Liquid Crystal Networks Meet Water: It's Complicated!
Bibliographic record
Abstract
Soft robots are composed of compliant materials that facilitate high degrees of freedom, shape-change adaptability, and safer interaction with humans. An attractive choice of material for soft robotics is crosslinked networks of liquid crystal polymers (LCNs), as they are responsive to a wide variety of external stimuli and capable of undergoing fast, programmable, complex shape morphing, which allows for their use in a wide range of soft robotic applications. However, unlike hydrogels, another popular material in soft robotics, LCNs have limited applicability in flooded or aquatic environments. This can be attributed not only to the poor efficiency of common LCN actuation methods underwater but also to the complicated relationship between LCNs and water. In this review, the relationship between water and LCNs is elaborated and the existing body of literature is surveyed where LCNs, both hygroscopic and non-hygroscopic, are utilized in aquatic soft robotic applications. Then the challenges LCNs face in widespread adaptation to aquatic soft robotic applications are discussed and, finally, possible paths forward for their successful use in aquatic environments are envisaged.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".