Flow-Driven Assembly of Microcapsules into Three-Dimensional Towers
Bibliographic record
Abstract
By harnessing biochemical signaling and chemotaxis, unicellular slime molds can aggregate on a surface to form a long, vertical stalk. Few synthetic systems can self-organize into analogous structures that emerge out of the plane. Through computational modeling, we devise a mechanism for assembling tower-like structures using microcapsules in solution as building blocks. In the simulations, chemicals diffusing from a central patch on a surface produce a concentration gradient, which generates a radially directed diffusioosmotic flow along the surface toward the center. This toroidal roll of a fluid pulls the microcapsules along the surface and lifts them above the patch. As more capsules are drawn toward the patch, some units are pushed off the surface but remain attached to the central microcapsule cluster. The upward-directed flow then draws out the cluster into a tower-like shape. The final three-dimensional (3D) structure depends on the flow field, the attractive capsule-capsule and capsule-surface interaction strengths, and the sedimentation force on the capsules. By tuning these factors, we can change the height of the structures that are produced. Moreover, by patterning the areas of the wall that are attractive to the capsules, we can form multiple vertical strands instead of a single tower. Our approach for flow-directed assembly can permit the growth of reconfigurable, 3D structures from simple subunits.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".