The hierarchical structure of seashells optimized to resist mechanical threats
Bibliographic record
Abstract
The vast majority of mollusks grow a hard shell for protection. Typical seashells are composed of two distinct layers, with an outer layer made of calcite, which is a hard but brittle material, and an inner layer made of a tough and ductile material called nacre. Nacre is a biocomposite material that consists of more than 95% of tablet-shaped aragonite, CaCO 3, and a soft organic material as the matrix. Although the brittle ceramic aragonite constitutes a high volume fraction of nacre, its mechanical properties are found to be surprisingly higher than those of its constituents. Calcite and nacre, two materials with distinct structures and properties, are believed to be arranged in an optimal fashion to defeat attacks from predators. This paper aims at capturing the design rules of a gastropod seashell by using multiscale modeling and optimization techniques. A two-layer finite element model of the seashell was developed to include shell geometry at the macroscale, whereas nacre material properties were modeled at the microscale. A representative volume element of the microstructure of nacre was used to formulate a closed-form expression of the elastic modulus of nacre, and a multiaxial failure criterion as a function of the key dimensions of the microstructure. Using the seashell model, the maximum load that the shell can carry at its apex was obtained and different failure modes were introduced. The results from optimization suggested that the natural seashell is optimally designed for resisting penetrations. Furthermore, experiments were performed on an actual shell of abalone to validate the results obtained from simulations and gain insight into the way that the shell fails under sharp penetration. Optimization and experimental results revealed that the shell shows its best performance when two modes of failure coincide within the structure.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".