Toughening of thin ceramic plates using bioinspired surface patterns
Bibliographic record
Abstract
High-performance natural materials such as bone, teeth or mollusk shells contain a large volume fraction of minerals to generate stiffness and hardness. They are also packed with weak interfaces, which generate nonlinear deformations and channel cracks into powerful toughening configurations. As a result, these natural materials achieve simultaneous stiffness, hardness and toughness, which are properties which are mutually exclusive in engineering materials. Following these concepts, we have engraved trenches with controlled patterns and depth in thin plates of aluminum oxide. The trenches can guide propagating cracks, which we use to implement toughening mechanisms and unusual deformation mechanisms. We present fracture results on samples with transverse interfaces and sinusoidal interfaces. We also explore interlocking-jigsaw like interfaces, which dissipate the most energy and produce the highest toughness. These interfaces also profoundly change the way the material deform in tension, by introducing controlled non-linear deformations accompanied with geometric hardening and frictional pullout, in an otherwise all-brittle material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".