Airfoil Deterministic Lateral Displacement for High-Throughput Particle Separation in Viscous Fluids

2021 ◽  
Author(s):  
Brian Senf ◽  
Kawkab Ahasan ◽  
Jong-Hoon Kim
Keyword(s):  
High Throughput ◽  
Lateral Displacement ◽  
Particle Separation ◽  
Viscous Fluids ◽  
Deterministic Lateral Displacement

Airfoil Deterministic Lateral Displacement for High-Throughput Particle Separation in Viscous Fluids

2020 ◽  
Author(s):  
Brian Senf ◽  
Kawkab Ahasan ◽  
Jong-Hoon Kim
Keyword(s):  
High Throughput ◽  
Biological Sample ◽  
Particle Trajectory ◽  
Lateral Displacement ◽  
Particle Separation ◽  
Reynolds Numbers ◽  
Deionized Water ◽  
Fluid Viscosity ◽  
Deterministic Lateral Displacement ◽  
Low Reynolds Numbers

Abstract Deterministic Lateral Displacement (DLD) is an inertial size-based particle separation technique with great possibilities for use with biological sample preparation. Recently it has been shown that particle shift of a DLD is highly dependent on the Reynolds number. Additionally, particle trajectory has been characterized in a high throughput airfoil array DLD with varying Angle of Attack (AoA) in Deionized water. The AoA can be shifted negatively assisting in particle trajectory increases at low Reynolds numbers. With variations in fluid viscosity, particle trajectories compared to Reynolds value should theoretically have a constant and similar slope. In this work, various viscosities are tested in a DLD with a neutral and negative AoA to eventually characterize non-Newtonian fluids within a DLD. Due to higher viscosities increasing the internal pressure of the device, the negative AoA DLD shows promising results at higher range viscosities due to its ability to shift particles at lower Reynolds numbers.

Limits of Parabolic Flow Theory in Microfluidic Particle Separation: A Computational Study

2013 ◽  
Author(s):  
Ryan S. Pawell ◽  
Tracie J. Barber ◽  
David W. Inglis ◽  
Robert A. Taylor
Keyword(s):  
Computational Study ◽  
Lateral Displacement ◽  
Particle Separation ◽  
Flow Profile ◽  
Flow Rates ◽  
Deterministic Lateral Displacement ◽  
Flow Development ◽  
Separation Threshold ◽  
Parabolic Flow ◽  
Size Based Separation

Microfluidic particle separation technologies are useful for enriching rare cell populations for academic and clinical purposes. In order to separate particles based on size, deterministic lateral displacement (DLD) arrays are designed assuming that the flow profile between posts is parabolic or shifted parabolic (depending on post geometry). The design process also assumes the shape of the normalized flow profile is speed-invariant. The work presented here shows flow profile shapes vary, in arrays with circular and triangular posts, from this assumption at practical flow rates (10 < Re < 100). The root-mean-square error (RMSE) of this assumption in the circular post arrays peaked at 0.144. The RMSE in the triangular post array peaked at 0.136. Flow development occurred more rapidly in circular post arrays when compared to triangular post arrays. Additionally, the changes in critical bumping diameter (DCB) the DLD design metric used to calculate the size-based separation threshold were examined for 10 different row shift fractions (FRS). These errors correspond to a DCB that varies as much as 11.7% in the circular post arrays and 15.1% in the triangular post arrays.

Study of Angle-of-Attack (AoA) for Airfoil in Deterministic Lateral Displacement (DLD)

2019 ◽  
Author(s):  
Kawkab Ahasan ◽  
Jong-Hoon Kim
Keyword(s):  
High Throughput ◽  
Lateral Displacement ◽  
Angle Of Attack ◽  
Critical Diameter ◽  
Control Flow ◽  
Flow Rates ◽  
Deterministic Lateral Displacement ◽  
Potential Applications ◽  
High Reynolds ◽  
Flow Symmetry

Abstract Deterministic lateral displacement (DLD) is a method of inertial size-based particle separation with potential applications in high throughput sample processing, such as the fractionation of blood or the purification of target species like viral particles or circulating tumor cells. Recently, it has been shown that symmetric airfoils with neutral angle-of-attack (AoA) can be used for high-throughput design of DLD device, due to their mitigation of vortex effects and preservation of flow symmetry under high Reynolds number (Re) conditions. While high-Re operation with symmetric airfoils has been established, the effect of AoA for airfoil on the DLD performance has not been characterized. In this study, we present a high-Re investigation with symmetric airfoil-shaped pillars having positive and negative 15 degree AoA. Both positive and negative AoA configurations yield significant flow anisotropy at higher flow rates. The stronger shift of the critical diameter (Dc) was observed with negative AoA, but not in positive AoA device. The most likely contributor may be the growing anisotropy that develops in the AoA device at higher flow rates. This study shows that high-Re DLD design with airfoil shaped pillars requires significant consideration for pillar orientation to control flow symmetry.

Towards Deep Learning Approaches for Quantitative Analysis of High-Throughput DLD

2020 ◽  
Author(s):  
Eric A. Gioe ◽  
Xiaolin Chen ◽  
Jong-Hoon Kim
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Lateral Displacement ◽  
Great Promise ◽  
Learning Approaches ◽  
Timely Manner ◽  
Particle Detection ◽  
New Techniques ◽  
Deterministic Lateral Displacement ◽  
Total Particle

Abstract Microfluidics has shown great promise for the sorting or separation of biological cells such as circulating tumor cells since the first studies came out a few decades ago. With recent advances in high-throughput microfluidics, analysis of massive amounts of data needs to be completed in an iterative, timely manner. However, the majority of analysis is either performed manually or through the use of superimposing multiple images to define the flow of the particles, taking a significant amount of time to complete. The objective of the work is to increase the efficiency and repeatability of particle detection in a high-throughput deterministic lateral displacement (DLD) device. The program proposed is in the early stages of development, but shows promise. The average time it takes to analyze over a gigabyte of video is 24.21 seconds. The average percent error of the total particle detection was 21.42%. The assumptions made for the initial version of the program affect the accuracy of the particle in wall detection, so new techniques that do not follow the assumptions will need to be investigated. More work will be completed to implement machine learning or deep learning to assist in the development of DLD devices.

scholarly journals Electrokinetically driven deterministic lateral displacement for particle separation in microfluidic devices

2014 ◽  
Vol 18 (5-6) ◽  
pp. 1195-1200 ◽  
Author(s):  
Srinivas Hanasoge ◽  
Raghavendra Devendra ◽  
Francisco J. Diez ◽  
German Drazer
Keyword(s):  
Lateral Displacement ◽  
Particle Separation ◽  
Microfluidic Devices ◽  
Deterministic Lateral Displacement
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