Bioprinting · Extrusion · 2022 · Scientific Reports
Development of a high-performance open-source 3D bioprinter
The application of 3D printing to biological research has provided the tissue engineering community with a method for organizing cells and biological materials into complex 3D structures. While many commercial bioprinting platforms exist, they are expensive, ranging from $5000 to over $1,000,000.
Performance the paper reports
1 of 5- Motion8.7 µmMotion accuracy: reported
- Volume—Volumetric accuracy: not reported
- Viability—Cell viability: not reported
- Unattended—Unattended operation: not reported
- Standard—Standard compliance: not reported
- Motion / positional accuracy8.7 µm
For the Z-axis, the unidirectional repeatability was 8.7 µm, and the bidirectional repeatability was 38.7 µm.
How hard this is to adopt
3.6 of 5, higher is easierEach score is shown against the range the other 59 builds actually occupy, because no criterion uses the full 1–5 scale. These describe how reproducible the build is, not how good the science is.
Resolution
3/1–5Quantitative handling accuracy reported, but minimum volume unclear.
Throughput
3/2–5Throughput not explicit; assigned moderate default.
Parts sourcing
3/2–4Mix of common parts and custom/printed components.
Skill needed
4/2–4Build/operation described as low-skill or basic assembly.
Facilities needed
4/2–4Uses common benchtop/desktop equipment or generic consumables.
Documentation
4/2–5Open resources include several build or operation artifacts.
Validation effort
4/2–4Validation includes standards, benchmarking, replicates, or multi-condition tests.
Cycle time
4/1–4Speed evidence suggests rapid, real-time, or automated operation.
2 further criteria are recorded but not shown
- Application Level — scored 2 (75% of records share one value), so it cannot separate one build from another.
- Build Time (scored for only 6 of 60 records), so it cannot separate one build from another.
Both remain in the published dataset.
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Record metadata
Publication data
Matched by doi · retrieved 2026-07-21
Abstract
The application of 3D printing to biological research has provided the tissue engineering community with a method for organizing cells and biological materials into complex 3D structures. While many commercial bioprinting platforms exist, they are expensive, ranging from $5000 to over $1,000,000. This high cost of entry prevents many labs from incorporating 3D bioprinting into their research. Due to the open-source nature of desktop plastic 3D printers, an alternative option has been to convert low-cost plastic printers into bioprinters. Several open-source modifications have been described, but there remains a need for a user-friendly, step-by-step guide for converting a thermoplastic printer into a bioprinter using components with validated performance. Here we convert a low-cost 3D printer, the FlashForge Finder, into a bioprinter using our Replistruder 4 syringe pump and the Duet3D Duet 2 WiFi for total cost of less than $900. We demonstrate that the accuracy of the bioprinter's travel is better than 35 µm in all three axes and quantify fidelity by printing square lattice collagen scaffolds with average errors less than 2%. We also show high fidelity reproduction of clinical-imaging data by printing a scaffold of a human ear using collagen bioink. Finally, to maximize accessibility and customizability, all components we have designed for the bioprinter conversion are provided as open-source 3D models, along with instructions for further modifying the bioprinter for additional use cases, resulting in a comprehensive guide for the bioprinting field.
Bibliographic details
Access and metrics
Topics and keywords
Funding
- National Institutes of Health · F30HL154728
- National Institutes of Health · K99HL155777
- U.S. Food and Drug Administration · R01FD006582
Perspective and practical signals
Why it matters
Canonical ‘Feinberg lab’ open-source bioprinter paper; directly in-scope for new open tools.
Limitations
no cell viability or functional validation, how reproducible is the building (e.g. build time, failure rate, user variability)