Engineering shape memory and morphing protein hydrogels based on protein unfolding and folding
Bibliographic record
Abstract
Abstract Engineering shape memory/morphing materials have achieved considerable progress in polymer-based systems with broad potential applications. However, engineering protein-based shape memory/morphing materials remains challenging and under-explored. Here we report the design of a bilayer protein-based shape memory/morphing hydrogel based on protein folding-unfolding mechanism. We fabricate the protein-bilayer structure using two tandem modular elastomeric proteins (GB1)8and (FL)8. Both protein layers display distinct denaturant-dependent swelling profiles and Young’s moduli. Due to such protein unfolding-folding induced changes in swelling, the bilayer hydrogels display highly tunable and reversible bidirectional bending deformation depending upon the denaturant concentration and layer geometry. Based on these programmable and reversible bending behaviors, we further utilize the protein-bilayer structure as hinge to realize one-dimensional to two-dimensional and two-dimensional to three-dimensional folding transformations of patterned hydrogels. The present work will offer new inspirations for the design and fabrication of novel shape morphing materials.
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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) | 0.000 | 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".