Semi‐Crystalline Rubber as a Light‐Responsive, Programmable, Resilient Robotic Material
Bibliographic record
Abstract
Abstract Polymers with large and reversible light‐induced deformation offer a plethora of opportunities for the wireless control of small‐scale soft robots. However, their widespread adoption in real‐world applications is hindered, mainly due to their intrinsic softening upon illumination. Such limitation has detrimental effects on the achievable stress, durability, and precise positional control of the soft actuators after multiple cycles of use. Here, a synthetic rubber from a polybutadiene‐polyethylene copolymer is reported as a durable material for light‐controlled soft robots. The rubber can be programmed to exhibit various deformation modes controlled by visible‐to‐infrared light through a photothermal effect. Semi‐crystallinity of polyethylene within the rubbery network provides this material with a remarkable modulus at high temperatures (2.5 MPa at 100–140 °C), deformation repeatability (>90%) and shape‐recovery (>98%) after 100 actuation cycles subject to loads ranging from 10 to 10 000 times of its body weight (1.4 kPa–1.4 MPa). Soft robotic applications are demonstrated, such as thermally‐driven jumping and photo‐driven cargo transport carrying up to 1200 times its own weight. The results expand the portfolio of materials in designing remotely‐controlled, robust, and resilient soft robots working at small scales.
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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) | 0.002 | 0.001 |
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".