Multi-stage fluidic reaction schemes for suspended particles (e.g., micro/nanospheres, cells, bacterial species, and extracellular vesicles) underly a diversity of chemical and biological applications. Conventional methods for executing such protocols can be exceedingly time, labor, and/or cost intensive. Microfluidic strategies can address these drawbacks; however, such technologies typically rely on clean room-based microfabrication that suffer from similar deficits for manufacturing the chips. To simultaneously overcome these challenges, here we explore the use of the submicron-scale additive manufacturing approach, “Two-Photon Direct Laser Writing (DLW)”, as a means for fabricating micro-fluidic “Deterministic Lateral Displacement (DLD)” arrays capable of passively guiding suspended particles across discrete, adjacent flow streams—the fundamental capability of continuous-flow multi-stage particle microreactors. Experimental results from microfluidic experimentation with 5 μm-in-diameter fluorescent particles revealed effective particle transport across flow streams, with 87.5% of fluorescent peaks detected in the designated, opposing outlet following the DLD array. These results suggest utility of the presented approach for micro- and nanoparticle-based microfluidic reactors targeting wide-ranging chemical and biological applications.
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Deterministic Lateral Displacement via Self-Assembly-Based Hexagonally Arranged Triangular Posts
Deterministic lateral displacement (DLD) is a microfluidic micro/nanopost array-based technique for size-based particle separations. A key challenge in scaling DLD for handling smaller particles is that creating such “nanoDLD” arrays can be cost-intensive with substantial technical hurdles. To circumvent such issues, here we explore a new “hexagonally arranged triangles (HAT)” DLD geometry that is based on patterns associated with nanosphere lithography (NSL). Finite element simulations and preliminary experiments with 0.86 μm and 4.7 μm particles suggest effective separation capabilities of the HAT-DLD approach, marking an important first step toward new classes of nanoDLD arrays fabricated through bottom-up, self-assembly-based NSL.
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- Award ID(s):
- 1761395
- NSF-PAR ID:
- 10376838
- Date Published:
- Journal Name:
- Proceedings of the 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences (µTAS 2021)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Chinese hamster ovary (CHO) cell is the most widely used mammalian cell line for commercial production of therapeutic protein. Any presence of non-viable cells in culture medium may adversely affect subsequent functionality of these proteins. Therefore, separation of non-viable cells from suspending medium is critical in biopharmaceutical and biomedical sectors. One such method termed Deterministic Lateral Displacement has already shown promising capabilities in separating cells based on the cell size difference by taking advantage of the predictable flow laminae. However, in cases where size overlaps between viable and non-viable cells are present, separation based solely on size suffers and high-resolution separation techniques are required. Dielectrophoresis, one of the most widely used nonlinear electro-kinetic mechanism, has the potential to manipulate the same size cells depending on the dielectric properties of individual cells. In this work, we demonstrated that a DLD device can be combined with a frequency-based AC electric field to perform high resolution continuous separation of non-viable CHO cells from the viable or productive cells. The behavior of the coupled DLD-DEP device is further investigated by employing numerical simulation to check the effect of geometrical parameters of the DLD arrays, velocities of the flow field and required applied voltages. A moderate row shift fraction with velocity 700μm/s provided a good separation behavior without any trapping. The cell viability was also ensured by maintaining proper electric field which otherwise may cause cell loss due to ion leakage. Our developed numerical model and presented results lay the groundwork for design and fabrication of high resolution DLD-DEP microchips for enhanced separation of viable and nonviable cells.
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Abstract Circulating Tumor Cells (CTCs), which migrate from original sites in a body to distant organs through blood, are a key factor in cancer detection. Emerging Label-free techniques owing to their inherent advantage to preserve characteristics of sorted cells and low consumption of samples can be promising to the prediction of cancer progression and metastasis research. Deterministic Lateral Displacement (DLD) is one of the label-free separation techniques employing a specific arrangement of micro-posts for continuous separation of suspended cells in a buffer based on the size of cells. Separation based solely on size is challenging since the size distributions of CTCs might overlap with those of normal blood cells. To address this problem, DLD can be combined with dielectrophoresis (DEP) technique which is the phenomenon of particle movement in a non-uniform electric field owing to the polarization effect. Although, DLD devices employ the laminar flow in low Reynolds number (Re) fluid flow due to predictability of such flow regimes, they should be improved to work in higher Re flow regime so as to attain high throughput devices. In this paper, a particle tracing simulation is developed to study the effects of different post shapes, shift fraction of micropost arrays, and dielectrophoresis forces on separation of CTCs from peripheral blood cells. Our numerical model and results provide a groundwork for design and fabrication of high-throughput DLD-DEP devices for improvement of CTC separation.
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Deterministic lateral displacement (DLD) is a microfluidic method for the continuous separation of particles based on their size. There is growing interest in using DLD for harvesting circulating tumor cells from blood for further assays due to its low cost and robustness. While DLD is a powerful tool and development of high-throughput DLD separation devices holds great promise in cancer diagnostics and therapeutics, much of the experimental data analysis in DLD research still relies on error-prone and time-consuming manual processes. There is a strong need to automate data analysis in microfluidic devices to reduce human errors and the manual processing time. In this work, a reliable particle detection method is developed as the basis for the DLD separation analysis. Python and its available packages are used for machine vision techniques, along with existing identification methods and machine learning models. Three machine learning techniques are implemented and compared in the determination of the DLD separation mode. The program provides a significant reduction in video analysis time in DLD separation, achieving an overall particle detection accuracy of 97.86% with an average computation time of 25.274 s.more » « less
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