A Porous Multi‐Stimuli‐Responsive Liquid Crystal Elastomer Actuator Enabled by Mof Loading
Bibliographic record
Abstract
Abstract A porous actuator is prepared using a liquid crystal elastomer (LCE) loaded with metal‐organic framework (MOF) nanoparticles (MIL‐88A). While the swellable MOF additive endows the LCE actuator with nanoscale pores, after its removal by chemical etching, macroporous LCE actuator is obtained. This LCE‐MOF actuator displays several interesting features. 1) Selective etching allows the actuator in water to curl in one direction and then reverse the curling direction owing to differential water absorption of the nano‐ and macroporous layers. 2) The stretching‐induced alignment of mesogens in the actuator is little affected by the presence of MOF and substantially retained after water uptake, so that an actuation deformation can be generated either by water absorption or thermally induced order‐disorder phase transition. 3) Assisted by the UV irradiation used for polymer cross‐linking, magnetic iron oxide FeO appears to be formed after chemical etching, which allows the actuator to gain the ability to move on water surface guided by a magnet. 4) The in situ formation of magnetic iron oxide in the macroporous actuator also provides an enhanced photothermal effect, making light‐driven locomotion of the actuator more effective. The use of active porogen like MOF opens a new way to explore porous LCE actuators.
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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".