Temperature‐Driven Topological Transformations in Prestressed Cellular Metamaterials
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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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".