Room temperature shape self-adjustable tough hydrogel based on multi-physical crosslinking
Bibliographic record
Abstract
Room temperature shape adjustable hydrogels (rtSAH) can be (re)processed into different stable shapes at ambient conditions, making them appealing for various applications. However, elastomer-like and mechanically tough rtSAH is hard to obtain because of the elastic recovery force that prevents a deformed new shape from being fixed. Herein, we demonstrate a supramolecular elastic rtSAH having high tensile strength (2.6 MPa) and large strain at break (1770 %), whose network structure is designed to rely on a combination of strong and weak non-covalent interactions through three types of physical crosslinking: host–guest interaction, hydrogen bonding and coordination interaction. When deformed at room temperature and held in that state for sufficient time, the hydrogel adapts to a stable new shape while remaining elastic. The reconstruction of the dynamic Fe 3+ -carboxylate coordination interaction in the deformed hydrogel is the key to self-locking the new shape with stored elastic energy and enabling the shape memory function. Exposure the rtSAH to UV light or an acid solution disrupts the coordination crosslinking and allows the hydrogel to recover its initial shape. The demonstrated structural design represents an effective strategy to develop all-physically crosslinked shape memory hydrogels whose shapes can be reprocessed and self-locked at room-temperature.
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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".