Self‐Propelled Morphing Matter for Small‐Scale Swimming Soft Robots
Bibliographic record
Abstract
Abstract Aquatic insects have developed versatile locomotion mechanisms that have served as a source of inspiration for decades in the development of small‐scale swimming robots. However, despite recent advances in the field, efficient, untethered, and integrated powering, actuation, and control of small‐scale robots remains a challenge due to the out‐of‐equilibrium and dissipative nature of the driving physical and chemical phenomena. Here, we have designed small‐scale, bioinspired aquatic locomotors with programmable deterministic trajectories that integrate self‐propelled chemical motors and photoresponsive shape‐morphing structures. A Marangoni motor system is developed integrating structural protein networks that self‐regulate the release of chemical fuel with photochemical liquid crystal network (LCN) actuators that change their shape and deform in and out of the surface of water. While the diffusion of fuel from the motor system regulates the propulsion, the dissipative photochemical deformation of LCNs provides locomotors with control over the directionality of motion at the air‐water interface. This approach gives access to five different but interchangeable modes of locomotion within a single swimming robot via morphing of the soft structure. The proposed design, which mimics the mechanisms of surface gliding and posture change of semiaquatic insects such as water treaders, offers solutions for autonomous swimming soft robots via untethered and orthogonal power and control.
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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".