Microscale geometrical features in the plant leaf epidermis confer enhanced resistance to mechanical failure
Bibliographic record
Abstract
Abstract In many plant species, epidermal tissues of leaves and petals feature irregular wavy cell geometries forming jigsaw puzzle patterns. At the origin of plant tissues are simple polyhedral progenitor cells that divide and grow into a kaleidoscopic array of morphologies that underpin plant organ functionality. The wide prevalence and great diversity of the wavy cell shape in the plant kingdom point to the significance of this trait and its tunability by environmental pressures. Despite multiple attempts to explain the advent of this complex cell geometry by evolutionary relevant functionality, our understanding of this peculiar tissue patterning preserved through evolution remains lacking. Here, by combining microscopic and macroscopic fracture experiments with computational fracture mechanics, we show that wavy epidermal cells toughen the plants’ protective skin. Based on a multi-scale approach, we demonstrate that, biological and synthetic materials alike can be toughened through an energy-efficient patterning process. Our data reveal a ubiquitous and tunable structural-mechanical mechanism employed in the macro-scale design of plants to protect them from the detrimental effects of surface fissures and to enable and guide the direction of beneficial fractures. We expect these data to inform selective plant breeding for traits enhancing plant survival under changing environmental conditions. From a materials engineering perspective, this work exemplifies that plants hold sophisticated design principles to inspire human-made materials.
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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 | 0.001 | 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".