Temperature‐Driven Topological Transformations in Prestressed Cellular Metamaterials
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Stimuli‐responsive materials are able to alter their physicochemical properties, e.g., shape, color, or stiffness, upon exposure to an external trigger, e.g., heat, light, or humidity, exhibiting environmental adaptability. Their capacity to undergo shape reconfiguration, pattern transformation, and property modulation enables multifunctionality. In this work, two strategies are harnessed, i.e., prestressed assembly and temperature‐dependent stiffness reversal, to introduce a class of temperature‐responsive metamaterials capable of undergoing topological transformations, endowing them with smart functionality. Through a combination of mechanics theory, numerical simulations, and thermomechanical experiments, the physical mechanisms underlying the temperature‐triggered topological transformations leading to pattern switches are first elucidated, and then the insights are leveraged to demonstrate tunable bandgaps and robotic capturers. These findings reveal the attainment of giant negative and positive values of coefficient of thermal expansion, accompanied by isotropic expansion and shrinkage under thermal actuation within a fairly rapid timeframe, below 6 s. The strategy here presented is versatile as it relies on a pair of off‐the‐shelf 3D printable materials, can be up‐ and down‐scaled, and can also be realized through other physical stimuli, e.g., light and moisture, paving the way for use in multifunctional applications, including stimulus‐triggered morphing devices, autonomous sensors and actuators, and reconfigurable soft robots.
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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) | 0.001 | 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 it