Bespoke particle shapes in granular matter
Bibliographic record
Abstract
Among granular matter, one type of particle has special properties. Upon being assembled in disordered configurations, these particles interlock, hook, almost braid, and – surprisingly, considering their relatively low packing fractions – show exceptional shear strength.Such is the case of non-convex particles. They have been used in the shapes of tetrapods, ‘L’, ‘Z’, stars, and many others, to protect coasts or build self-standing structures requiring no binders or external supports. Although these structures are often designed without a comprehensive mechanical characterization, they have already demonstrated great potential as highly resistant construction materials. Nevertheless, it is natural to attempt to find the most appropriate non-convex shapes for any given application. Can a particle shape be tuned to obtain a desired mechanical behavior? Although this question cannot be answered yet, current technological, simulation, and experimental developments strongly suggest that it can be resolved in the next decade. A clear understanding of the relationships between particle shapes, mechanical response, and packing properties will be key to providing insights into the behavior of these materials. Such work should stand on 1) robust and general shape descriptors that encode the complexity of non-convex shapes (i.e., the number of arms, the symmetries, and asymmetries of the bodies, the presence of holes, etc.), 2) the analysis of the response of assemblies under different loading conditions, and 3) the disposition and reliability of non-convex shapes to ensure durability. The manufacturing process and an efficient use of resources are additional elements that could further help to optimize particle shape. In the quest of designing bespoke non-convex particles, this paper consolidates the challenges that remain unresolved. It also outlines some routes to explore based on the latest developments in technology and research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".