Continuously Sorting Particles According to Shape

There are numerous filters to separate particles in liquid based on their size, which can be enough to isolate them; however, particle shape can be more important, as it distinguishes healthy red blood cells from those affected by sickle-cell disease or malaria. Shape can also be used to determine what stage a cell is in of the cell cycle, which would benefit researchers looking for dividing cells. Recent research by Dino Di Carlo of UCLA looks to separate particles of differing aspect ratios continuously, using inertial fluid-dynamics. His work, “Continuous Inertial Focusing and Separation of Particles by Shape,” featured in Physical Review X reminds me of his previous work to use inertial fluid-dynamics to continuously filter particles according to size.

Existing methods to separate particles according to shape include hydro-dynamic filtration (HDF) and deterministic lateral displacement (DLD), along with a few others. DLD involves a grid of posts in a channel that is arranged in a way that effectively separates particles according to size. This can be enhanced by controlling particle orientation in order to filter by particle shape or shear stress to separate by particle deformability. HDF uses highly branched channels to separate particles by size according to the fluidic resistance of the side channels. Both of these methods are passive and continuous, but they have highly complex structures and require low flow rates (no greater than a few µL/min). Di Carlo’s method uses fluid inertia to focus particles in channels at high flow rates from 40 to 80 µL/min. This utilizes a shear-gradient lift force and a wall-effect lift force in order to shift particles across streamlines when the Reynolds number of the particle is on the magnitude of 1 or greater. The shear-gradient lift force is directed down the shear gradient and toward the wall, while the wall-effect lift force is caused by the wake of a particle near the wall and is directed away from the wall. With a Reynolds numbers on the order of 1, inertial lift forces dominate the particle behavior while viscous interactions dominate when the particle Reynolds number is << 1. As the particle Reynolds number increases, migration across streamlines, away from the center line, is observed.

Di Carlo attempted to separate spheres and ellipsoids of varying aspect ratios, while conserving volume. Spheres of 3 µm and 6 µm in diameter, and ellipsoids that conserved the volume at 1:3 and 1:5 aspect ratios were used.

Spheres and ellipsoids are sorted using inertial fluid-dynamics. Particles with larger aspect radios reach equilibrium positions (Xeq) 4 cm downstream.

This work demonstrates that rod-like particles find equilibrium positions close to the center of the channel, while spheres of the same volume end up in streamlines close to the wall. When the major axis of the particles rotates perpendicular to the plane of the wall, the wall-effect lift increases and the particles is pushed away from the wall. Once the major axis has realigned with the direction of the flow, the wall-effect lift decreases and the particles move towards the wall one again. However, particles with higher aspect ratios experience a wall-effect lift greater than the shear-gradient lift and find equilibrium positions closer to the center of the channel. As the Reynolds number of the particle increases (increasing flow rate is one way to increase Reynolds number), the shear-gradient lift force increases faster than the wall-effect lift force. But the particles with higher aspect ratios rotate and experience a greater wall-effect lift force and return to the center. This relationship with increasing particle Reynolds number allows this method to scale with flow rate, while the previous methods do not.

Four different shape-activated particle-sorting (SAPS) devices were designed with varying numbers of outlets, outlet resistances, channel aspect ratios and flow rates. Three of the devices used 6 µm spheres and their derived ellipsoids, while the final device used the 3 µm particles. All of the devices had varying performances, but the researchers selected device C, which had 7 outlets and isolated 88% of the spheres with 87% purity, 49% of 1:5 rods with 78% purity and 77% of 1:3 rods with 80% purity, for sorting yeast cells. Yeast cells are normally spherical, but form a bispherical twin or aggregate when budding. This change in shape is similar to the varying aspect ratios examined previously. It is useful to synchronize cell cycle stages, but this may be achieved with chemicals that alter cell physiology, changes in temperature or size filtration. SAPS C was able to extract nondividing singles with high yield and purity up to 94% and 54% of budded yeast cells were recovered at 31% purity, which increased from 6.6% purity at the inlet. According to my rough measurements from the paper’s figures, the aspect ratio of budding yeast is less than 2:1 which may explain the difference in performance compared to the original ellipsoids. Previously, Sugaya et al. used a 5 outlet HDF system to separate budding yeast cells. In comparison, Sugaya achieved up to 69.4% purity of budding cells in one outlet, up from 39.4% at the inlet. This same outlet recovered 28.8% of budding cells while another outlet recovered 65.2%. There is still room for improvement of Di Carlo’s budding yeast yield, but this operated at 1500 cell/s, compared to 100 cell/s from previous work that utilized dielectrophoretic forces according to the opacity of dividing yeast.

