Two-Way Reversible Shape Memory Polymers Containing Polydopamine Nanospheres: Light Actuation, Robotic Locomotion, and Artificial Muscles
Bibliographic record
Abstract
Two-way reversible shape memory polymers (2W-SMPs), especially those that are light-responsive, are highly desirable for many applications, especially in the biomedical field, because of the convenience of indirect heating. We have designed and prepared a series of light-actuated 2W-SMP composites by incorporating very small amounts of polydopamine (PDA) nanospheres into semicrystalline polymer networks based on biodegradable poly(ε-caprolactone) copolymers. PDA nanospheres can be well dispersed in chloroform and well mixed with the polymer network. PDA nanospheres manifest good photothermal effect because of their strong absorption of light. The variation in temperature of the polymer composites can be correlated with irradiation time, light intensity, and the content of PDA nanospheres. Equations are developed to fit the temperature changes of the materials as a function of irradiation power and of the PDA particles content for a better understanding of the kinetics of the light-to-heat conversion. These polymer composites show excellent two-way reversible shape memory effects (2W-SMEs) under stress-free condition when the light is switched on and off showing a reversible angle change of 45°. The speed of angle change is larger for polymer composites irradiated with a stronger light or with a higher content of PDA nanospheres. This is the first report on 2W-SMPs using incorporated PDA nanospheres as photothermal fillers. A moving robot is designed based on photoresponsive 2W-SMP composites, which can walk on a track with triangular saw-teeth. This composite is capable of lifting and lowering a weight, acting as artificial muscles, and its actuated stress is much higher than the maximum stress yielded by most mammalian skeletal muscles. The use of biodegradable polyesters and thermal fillers made of a natural compound dopamine makes such composites potentially useful as biomaterials.
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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) | 0.000 | 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".