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 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) | 0.002 | 0.001 |
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".