Temperature-responsive multistable kirigami with reprogrammable multi-shape memory
Bibliographic record
Abstract
Shape memory materials retain temporary shapes without external constraints and return to their permanent shape when exposed to an external trigger, e.g., light, humidity, or heat. Current shape memory materials can maintain a modest number of shapes, deliver limited modes of deformation with undesired spring-back, suffer slow response speed, and typically require laborious thermomechanical programming and tuning their glass transition temperatures through alteration in chemical composition. In this work, we demonstrate the attainment of a robust and simplified multi-shape memory effect in a class of 3D-printed kirigami that merely relies on two off-the-shelf polymers with distinct temperature-dependent elastic moduli. By programming the kirigami multistability in the low-temperature regime, our multi-shape memory metamaterials can be reconfigured in-situ to retain a geometrical rich and diverse set of stable temporary shapes in planar and spatial kirigami tessellations before reverting to their permanent shape through a heat-induced stiffness reversal. Through mechanics theory, numerical simulations, and thermomechanical experiments, we first investigate the physical mechanism that marks stability transitions and deformation modes, and then leverage the insights to demonstrate their multifunctionality in a diverse range of applications, including temperature sensors, actuators, and robotic grippers. Unreliant on the chemistry tuning of material composition, their hallmarks include the delivery of multiple deformation modes and combination thereof, rich and robust multi-shape memory effect with no spring-back, reprogrammable shape changes, stiffness switch, and heat-induced swift shape recovery. Our strategy is versatile, can be adapted to other 3D printable materials and physicochemical stimuli, e.g., light, moisture, and solute, and can be up- and down-scaled, paving the way for a wide range of multifunctional applications, including adaptive morphing devices, self-powered sensors and actuators, and reconfigurable soft robots.
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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".