Highly stretchable hydrogels for UV curing based high-resolution multimaterial 3D printing
Bibliographic record
Abstract
We report a method to prepare highly stretchable and UV curable hydrogels for high resolution DLP based 3D printing. Hydrogel solutions were prepared by mixing self-developed high-efficiency water-soluble TPO nanoparticles as the photoinitiator with an acrylamide-PEGDA (AP) based hydrogel precursor. The TPO nanoparticles make AP hydrogels UV curable, and thus compatible with the DLP based 3D printing technology for the fabrication of complex hydrogel 3D structures with high-resolution and high-fidelity (up to 7 μm). The AP hydrogel system ensures high stretchability, and the printed hydrogel sample can be stretched by more than 1300%, which is the most stretchable 3D printed hydrogel. The printed stretchable hydrogels show an excellent biocompatibility, which allows us to directly 3D print biostructures and tissues. The great optical clarity of the AP hydrogels offers the possibility of 3D printing contact lenses. More importantly, the AP hydrogels are capable of forming strong interfacial bonding with commercial 3D printing elastomers, which allows us to directly 3D print hydrogel-elastomer hybrid structures such as a flexible electronic board with a conductive hydrogel circuit printed on an elastomer matrix.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".