George Mason University and North Carolina company Phase Inc. have been awarded a National Science Foundation STTR grant to develop a new class of 3D printed microfluidic devices. The goal is to carry the technology out of the research lab and into wider use, yielding a more dependable route to the tools that organ-on-a-chip development and human-centered biomedical research increasingly depend on.
The collaboration merges the extracellular vesicle (EV) biology work of College of Science professor Ramin M. Hakami’s group with the bioengineering and materials expertise of College of Engineering and Computing associate professor Remi Veneziano’s group, building on a microfluidic EV platform the two teams previously developed and published together. Phase brings its ambition to build a fully automated, end-to-end system spanning custom device design, scalable 3D printed polydimethylsiloxane (PDMS) chip production and automated fluid handling.
Why Microfluidics Matter Now
Microfluidic devices route tiny volumes of fluid through miniature channels to recreate biological conditions at the cellular scale, offering a more realistic model of human biology than conventional flat cell cultures. That makes them valuable across drug discovery, disease research and toxicology, and increasingly relevant as the FDA moves to phase out certain animal-testing requirements in favor of more human-relevant methods.
The catch is manufacturing: producing complex PDMS devices today typically demands cleanrooms, manual tuning and repeated trial-and-error. The NSF-backed effort aims to break that bottleneck using thermal and curing models that predict how PDMS behaves during printing, allowing print parameters to be optimized before a device is ever made.
“This partnership helps us move one step closer to a fully automated, scalable microfluidic platform,” said Jeff Schultz, principal investigator and co-founder of Phase. “Our goal is to make microfluidic technology more reproducible and more accessible to researchers and companies working to develop better human-relevant models.”
Testing Devices for Accuracy and Biological Function
George Mason’s researchers will evaluate the printed devices for dimensional accuracy, surface quality, batch-to-batch consistency and biological performance, including testing a chip designed to study EV function. EVs are cell-released nanoparticles central to communication between cells, carry regulatory roles in diseases ranging from cancer to infectious and neurological disorders, and hold strong potential for drug delivery.
“Being able to rapidly and cost‑effectively prototype and fabricate custom microfluidic devices will significantly enhance our capacity to design relevant microphysiological systems and will help broaden access to this technology to many research laboratories” said Veneziano.
Industrializing How Microfluidic Chips Are Made
Phase isn’t testing drugs or growing tissue, it’s building the manufacturing layer beneath that work. Complex PDMS chips still depend on cleanrooms and hands-on tuning, which locks out small labs and makes results hard to reproduce. Phase aims to turn chip fabrication into an automated, repeatable process.
Phase isn’t alone in chasing that production layer. Recently, additive manufacturing firm Intrepid Automation partnered with Rapid Fluidics to scale U.S.-based microfluidic production, targeting the same bottleneck between lab prototypes and high-volume, regulatory-compliant output, with early results reportedly cutting production time from six weeks to minutes.
The other front is the printing process itself. Missouri University of Science and Technology developed a faster, light-based method for producing organs-on-a-chip, using a self-assembling resin to form intricate microchannels in one pass, an attempt, like Phase’s, to make chip fabrication faster, cleaner and easier to scale.
Few players are attacking microfluidic manufacturing itself; most work happens downstream, testing on finished chips. Phase is betting that predictive, software-driven fabrication is what pulls the field out of the cleanroom.
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Featured image shows a microfluid device. Photo via George Mason University.
Facts Only
* George Mason University and North Carolina company Phase Inc. received an NSF STTR grant.
* The grant supports the development of a new class of 3D printed microfluidic devices.
* The goal is to move the technology into wider use for organ-on-a-chip development and human-centered biomedical research.
* The collaboration merges EV biology work from Ramin M. Hakami’s group with bioengineering/materials expertise from Remi Veneziano’s group.
* Phase intends to build a fully automated, end-to-end system for custom design, scalable 3D printed PDMS chip production, and automated fluid handling.
* Microfluidic devices route small fluid volumes through miniature channels to recreate biological conditions at the cellular scale.
* Microfluidics are valuable across drug discovery, disease research, and toxicology.
* The effort uses thermal and curing models to predict PDMS behavior during printing to optimize parameters before manufacturing.
* Researchers will test printed devices for dimensional accuracy, surface quality, batch-to-batch consistency, and biological performance, including EV function testing.
* Phase aims to automate the fabrication of complex PDMS chips.
* Other entities, such as Intrepid Automation and Rapid Fluidics, have partnered to scale microfluidic production.
* Missouri University of Science and Technology developed a faster, light-based method for organs-on-a-chip using self-assembling resin.
Executive Summary
George Mason University and North Carolina company Phase Inc. received a National Science Foundation STTR grant to develop a new class of 3D printed microfluidic devices. The objective is to transition this technology from research labs to wider application, providing a more reliable route for tools used in organ-on-a-chip development and human-centered biomedical research. This collaboration integrates extracellular vesicle (EV) biology research from Ramin M. Hakami's group with bioengineering and materials expertise from Remi Veneziano's group, building upon prior work on a microfluidic EV platform. Phase aims to create a fully automated, end-to-end system encompassing custom device design, scalable 3D printed polydimethylsiloxane (PDMS) chip production, and automated fluid handling.
Microfluidics enable the creation of realistic biological models by routing small fluid volumes through channels at the cellular scale, offering alternatives to conventional flat cell cultures in drug discovery, disease research, and toxicology, particularly as regulatory bodies shift toward human-relevant testing methods. A significant challenge currently lies in manufacturing these complex PDMS devices, which typically requires manual tuning in cleanrooms. The NSF-backed effort seeks to overcome this bottleneck by using thermal and curing models to predict PDMS behavior during printing, allowing optimization before fabrication. Researchers will evaluate printed devices for dimensional accuracy, surface quality, consistency, and biological performance, including testing EV function. Furthermore, the work focuses on building the manufacturing layer itself, aiming to automate chip fabrication.
Full Take
The narrative positions the key challenge not in the biological science itself, but in the industrialization bottleneck—the gap between laboratory prototyping and scalable, reproducible manufacturing of complex microfluidic chips. The push to integrate predictive modeling into 3D printing addresses this gap by attempting to shift complexity from manual post-processing (cleanrooms, trial-and-error) to automated, software-driven fabrication. This suggests an underlying pattern where scientific advancement is frequently stalled by infrastructural constraints, and the solution lies in abstracting physical processes into controllable digital models.
The context of integrating EV biology with advanced fabrication highlights a systemic need for cross-disciplinary platforms. The focus shifts from simply creating functional devices to establishing an entire, scalable ecosystem. The mention of other entities achieving similar goals—like Intrepid Automation and Missouri S&T’s resin method—suggests that the path forward is not purely academic but relies on industrial alignment. This creates a dynamic where foundational research (EVs) must be coupled with engineering scalability to achieve real-world impact, otherwise, the technology remains confined to niche, slow research settings rather than influencing broader clinical or discovery pipelines.
The pattern observed is a tension between high-fidelity biological realism and manufacturing accessibility. The potential implication is that if predictive software can successfully manage the inherent variability of additive manufacturing—moving production from bespoke artisanal work to automated mass production—it could dramatically democratize access to human-relevant modeling, potentially accelerating regulatory acceptance for these systems by providing consistent, reproducible data streams. The question remains whether the proposed technological leap is sufficiently integrated across all necessary disciplines to fully resolve the transition from niche innovation to widespread industrial application.
Sentinel — Human
This analysis appears to be well-researched reporting on a specific scientific/industrial collaboration, exhibiting the structure and detail typical of human journalistic synthesis.