Bibliographic record
Abstract
Billions of years of evolution have produced extremely efficient natural materials, which are increasingly becoming a source of inspiration for engineers. Biomimetics-the science of imitating nature-is a growing multidisciplinary field which is now leading to the fabrication of novel materials with remarkable mechanical properties. This article discusses the mechanics of hard biological materials, and more specifically of nacre and bone. These high-performance natural composites are made up of relatively weak components (brittle minerals and soft proteins) arranged in intricate ways to achieve specific combinations of stiffness, strength and toughness (resistance to cracking). Determining which features control the performance of these materials is the first step in biomimetics. These 'key features' can then be implemented into artificial bio-inspired synthetic materials, using innovative techniques such as layer-by-layer assembly or ice-templated crystallization. The most promising approaches, however, are self-assembly and biomineralization because they will enable tight control of structures at the nanoscale. In this 'bottom-up' fabrication, also inspired from nature, molecular structures and crystals are assembled with a little or no external intervention. The resulting materials will offer new combinations of low weight, stiffness and toughness, with added functionalities such as self-healing. Only tight collaborations between engineers, chemists, materials scientists and biologists will make these 'next-generation' materials a reality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".