Messy or Ordered? Multiscale Mechanics Dictates Shape‐Morphing of 2D Networks Hierarchically Assembled of Responsive Microfibers
Bibliographic record
Abstract
Abstract Shape‐morphing active networks of mesoscale filaments are a common hierarchical feature in biology for applying forces, transporting materials, and inducing motility with microscale resolution. Synthetic morphing systems of similar dimensions and capabilities hold potential for a range of technological applications, from micro‐muscles to shape‐morphing optical devices. Here, the fabrication of highly‐ordered 2D networks hierarchically constructed of thermoresponsive mesoscale polymeric fibers, which can exhibit morphing with microscale resolution, is presented. It is demonstrated both experimentally and computationally that the morphing of such networks strongly depends on the physical attributes of the single fiber, in particular on two intrinsic length scales—the fiber diameter and mesh size, which stems from network's density. It is shown that depending on these parameters, such fiber‐networks exhibit one of two thermally driven morphing behaviors: i) the fibers stay straight, and the network preserves its ordered morphology, exhibiting a bulk‐like behavior; or ii) the fibers buckle and the network becomes messy and highly disordered. Notably, in both cases, the networks display memory and regain their original ordered morphology upon shrinking. This hierarchically induced phase transition, demonstrated here on a range of networks, offers a new way of controlling the shape‐morphing of synthetic materials with mesoscale resolutions.
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 | 0.000 |
| 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".