Multilayer Graphene/PDMS Composite Gradient Materials for High‐Efficiency Photoresponse Actuators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it