Di Carlo has proposed that this work be used to sort shaped particles in other areas to improve cytometry that operates on spherical particles, alignment of barcoded particles, and identification of microalgae that vary in size and shape. Interestingly, he also introduced the capability of this setup for a non-biological process: improving cement. Cement strength and stability are affected by particle shape and size and could benefit from shape based separation. According to Dr. Di Carlo, “… [Cement] particles that are too large may not react completely in internal regions of the particle, while smaller particles with very high surface area to volume ratios can react too quickly and may not be stable.” I’m excited to see microfluidics expand into more established industries and further demonstrating real-world potential to be a more cost effective, accessible technology.

Masaeli, M., Sollier, E., Amini, H., Mao, W., Camacho, K., Doshi, N., Mitragotri, S., Alexeev, A., & Di Carlo, D. (2012). Continuous Inertial Focusing and Separation of Particles by Shape Physical Review X, 2 (3) DOI: 10.1103/PhysRevX.2.031017

Di Carlo, D., Irimia, D., Tompkins, R., & Toner, M. (2007). Continuous inertial focusing, ordering, and separation of particles in microchannels Proceedings of the National Academy of Sciences, 104 (48), 18892-18897 DOI: 10.1073/pnas.0704958104

Sugaya, S., Yamada, M., & Seki, M. (2011). Observation of nonspherical particle behaviors for continuous shape-based separation using hydrodynamic filtration Biomicrofluidics, 5 (2) DOI: 10.1063/1.3580757

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New robots can continuously map their environment with low-cost camera

ScienceDaily (Feb. 16, 2012) — Robots could one day navigate through constantly changing surroundings with virtually no input from humans, thanks to a system that allows them to build and continuously update a three-dimensional map of their environment using a low-cost camera such as Microsoft's Kinect.

The system, being developed by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), could also allow blind people to make their way unaided through crowded buildings such as hospitals and shopping malls.

To explore unknown environments, robots need to be able to map them as they move around -- estimating the distance between themselves and nearby walls, for example -- and to plan a route around any obstacles, says Maurice Fallon, a research scientist at CSAIL who is developing these systems alongside John J. Leonard, professor of mechanical and ocean engineering, and graduate student Hordur Johannsson.

But while a large amount of research has been devoted to developing one-off maps that robots can use to navigate around an area, these systems cannot adjust to changes in the surroundings over time, Fallon says: "If you see objects that were not there previously, it is difficult for a robot to incorporate that into its map."

The new approach, based on a technique called Simultaneous Localization and Mapping (SLAM), will allow robots to constantly update a map as they learn new information over time, he says. The team has previously tested the approach on robots equipped with expensive laser-scanners, but in a paper to be presented this May at the International Conference on Robotics and Automation in St. Paul, Minn., they have now shown how a robot can locate itself in such a map with just a low-cost Kinect-like camera.

As the robot travels through an unexplored area, the Kinect sensor's visible-light video camera and infrared depth sensor scan the surroundings, building up a 3-D model of the walls of the room and the objects within it. Then, when the robot passes through the same area again, the system compares the features of the new image it has created -- including details such as the edges of walls, for example -- with all the previous images it has taken until it finds a match.

At the same time, the system constantly estimates the robot's motion, using on-board sensors that measure the distance its wheels have rotated. By combining the visual information with this motion data, it can determine where within the building the robot is positioned. Combining the two sources of information allows the system to eliminate errors that might creep in if it relied on the robot's on-board sensors alone, Fallon says.

Once the system is certain of its location, any new features that have appeared since the previous picture was taken can be incorporated into the map by combining the old and new images of the scene, Fallon says.

The team tested the system on a robotic wheelchair, a PR2 robot developed by Willow Garage in Menlo Park, Calif., and in a portable sensor suit worn by a human volunteer. They found it could locate itself within a 3-D map of its surroundings while traveling at up to 1.5 meters per second.

Ultimately, the algorithm could allow robots to travel around office or hospital buildings, planning their own routes with little or no input from humans, Fallon says.

It could also be used as a wearable visual aid for blind people, allowing them to move around even large and crowded buildings independently, says Seth Teller, head of the Robotics, Vision and Sensor Networks group at CSAIL and principal investigator of the human-portable mapping project. "There are also a lot of military applications, like mapping a bunker or cave network to enable a quick exit or re-entry when needed," he says. "Or a HazMat team could enter a biological or chemical weapons site and quickly map it on foot, while marking any hazardous spots or objects for handling by a remediation team coming later. These teams wear so much equipment that time is of the essence, making efficient mapping and navigation critical."

While a great deal of research is focused on developing algorithms to allow robots to create maps of places they have visited, the work of Fallon and his colleagues takes these efforts to a new level, says Radu Rusu, a research scientist at Willow Garage who was not involved in this project. That is because the team is using the Microsoft Kinect sensor to map the entire 3-D space, not just viewing everything in two dimensions.

"This opens up exciting new possibilities in robot research and engineering, as the old-school 'flatland' assumption that the scientific community has been using for many years is fundamentally flawed," he says. "Robots that fly or navigate in environments with stairs, ramps and all sorts of other indoor architectural elements are getting one step closer to actually doing something useful. And it all starts with being able to navigate."

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The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Helen Knight.

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