From Nature to Engineering: Mortar Volume and Interfacial Mechanics in Bioinspired Ceramics
Bibliographic record
Abstract
Natural armors such as nacre inspire the development of bioinspired ceramics due to their exceptional toughness and damage tolerance. This study examines the influence of mortar volume fraction on the interfacial mechanics of bioinspired ceramic composites using single lap joint specimens with varying soft‐layer (mortar) volumes: low mortar volume (LMV, 3%), medium mortar volume (MMV, 6%), and high mortar volume (HMV, 9%). These fractions reflect natural nacre's mortar content and are precisely controlled during specimen fabrication. Experimental results demonstrate adhesive failure as the dominant mode, with increased mortar content enhancing maximum elongation without altering ultimate shear strength. Finite element analysis using ABAQUS and a cohesive zone model reveal mortar volume's significant effect on interfacial sliding behavior and strain energy release rate (SERR). Specifically, compared to MMV, LMV exhibits a 5% increase in interfacial stiffness and a 26% reduction in SERR, while HMV shows a 4% stiffness decrease and a 29% increase in SERR. The increased mortar content delays crack initiation and extends the plastic deformation phase, thereby enhancing energy dissipation capabilities. These findings inform the development of tougher, damage‐resistant ceramic composites for engineering by optimizing mortar volume in bioinspired ceramics.
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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".