transformation and achievable large strain (up to 400%), which allow their diverse applications. One exciting application is soft microrobots towards biomedical applications. Here, it is demonstrated that polarization colors, an intrinsic property from the anisotropic nature of LCEs, can assist LCEs in their diverse applications. Microscale color pixels with sizes as small as 15 µm × 15 µm are obtained with designable colors from precisely controlled thicknesses enabled by two‐photon polymerization technique. It is then demonstrated that in‐plane rotation of LCE pixels only changes their color brightness instead of color hue and small tilting angle (<15°) causes almost no change of their color. These properties enable high‐contrast tracking of LCEs under polarized microscope. Furthermore, it is exhibited that both 2D and 3D microstructures with pre‐designed multiple colors can be realized, allowing for LCE applications requiring precise discrimination of different components. Finally, dynamic polarization colors are explored by insertion of waveplates and change of temperature, which allows for either better tracking of LCEs or applications of LCEs in temperature sensing and information encryption. It is expected that polarization colors will assist various LCE applications, especially soft microrobot and art display applications.
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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".