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Journal articles

January 2025

Archerfish: a retrofitted 3D printer for high-throughput combinatorial experimentation via continuous printing

Siemenn, Alexander E.; Das, Basita; Aissi, Eunice; Sheng, Fang; Elliott, Lleyton; Hudspeth, Blake; Meyers, Marilyn; Serdy, James; Buonassisi, Tonio

Abstract

Archerfish is a low-cost, high-throughput tool for combinatorial materials research. Retrofitted with in situ mixing, Archerfish prints 250 unique compositions per min—a 100× acceleration factor—for aqueous, nanoparticle, and crystalline materials.
The maturation of 3D printing technology has enabled low-cost, rapid prototyping capabilities for mainstreaming accelerated product design. The materials research community has recognized this need, but no universally accepted rapid prototyping technique currently exists for material design. Toward this end, we develop Archerfish, a 3D printer retrofitted to dispense liquid with in situ mixing capabilities for performing high-throughput combinatorial printing (HTCP) of material compositions. Using this HTCP design, we demonstrate continuous printing throughputs of up to 250 unique compositions per minute, 100× faster than similar tools such as Opentrons that utilize stepwise printing with ex situ mixing. We validate the formation of these combinatorial “prototype” material gradients using hyperspectral image analysis and energy-dispersive X-ray spectroscopy. Furthermore, we describe hardware challenges to realizing reproducible, accurate, and precise composition gradients with continuous printing, including those related to precursor dispensing, mixing, and deposition. Despite these limitations, the continuous printing and low-cost design of Archerfish demonstrate promising accelerated materials screening results across a range of materials systems from nanoparticles to perovskites.

Acknowledgements

The authors acknowledge Shijing Sun and Liu Zhe for their discussions and input during the early development of Archerfish. A. E. S. acknowledges Nadya Peek, Lilo Pozzo, and the other participants of the “Pathways to Open-Source Hardware for Laboratory Automation” workshop, as this paper would not exist without their fruitful discussions on open hardware. The authors acknowledge Emre Tekoglu, Armi Tiihonen, Elena Botica Artalejo, and Hamide Kavak for their assistance in preparing precursors and substrates for printing. The authors acknowledge funding support from: the Defense Advanced Research Projects Agency (DARPA) under contract no. HR001118C0036; German Academic Exchange Services (DAAD); First Solar; TotalEnergies; Eni S.p.A. through the MIT Energy Initiative; University of Toronto's Acceleration Consortium; and U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technology Office (SETO) Award Number DE-EE0010503. This work made use of the MRSEC Shared Experimental Facilities at MIT, supported by the National Science Foundation under award number DMR-1419807.

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