Designing and Characterizing a 3D Printed Staggered Herringbone Mixer

Three-dimensional (3D) printing has emerged as a faster and cheaper alternative for conventional lithographic mold fabrication for microfluidic devices. However, 3D printers are limited in the resolution of reproduced features for conventional devices which can impact performance. Here, we study these limitations for the case of microfluidic mixers. We observe that resolution limitations are exacerbated by systematic error in printed dimensions near the lower resolution limit of the printer, and present guidelines for accurately printing microfluidic features down to the 100 µm scale with a MiiCraft 50 3D printer. Specifically, we investigate the mixing performance of one of the most prevalent static micromixers, the staggered herringbone mixer (SHM), with dimensions scaled to the 3D printer resolution limit and with common fabrication defects from 3D printed molds. Microfluidic molds for the SHM were 3D printed with several cycles of SHM features, and then transferred to PDMS using soft lithography. Print-to-print defects and variations in the SHM geometry caused by printing limitations, and their impacts on mixing performance, were thoroughly quantified. Mixing performance was assessed for a range of Péclet numbers using fluorescence microscopy to observe the extent of mixing of aqueous streams with and without a fluorescent dextran tracer along successive SHM cycles. Well-mixed output streams were observed at Pe ≤ 2000, but more cycles of mixing were needed as compared to SHM channels with smaller feature sizes from prior studies. Although modest losses in mixing quality are apparent as the number of device irregularities increases, these differences largely disappear after 2-3 cycles of mixing. These results demonstrate the feasibility of 3D printing as a method for SHM device fabrication that is robust to print-to-print variations, and could enable its use for applications that call for low-cost and high-throughput manufacturing while maintaining performance, such as diagnostic platforms.