Multilayer Graphene/PDMS Composite Gradient Materials for High‐Efficiency Photoresponse Actuators
Bibliographic record
Abstract
Abstract Smart actuating materials have a wide range of applications in artificial muscles, soft robots, and flexible electronics. The preparation of highly sensitive and reliable actuators is a top priority in this regard. In this work, a multilayer graphene/polydimethylsiloxane (PDMS) composite gradient material is designed and prepared by a simple in situ stacking and curing method for high‐efficiency photoresponse actuator. The typical gradient structured material consists of a pure PDMS film and multiple graphene/PDMS composite films with monotonically varying graphene concentration. Attributed to gradient structure design and high photothermal conversion efficiency of graphene, the actuator shows the enhanced photoresponse properties. Through theoretical modeling, finite element analysis and experiments, it is confirmed that with increasing the stacked layer number at the same total thickness, the gradient structured actuator can present a better actuation performance. In addition, the film thickness and the concentration of graphene are also found to have an obvious effect on the actuating behavior, enabling the deflection over 90°. The applications of the actuator as a cantilever beam, a soft crawling robot and a smart gripper are also demonstrated. This provides a new design idea for further improving the actuation performance of the soft actuator.
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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".