Photoresponsive Biomimetic Soft Robots Enabled by Near‐Infrared‐Driven and Ultrarobust Sandwich‐Structured Nanocomposite Films
Bibliographic record
Abstract
Soft robots, intelligent structures built up of smart soft materials, are capable of being programmed to perform delicate work. Recently, plenty of biomimetic soft robots with functionalities of grasping, sensing, searching, and transporting have been exploited by emulating activities of living creatures adapting to ecological environments. However, mass production of biomimetic soft robots has remained a grand challenge while maintaining stable pre‐engineered functionalities under distinct circumstances, which significantly constrains their practical applications. To this end, a facile and scalable approach that can be utilized for mass‐producing sandwich‐structured photoresponsive polyimide (PI)/Au/low‐density polyethylene (LDPE) nanocomposite films is reported. Attributed to the remote and precise‐driven mode, reversible and stable actuation behavior, and the ultrarobust mechanical properties of the sandwich‐structured PI/Au/LDPE nanocomposite films, it was possible to devise a variety of photoresponsive biomimetic soft robots such as artificial flytrap, directionally moveable caterpillar‐inspired walker, and dolphin‐like cruisable and loadable swimmer via simply tailoring them into predesigned geometries.
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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".