Encoding kirigami bi-materials to morph on target in response to temperature
Bibliographic record
Abstract
Shape morphing in response to an environmental stimulus, such as temperature, light, and chemical cues, is currently pursued in synthetic analogs for manifold applications in engineering, architecture, and beyond. Existing strategies mostly resort to active, namely smart or field responsive, materials, which undergo a change of their physical properties when subjected to an external stimulus. Their ability for shape morphing is intrinsic to the atomic/molecular structure as well as the mechanochemical interactions of their constituents. Programming shape changes with active materials require manipulation of their composition through chemical synthesis. Here, we demonstrate that a pair of off-the-shelf passive solids, such as wood and silicone rubber, can be topologically arranged in a kirigami bi-material to shape-morph on target in response to a temperature stimulus. A coherent framework is introduced to enable the optimal orchestration of bi-material units that can engage temperature to collectively deploy into a geometrically rich set of periodic and aperiodic shapes that can shape-match a predefined target. The results highlight reversible morphing by mechanics and geometry, thus contributing to relax the dependence of current strategies on material chemistry and fabrication.
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How this classification was reachedexpand
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 | 0.002 | 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 teacher head, 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".