Handheld skin printer: <i>in situ</i> formation of planar biomaterials and tissues
Bibliographic record
Abstract
We present a handheld skin printer that enables the in situ formation of biomaterial and skin tissue sheets of different homogeneous and architected compositions. When manually positioned above a target surface, the compact instrument (weight <0.8 kg) conformally deposits a biomaterial or tissue sheet from a microfluidic cartridge. Consistent sheet formation is achieved by coordinating the flow rates at which bioink and cross-linker solution are delivered, with the speed at which a pair of rollers actively translate the cartridge along the surface. We demonstrate compatibility with dermal and epidermal cells embedded in ionically cross-linkable biomaterials (e.g., alginate), and enzymatically cross-linkable proteins (e.g., fibrin), as well as their mixtures with collagen type I and hyaluronic acid. Upon rapid crosslinking, biomaterial and skin cell-laden sheets of consistent thickness, width and composition were obtained. Sheets deposited onto horizontal, agarose-coated surfaces were used for physical and in vitro characterization. Proof-of-principle demonstrations for the in situ formation of biomaterial sheets in murine and porcine excisional wound models illustrate the capacity of depositing onto inclined and compliant wound surfaces that are subject to respiratory motion. We expect the presented work will enable the in situ delivery of a wide range of different cells, biomaterials, and tissue adhesives, as well as the in situ fabrication of spatially organized biomaterials, tissues, and biohybrid structures.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".