Electrically driven liquid crystal network actuators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Soft actuators based on liquid crystal networks (LCNs) have aroused great scientific interest for use as stimuli-controlled shape-changing and moving components for robotic devices due to their fast, large, programmable and solvent-free actuation responses. Recently, various LCN actuators have been implemented in soft robotics using stimulus sources such as heat, light, humidity and chemical reactions. Among them, electrically driven LCN actuators allow easy modulation and programming of the input electrical signals (amplitude, phase, and frequency) as well as stimulation throughout the volume, rendering them promising actuators for practical applications. Herein, the progress of electrically driven LCN actuators regarding their construction, actuation mechanisms, actuation performance, actuation programmability and the design strategies for intelligent systems is elucidated. We also discuss new robotic functions and advanced actuation control. Finally, an outlook is provided, highlighting the research challenges faced with this type of actuator.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.005 | 0.001 |
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 it