High‐Resolution 3D Printing of Stretchable Granular Hydrogel Filaments for Fabricating Robust and Durable Tissue Phantoms with Tunable Mechanical Strength
Bibliographic record
Abstract
Abstract Granular hydrogels are a promising class of 3D‐printable inks but often suffer from low printing resolution due to large microgel sizes (>100 µm) and weak mechanical performance from lower packing density. To overcome these limitations, a novel whey protein microgels‐based granular hydrogel (WMGH) is developed, consisting of uniform, size‐controllable microgels (1, 6, and 20 µm) via protein‐polysaccharide segregative phase separation. The smaller microgels enable WMGH to stretch like continuous liquid inks by adjusting printing speed and pressure, achieving high‐resolution 3D‐printing (200 µm) with minimal ink spreading (≈5%) using a 25G nozzle (260 µm). This allows the fabrication of intricate structures like human ear and aortic valve models. Incorporating a polyacrylamide (PAM) second percolating network transforms WMGH inks into double‐network hydrogels (DN‐WMGH), showing up to 36 fold increase in toughness (1.45 MJ m−3) compared to PAM hydrogels. Controlling microgel size provides a new approach for tailoring mechanical strength (6–300 kPa) while maintaining durability, exhibiting full recovery after 100 tensile cycles at 100% strain. DN‐WMGH from biopolymers demonstrated good compatibility. This high‐resolution 3D‐printing of robust DN‐WMGH replicates the mechanical properties of various tissues, from brain (<10 kPa) to intestine (≈300 kPa), demonstrating new possibilities for tissue‐mimicking applications in surgical training, implantable devices, and drug‐delivery systems.
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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".