Bioinspired Hierarchical Ceramic Sutures for Multi‐Modal Performance
Bibliographic record
Abstract
Abstract Natural suture structures, characterized by hard segments joined along a patterned weak interface, are found to provide unique toughening mechanisms for brittle bulk biological materials. Hierarchical ceramic sutures inspired by white‐tailed deer crania and diabolical ironclad beetle exoskeletons are developed using a biomimetic approach. Overlapping geometries unlock twin energy absorption mechanisms. Ceramics with Surlyn‐infiltrated precision laser‐cuts are fabricated using a semi‐automated and smart advanced manufacturing platform. A parametric study comprising four‐point bending, fracture toughness, and tensile tests is conducted to evaluate toughness, strength, and stiffness with geometrical interlocking in two hierarchical orders. Digital image correlation is utilized to analyze the local toughening mechanisms and failure modes in the fracture tests. For all three metrics, the panels with second‐order hierarchy outperform the anti‐trapezoidal equivalents. The ceramic sutures show up to 590%, 340%, and 700% improvements in energy absorption in the tensile, bending, and fracture tests, respectively, owing to the optimal first‐ and second‐order interlocking angles. The high‐order fractal interlocking at multiple scales and overlapping teeth are found to provide high flexibility and failure resistance, whereas progressive fracture mechanisms delay catastrophic failure by up to 50%. The concept of hierarchical suture can lead to industrially applied ceramic systems with tailored mechanical performances.
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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".