Staging the Self-Assembly Process: Inspiration from Biological Development
Bibliographic record
Abstract
One of the practical challenges facing the creation of self-assembling systems is being able to exploit a limited set of fixed components and their bonding mechanisms. The method of staging divides the self-assembly process into time intervals, during which components can be added to, or removed from, an environment at each interval. Staging addresses the challenge of using components that lack plasticity by encoding the construction of a target structure in the staging algorithm itself and not exclusively in the design of the components. Previous staging strategies do not consider the interplay between component physical features (morphological information). In this work we use morphological information to stage the self-assembly process, during which components can only be added to their environment at each time interval, to demonstrate our concept. Four experiments are presented, which use heterogeneous, passive, mechanical components that are fabricated using 3D printing. Two orbital shaking environments are used to provide energy to the components and to investigate the role of morphological information with component movement in either two or three spatial dimensions. The benefit of our staging strategy is shown by reducing assembly errors and exploiting bonding mechanisms with rotational properties. As well, a doglike target structure is used to demonstrate in theory how component information used at an earlier time interval can be reused at a later time interval, inspired by the use of a body plan in biological development. We propose that a staged body plan is one method toward scaling self-assembling systems with many interacting components. The experiments and body plan example demonstrate, as proof of concept, that staging enables the self-assembly of more complex morphologies not otherwise possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".