Bibliographic record
Abstract
Abstract Smart microstructured materials enable functions such as actuation, detection, transportation, and sensing with potential applications ranging from robotics and photonics to biomedical devices. Of the many materials systems, liquid crystal polymer networks (LCN) are fascinating owing to their ability to exhibit reversible macroscopic deformation driven by a molecular order–disorder phase transition. LCN have been increasingly explored for their utility in the design and fabrication of smart actuating devices capable of complex shape changes or motions upon external stimulation of humidity, heat, light, and other stimuli, and recent studies in this field show that their actuation complexity can be enriched and actuation performance enhanced by having some sort of microstructures. Herein, the recent progress in microstructured actuation of LCN materials with substructures in scale ranging from micrometer to millimeter is reported, placing the emphasis on the main approaches to generating a microstructure in LCN, which include patterned LC director fields, patterned chain crosslinking in LCN with uniaxial orientation of mesogens, 3D/4D printing, and replica molding. The potential applications in microstructured 3D actuators and devices as well as functional LCN surfaces are also highlighted, with an outlook on important issues and future trends in smart microstructured LCN materials and 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 | 0.000 | 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".