Customizable Multidimensional Self-Wrinkling Structure Constructed via Modulus Gradient in Chitosan Hydrogels
Bibliographic record
Abstract
Customizable patterning and deformation of soft matter represents a powerful tool to achieve programmable 3D configurations of soft materials. However, customizing the multidimensional self-wrinkling hydrogels for specific configuration on demand remains a challenge. This work introduces a facile, effective approach to construct self-wrinkling hydrogels with customizable geometric dimension and well-aligned wrinkle structure. By prestretching chemically cross-linked chitosan elastic hydrogel in water for a short period of time (1 min), the chitosan chains and bundles were further physically cross-linked to form aggregates, quickly creating a closely packed nanofiber layer as a shell on the hydrogel surface. The significant modulus gradient between the relatively stiff shell and the inner elastic networks of the chemically cross-linked hydrogel drives the formation of the wrinkling surface topography. This has allowed construction of 1D fiber, 2D plane, 3D tubular, and 3D scaffold self-wrinkling hydrogels with well-organized microgroove-like structure and controllable size. Moreover, the self-wrinkling hydrogel can act as an excellent matrix for fabricating multifunctional devices with customizable geometry by integrating different functional components, highlighting the possibility for constructing soft material structures to create novel biomedical and engineering devices from natural polymers.
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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".