Liquid‐Crystal‐Elastomer‐Actuated Reconfigurable Microscale Kirigami Metastructures
Bibliographic record
Abstract
Programmable actuation of metastructures with predesigned geometrical configurations has recently drawn significant attention in many applications, such as smart structures, medical devices, soft robotics, prosthetics, and wearable devices. Despite remarkable progress in this field, achieving wireless miniaturized reconfigurable metastructures remains a challenge due to the difficult nature of the fabrication and actuation processes at the micrometer scale. Herein, microscale thermo-responsive reconfigurable metasurfaces using stimuli-responsive liquid crystal elastomers (LCEs) is fabricated as an artificial muscle for reconfiguring the 2D microscale kirigami structures. Such structures are fabricated via two-photon polymerization with sub-micrometer precision. Through rationally designed experiments guided by simulations, the optimal formulation of the LCE artificial muscle is explored and the relationship between shape transformation behaviors and geometrical parameters of the kirigami structures is build. As a proof of concept demonstration, the constructs for temperature-dependent switching and information encryption is applied. Such reconfigurable kirigami metastructures have significant potential for boosting the fundamental small-scale metastructure research and the design and fabrication of wireless functional devices, wearables, and soft robots at the microscale as well.
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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".