Microfluidics‐Enabled Multimaterial Maskless Stereolithographic Bioprinting
Bibliographic record
Abstract
A stereolithography-based bioprinting platform for multimaterial fabrication of heterogeneous hydrogel constructs is presented. Dynamic patterning by a digital micromirror device, synchronized by a moving stage and a microfluidic device containing four on/off pneumatic valves, is used to create 3D constructs. The novel microfluidic device is capable of fast switching between different (cell-loaded) hydrogel bioinks, to achieve layer-by-layer multimaterial bioprinting. Compared to conventional stereolithography-based bioprinters, the system provides the unique advantage of multimaterial fabrication capability at high spatial resolution. To demonstrate the multimaterial capacity of this system, a variety of hydrogel constructs are generated, including those based on poly(ethylene glycol) diacrylate (PEGDA) and gelatin methacryloyl (GelMA). The biocompatibility of this system is validated by introducing cell-laden GelMA into the microfluidic device and fabricating cellularized constructs. A pattern of a PEGDA frame and three different concentrations of GelMA, loaded with vascular endothelial growth factor, are further assessed for its neovascularization potential in a rat model. The proposed system provides a robust platform for bioprinting of high-fidelity multimaterial microstructures on demand for applications in tissue engineering, regenerative medicine, and biosensing, which are otherwise not readily achievable at high speed with conventional stereolithographic biofabrication platforms.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".