Shining Light on Liquid Crystal Polymer Networks: Preparing, Reconfiguring, and Driving Soft Actuators
Bibliographic record
Abstract
Abstract Soft actuators based on liquid crystal polymer networks (LCNs) have emerged as an exciting research topic due to many envisioned applications in areas such as soft robotics and self‐regulating devices. The infatuation stems from the amazing ability of LCNs to display reversible, large amplitude, and complex shape change as well as locomotion upon a stimuli‐triggered phase transition of mesogens between ordered liquid crystal and disordered isotropic state. Among the various stimuli, light arguably is the most attractive choice. Light can easily be adjusted for its wavelength, intensity, or polarization, structured by means of photomasks or interference patterns, and applied to a target remotely and with high spatiotemporal resolution. Indeed, much research effort is dedicated to LCN actuators that are designed to respond to light in many ways, with light being used not only as an external energy source to drive the shape changes or motion of LCN actuators, but also as a versatile tool in their fabrication and reconfiguration. In this Review, recent achievements are highlighted, a number of important issues are discussed, and critical analysis on the use of light in making, reconfiguring, and driving LCN actuators is provided.
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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".