State of the Art, Insights and Perspectives for Bio‐Inspired Liquid Crystal Elastomer Soft Actuators
Bibliographic record
Abstract
After 30 years of development, liquid crystal elastomer (LCE) biomimetic soft robots have been engineered to possess the capability to mimic and surpass the locomotion of soft organisms. However, most of the current reviews on LCEs are focused on their material/alignment design, fabrication technologies, actuation mechanisms, and applications. The latest research progress of biomimetic LCE soft actuators is systematically reviewed here from a novel perspective of material-structure-function interrelationship, which includes plant-inspired biomimetic shape-morphing of LCEs, animal-inspired biomimetic locomotion of LCEs, and bionic intelligent color-changing of LCEs. In addition, the potential application prospects and challenges of bio-inspired LCE soft actuators are discussed, where further in-depth research is required. Directions and valuable insights are provided for subsequent research efforts. This paper offers an inspiring and critical overview of the significant progress in the field of smart biomimetic LCE soft robotics and provides an available guide for researchers who are considering entering the exciting domain of LCE soft actuators.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".