High‐Resolution Additive Manufacturing of a Biodegradable Elastomer with A Low‐Cost LCD 3D Printer
Bibliographic record
Abstract
Artificial organs and organs-on-a-chip (OoC) are of great clinical and scientific interest and have recently been made by additive manufacturing, but depend on, and benefit from, biocompatible, biodegradable, and soft materials. Poly(octamethylene maleate (anhydride) citrate (POMaC) meets these criteria and has gained popularity, and as in principle, it can be photocured and is amenable to vat-photopolymerization (VP) 3D printing, but only low-resolution structures have been produced so far. Here, a VP-POMaC ink is introduced and 3D printing of 80 µm positive features and complex 3D structures is demonstrated using low-cost (≈US$300) liquid-crystal display (LCD) printers. The ink includes POMaC, a diluent and porogen additive to reduce viscosity within the range of VP, and a crosslinker to speed up reaction kinetics. The mechanical properties of the cured ink are tuned to match the elastic moduli of different tissues simply by varying the porogen concentration. The biocompatibility is assessed by cell culture which yielded 80% viability and the potential for tissue engineering illustrated with a 3D-printed gyroid seeded with cells. VP-POMaC and low-cost LCD printers make the additive manufacturing of high resolution, elastomeric, and biodegradable constructs widely accessible, paving the way for a myriad of applications in tissue engineering and 3D cell culture as demonstrated here, and possibly in OoC, implants, wearables, and soft robotics.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".