Anticipating Cutoff Diameters in Deterministic Lateral Displacement (DLD) Microfluidic Devices for an Optimized Particle Separation

Deterministic Lateral Displacement Using Hexagonally Arranged, Bottom-Up-Inspired Micropost Arrays

Size-based separation of particles in microfluidic devices can be achieved using arrays of micro- or nanoscale posts using a technique known as deterministic lateral displacement (DLD). To date, DLD arrays have been limited to parallelogram or rotated-square arrangements of posts, with various post shapes having been explored in these two principal arrangements. This work examines a new DLD geometry based on patterning obtainable through self-assembly of single-layer nanospheres, which we call hexagonally arranged triangle (HAT) geometry. Finite element simulations are used to characterize the DLD separation properties of the HAT geometry. The relationship between the array angle, the gap spacing, and the critical diameter for separation is derived for the HAT geometry and expressed in a similar mathematical form as conventional parallelogram and rotated-square DLD arrays. At array angles <7°, HAT structures demonstrate smaller particle sorting capability (smaller critical diameter-to-gap spacing ratio) compared to published experimental results for parallelogram-type DLD arrays with circular posts. Experimental validation of DLD separation confirms the separation ability of the HAT array geometry. It is envisioned that this work will provide the first step toward future implementation of nanoscale DLD arrays fabricated by low-cost, bottom-up self-assembly approaches.

Deep Learning-Enabled Label-Free On-Chip Detection and Selective Extraction of Cell Aggregate-Laden Hydrogel Microcapsules

Microfluidic encapsulation of cells/tissues in hydrogel microcapsules has attracted tremendous attention in the burgeoning field of cell-based medicine. However, when encapsulating rare cells and tissues (e.g., pancreatic islets and ovarian follicles), the majority of the resultant hydrogel microcapsules are empty and should be excluded from the sample. Furthermore, the cell-laden hydrogel microcapsules are usually suspended in an oil phase after microfluidic generation, while the microencapsulated cells require an aqueous phase for further culture/transplantation and long-term suspension in oil may compromise the cells/tissues. Thus, real-time on-chip selective extraction of cell-laden hydrogel microcapsules from oil into aqueous phase is crucial to the further use of the microencapsulated cells/tissues. Contemporary extraction methods either require labeling of cells for their identification along with an expensive detection system or have a low extraction purity (<≈30%). Here, a deep learning-enabled approach for label-free detection and selective extraction of cell-laden microcapsules with high efficiency of detection (≈100%) and extraction (≈97%), high purity of extraction (≈90%), and high cell viability (>95%) is reported. The utilization of deep learning to dynamically analyze images in real time for label-free detection and on-chip selective extraction of cell-laden hydrogel microcapsules is unique and may be valuable to advance the emerging cell-based medicine.