Combining Simple Deformations to Elicit Complex Motions and Directed Swimming of Smart Organic Crystals with Controllable Thickness
Bibliographic record
Abstract
Abstract The lack of control over the crystal growth in a systematic way currently stands as an unsurmountable impediment to the preparation of dynamic crystals as soft robots; in effect, the mechanical effects of molecular crystals have become a subject of scattered reports that pertain only to specific crystal sizes and actuation conditions, often without the ability to establish or confirm systematic trends. One of the factors that prevents the verification of such performance is the unavailability of strategies for effectively controlling crystal size and aspect ratio, where crystals of serendipitous size are harvested from crystallization solution. Here we devised a water‐assisted precipitation method to prepare crystals of chemical variants of 9‐anthracene derivatives with various thicknesses that respond to ultraviolet light with simple mechanical effects, including bending, splintering, and rotation. By capitalizing on the robust mechanical flexibility and deformability of crystals, we demonstrate systematic variations in crystal deformation that are further elevated in complexity to construct crystal‐based robots capable of controllable motions reminiscent of sailing and humanoid movements. The results illustrate an approach to eliminate one of the critical obstacles towards complete control over the motility of dynamic molecular crystals as microrobots in non‐aerial environments.
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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".