Conformal Bioprinting of Bi-phasic Jammed Bioinks, Independent of Gravity, Orientation, and Curvature
Bibliographic record
Abstract
Abstract Rapid in situ bioprinting on complex, human-scale anatomical surfaces remain a key challenge for clinical translation. Here, we present a gravity-independent, conformal bioprinting strategy using bi-phasic granular bioink and multinozzle printheads capable of adapting to arbitrary surface curvatures. The bioink comprised of jammed gelatin microgels suspended in a fibrinogen matrix exhibits yield-stress behavior to maintain shape fidelity after extrusion while supporting cell viability and proliferation. Two monolithic multinozzle printhead architectures with identical bioink delivery networks were evaluated: (1) a rigid configuration for handheld bioprinting and (2) a soft robotic variant capable of real-time curvature adaptation via pneumatic actuation. Microgravity experiments aboard a parabolic flight confirmed successful bioink deposition under ∼0 g conditions. A ladder-rung microfluidic architecture ensured uniform bioink delivery across printhead nozzles, improving deposition consistency. In situ bioprinting on anatomical facial phantoms confirmed conformal, high-throughput (deposition at 20 mm·s -1 ) deposition of bioink over physiologically relevant curvatures, both with and without cells. Cell-laden constructs retained >85% cell viability post-printing and supported proliferation. This work introduces a scalable bioprinting platform suitable for clinical, remote, and deep-space environments, enabling autonomous tissue fabrication. The curvature-adaptive printhead advances current in situ bioprinting capabilities, facilitating the generation of personalized grafts with complex anatomical geometries.
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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) | 0.000 | 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".