Phase Inc. and George Mason University Win NSF Grant for Automated 3D Printing of Lab-on-a-Chip Devices

Phase Inc., a North Carolina-based microfluidics manufacturing company, has been awarded a National Science Foundation STTR grant in partnership with George Mason University to develop an automated, end-to-end 3D printing system for polydimethylsiloxane (PDMS) microfluidic devices. The project combines the extracellular vesicle (EV) biology expertise of GMU professor Ramin M. Hakami with the bioengineering and materials capabilities of associate professor Remi Veneziano, building on a previously published microfluidic EV platform. Phase co-founder and principal investigator Jeff Schultz will lead the effort to integrate custom device design, scalable 3D printed PDMS chip production, and automated fluid handling into a single platform. The grant targets the manufacturing bottleneck that currently limits microfluidic device reproducibility and accessibility, using thermal and curing models to predict PDMS behavior during printing and optimize parameters before fabrication.

This award targets a persistent gap in the microfluidics value chain: the transition from manual, cleanroom-dependent fabrication to reproducible, automated production. Microfluidic devices are critical for organ-on-a-chip development, drug discovery, and disease research, and their relevance is growing as the FDA moves to reduce animal-testing requirements in favor of human-relevant models. However, complex PDMS chip production still relies on cleanrooms, manual tuning, and trial-and-error iteration, locking out smaller labs and limiting batch-to-batch consistency. Phase is not developing biological assays or therapeutic candidates; it is building the manufacturing infrastructure beneath that work. The NSF backing signals federal recognition that AM-enabled microfluidic production is a rate-limiting step for broader adoption of microphysiological systems. The project fits the recurring pattern of AM moving from prototyping into production infrastructure for specialized, high-value biomedical tools, where reproducibility and automation are the primary barriers to scale.

For Phase, the practical challenge is translating predictive models into a system that delivers consistent, cleanroom-grade PDMS devices without cleanroom overhead. The company must demonstrate that its automated platform can match or exceed the dimensional accuracy, surface quality, and biological performance of manually fabricated chips across multiple device geometries. For the broader AM industry, this project is a reminder that the most impactful applications are often invisible: not printing end-use implants or surgical guides, but enabling the tools that enable biology. The next milestone will be peer-reviewed validation data showing batch-to-batch consistency and functional EV chip performance.