Flow-based manipulation of particles is an essential tool for studying soft materials, but prior work has nearly exclusively relied on using two-dimensional (2D) flows generated in planar microfluidic geometries. In this work, we demonstrate 3D trapping and manipulation of freely suspended particles, droplets, and giant unilamellar vesicles in 3D flow fields using automated flow control. Three-dimensional flow fields including uniaxial extension and biaxial extension are generated in 3D-printed fluidic devices combined with active feedback control for particle manipulation in 3D. Flow fields are characterized using particle tracking velocimetry complemented by finite-element simulations for all flow geometries. Single colloidal particles (3.4 μm diameter) are confined in low viscosity solvent (1.0 mPa s) near the stagnation points of uniaxial and biaxial extensional flow for long times (≥10 min) using active feedback control. Trap stiffness is experimentally determined by analyzing the power spectral density of particle position fluctuations. We further demonstrate precise manipulation of colloidal particles along user-defined trajectories in three dimensions using automated flow control. Newtonian liquid droplets and GUVs are trapped and deformed in precisely controlled uniaxial and biaxial extensional flows, which is a new demonstration for 3D flow fields. Overall, this work extends flow-based manipulation of particles and droplets to three dimensions, thereby enabling quantitative analysis of colloids and soft materials in complex nonequilibrium flows.
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Nanoparticle dispersion in porous media: Effects of hydrodynamic interactions and dimensionality
We investigate the effect of steric and hydrodynamic interactions (HI) on quiescent diffusion and flow‐driven transport of finite‐sized nanoparticles through periodic 2D (two‐dimensional) and 3D (three‐dimensional) nanopost arrays using Stokesian dynamics simulations. We find that steric and HI hinder particle diffusivity under quiescent conditions and enhance longitudinal dispersion under flow. Moreover, the presence of HI leads to a power‐law increase in the longitudinal dispersion coefficient with Pe due to spatial variations in the fluid velocity. Lastly, our simulations reveal that longitudinal particle dispersion coefficients behave similarly in 2D and 3D when HI are included.
more » « less- Award ID(s):
- 1751173
- NSF-PAR ID:
- 10449067
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- AIChE Journal
- Volume:
- 67
- Issue:
- 3
- ISSN:
- 0001-1541
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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A multi-dimensional model of the spark ignition process for SI engines was developed as a user defined function (UDF) integrated into the commercial engine simulation software CONVERGE CFD. The model simulates spark plasma movement in an inert flow environment without combustion. The UT model results were compared with experiments for arc movement in a crossflow and also compared with calorimeter measurements of thermal energy deposition under quiescent conditions. The arc motion simulation is based on a mean-free-path physical model to predict the arc movement given the contours of the crossflow velocity through the gap and the interaction of the spatially resolved electric field with the electrons making up the arc. A further development is the inclusion of a model for the thermal energy deposition of the arc as it is stretched by the interaction of the flow and the electric field. A novel feature of this model is that the thermal energy delivered to the gap at the start of the simulation is distributed uniformly along the arc rather than at discrete points along the arc, as is the case with the default CONVERGE CFD ignition models. This feature was found to greatly reduce the tendency of the arc to distort its shape and tangle itself in a non-physical way, as is the tendency when discrete energy input is used. It was found that the tangled distortion of the arc when using discrete energy input was due to perturbations along the arc caused by differential expansion of the gas along groups of adjacent mesh cells that either had energy input or did not. The distributed energy feature also gave arc temperature distributions that were more spatially uniform and had steeper temperature gradients, consistent with experimental arc images. The results are compared with experimental high-speed video images of arc movement for a spark plug of similar geometry and taken over a range of pressures and crossflow velocities in a high-pressure constant volume vessel. There is good agreement between the simulations and experimental images for the arc stretch distance in response to a crossflow. The simulations did not display as much lateral arc dispersion as seen in the experimental results, however, that were perhaps associated with flow recirculation zones downstream of the gap, present in the experiments. The influence of the electric field was shown by turning off the electric field effect in the simulations such that the arc movement was influenced by the flow field alone. The effect of the electric field was found to be more pronounced at lower crossflow velocities of 5 m/s and at lower pressures.more » « less
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Abstract Motivation Three-dimensional (3D) genome organization plays important functional roles in cells. User-friendly tools for reconstructing 3D genome models from chromosomal conformation capturing data and analyzing them are needed for the study of 3D genome organization.
Results We built a comprehensive graphical tool (GenomeFlow) to facilitate the entire process of modeling and analysis of 3D genome organization. This process includes the mapping of Hi-C data to one-dimensional (1D) reference genomes, the generation, normalization and visualization of two-dimensional (2D) chromosomal contact maps, the reconstruction and the visualization of the 3D models of chromosome and genome, the analysis of 3D models and the integration of these models with functional genomics data. This graphical tool is the first of its kind in reconstructing, storing, analyzing and annotating 3D genome models. It can reconstruct 3D genome models from Hi-C data and visualize them in real-time. This tool also allows users to overlay gene annotation, gene expression data and genome methylation data on top of 3D genome models.
Availability and implementation The source code and user manual: https://github.com/jianlin-cheng/GenomeFlow.
Supplementary information Supplementary data are available at Bioinformatics online.
