Bibliographic record
Abstract
Hydrogels have emerged as a new generation of functional materials with a broad range of applications in diverse fields. They usually cannot be reshaped or recycled, restricting their ability to respond to evolving demands and emerging applications, and contributing to the accumulation of synthetic polymer waste and associated environmental concerns. Here, a robust and general strategy is reported to engineer fully reshapeable and recyclable protein hydrogels by integrating protein folding-unfolding with reversible disulfide crosslinking. Taking advantage of the substantial stiffness contrast between protein hydrogels in their folded and unfolded protein states, protein hydrogels are reversibly reshaped across one-, two-, and three-dimensional geometries. Due to the denaturant concentration dependency and reversibility of the protein folding-unfolding process, the resulting reshaping is highly tunable, reproducible, and chemically erasable, enabling consecutive shape transformations from a single hydrogel precursor. To impart full recyclability, irreversible crosslinking chemistry is replaced with a dynamic disulfide-based one. The resulting hydrogels can be fully recycled, and the recovered proteins can be reused to form new hydrogels, allowing for complete recycling and repeated remolding of hydrogels into new shapes without compromising their mechanical properties. This approach establishes a robust platform for developing next-generation protein-based materials with dynamic formability and true material circularity.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".