Structurally Efficient Three-dimensional Metamaterials with Controllable Thermal Expansion
Bibliographic record
Abstract
The coefficient of thermal expansion (CTE) of architected materials, as opposed to that of conventional solids, can be tuned to zero by intentionally altering the geometry of their structural layout. Existing material architectures, however, achieve CTE tunability only with a sacrifice in structural efficiency, i.e. a drop in both their stiffness to mass ratio and strength to mass ratio. In this work, we elucidate how to resolve the trade-off between CTE tunability and structural efficiency and present a lightweight bi-material architecture that not only is stiffer and stronger than other 3D architected materials, but also has a highly tunable CTE. Via a combination of physical experiments on 3D fabricated prototypes and numeric simulations, we demonstrate how two distinct mechanisms of thermal expansion appearing in a tetrahedron, can be exploited in an Octet lattice to generate a large range of CTE values, including negative, zero, or positive, with no loss in structural efficiency. The novelty and simplicity of the proposed design as well as the ease in fabrication, make this bi-material architecture well-suited for a wide range of applications, including satellite antennas, space optical systems, precision instruments, thermal actuators, and MEMS.
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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".