High‐Resolution Heterogeneous Hydrogel Printing Using a Home Projector
Bibliographic record
Abstract
Soft hydrogels are being increasingly recognized for their versatility and unique properties, making them attractive for a range of applications in tissue engineering, biomedical devices, and beyond. Among fabrication methods, 3D printing stands out for its precise control over material distribution, enabling the creation of complex structures. Traditional printing methods, however, struggle to produce heterogeneous hydrogels with diverse properties. Here, a novel approach is introduced utilizing polyvinyl alcohol bearing styrylpyridinium groups (PVA-SbQ) for high-resolution heterogeneous hydrogel printing. By leveraging the photoreactive nature of PVA-SbQ, precise control over crosslinking time at different positions within a PVA-SbQ hydrogel is demonstrated using a simple home projector. This enables the creation of intricate patterns with tailored properties within a heterogeneous hydrogel, showcasing synergistic combinations of soft and tough domains, as well as high and low swelling regions. The method not only advances the field of hydrogel printing but also holds promise for applications in pattern encryption, 4D printing, cell organization, and cell alignment. By overcoming the limitations of traditional printing techniques, the approach opens new avenues for the fabrication of complex and heterogeneous hydrogel structures with diverse applications in biomedical engineering and beyond.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".