Mechanical characterization of an origami-inspired super deformable metamaterial with high tunability for tissue engineering
Bibliographic record
Abstract
• A novel origami-inspired super-compressible metamaterial (OISCM) for tissue engineering was introduced. • OISCM shows unique recoverable strain tolerance in quasi static and cyclic tests under large deformations. • Baysian machine learning was implemented to create predictive models of structural and mechanical properties of OISCM. • Wide range of elastic modulus and porosity was achieved by adjusting key input design parameters of OISCM. Origami-inspired metamaterials have gained significant attention for their ability to mimic the complex mechanical behavior of biological tissues and their potential applications in advanced surgical treatments. Inspired by Kresling origami, we introduced a metamaterial capable of large recoverable deformations. A parametric design explored the effects of changing geometrical parameters on the mechanical properties of the metamaterial. Eighteen designs were fabricated and mechanically tested for practicability assessment and validation purposes. Non-linear finite element method was leveraged to test the entire design space of the metamaterial. Using Bayesian machine learning, the sensitivity of surface to volume ratio, porosity, elastic modulus, strain energy density, and maximum local strain to the design inputs was assessed and their corresponding predictive models were created. The fabricated designs could withstand 80 % and up to 70 % recoverable strain in quasi static and cyclic loading, respectively, while exhibiting a wide range of structural and mechanical properties. From predictive models, elastic modulus of 0.1 Pa to 1.8 KPa was attainable, while having porosities from 49.7 % to 99.9 %. This study demonstrated the feasibility of the design and manufacturing of an origami-inspired super deformable metamaterial with highly-tunable structural and mechanical properties, which can be used for various tissue engineering applications.
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How this classification was reachedexpand
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) | 0.001 | 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) | 0.000 | 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 teacher head, 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".