Perforated shellular metamaterials with programmablemultifunctionalities
Bibliographic record
Abstract
Shellulars are comprised of a periodic 3D unit cell of continuously smooth and non-self-intersecting shells. Shellulars offer less sensitivity to stress concentration and architectural defects than other cellular solids, and therefore are promising candidates for realizing ultralight architected materials with enhanced stiffness, strength, and resilience. They are routinely developed based on triply periodic minimal surfaces (TPMS) with zero mean curvature. The pre-fabricated topological features of TMPS hold great promise for creating shell-like metamaterials with unparalleled multifunctional properties for applications in catalytic converters, heat exchanger, and microbatteries. The geometry of shell surfaces can be also tailored in the post-fabrication state by harnessing structural instability. In this study, we present a series of novel design routes for 3D printing of programmable and previously inaccessible deployable multistable shellular metamaterials by introducing delicate perforations on the surface of shellulars. Two perforation design strategies are introduced and the mechanical properties and structural stability characteristics of the perforated shellular metamaterials are analyzed by simplified theoretical mechanics models, finite element simulations, and mechanical testing on SLS 3D printed samples. The developed perforated shellulars demonstrate controllable rigidity, enhanced energy dissipation, a plethora of stable configurations, and even thermoacoustic properties.
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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".