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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Self-sculpting sand: Heaps of 'smart sand’ could assume any shape, form new tools or duplicatie broken parts

ScienceDaily (Apr. 2, 2012) — Imagine that you have a big box of sand in which you bury a tiny model of a footstool. A few seconds later, you reach into the box and pull out a full-size footstool: The sand has assembled itself into a large-scale replica of the model.

That may sound like a scene from a Harry Potter novel, but it's the vision animating a research project at the Distributed Robotics Laboratory (DRL) at MIT's Computer Science and Artificial Intelligence Laboratory. At the IEEE International Conference on Robotics and Automation in May DRL researchers will present a paper describing algorithms that could enable such "smart sand." They also describe experiments in which they tested the algorithms on somewhat larger particles -- cubes about 10 millimeters to an edge, with rudimentary microprocessors inside and very unusual magnets on four of their sides.

Unlike many other approaches to reconfigurable robots, smart sand uses a subtractive method, akin to stone carving, rather than an additive method, akin to snapping LEGO blocks together. A heap of smart sand would be analogous to the rough block of stone that a sculptor begins with. The individual grains would pass messages back and forth and selectively attach to each other to form a three-dimensional object; the grains not necessary to build that object would simply fall away. When the object had served its purpose, it would be returned to the heap. Its constituent grains would detach from each other, becoming free to participate in the formation of a new shape.

Distributed intelligence

Algorithmically, the main challenge in developing smart sand is that the individual grains would have very few computational resources. "How do you develop efficient algorithms that do not waste any information at the level of communication and at the level of storage?" asks Daniela Rus, a professor of computer science and engineering at MIT and a co-author on the new paper, together with her student Kyle Gilpin. If every grain could simply store a digital map of the object to be assembled, "then I can come up with an algorithm in a very easy way," Rus says. "But we would like to solve the problem without that requirement, because that requirement is simply unrealistic when you're talking about modules at this scale." Furthermore, Rus says, from one run to the next, the grains in the heap will be jumbled together in a completely different way. "We'd like to not have to know ahead of time what our block looks like," Rus says.

Conveying shape information to the heap with a simple physical model -- such as the tiny footstool -- helps address both of these problems. To get a sense of how the researchers' algorithm works, it's probably easiest to consider the two-dimensional case. Picture each grain of sand as a square in a two-dimensional grid. Now imagine that some of the squares -- say, in the shape of a footstool -- are missing. That's where the physical model is embedded.

According to Gilpin-author on the new paper, the grains first pass messages to each other to determine which have missing neighbors. (In the grid model, each square could have eight neighbors.) Grains with missing neighbors are in one of two places: the perimeter of the heap or the perimeter of the embedded shape.

Once the grains surrounding the embedded shape identify themselves, they simply pass messages to other grains a fixed distance away, which in turn identify themselves as defining the perimeter of the duplicate. If the duplicate is supposed to be 10 times the size of the original, each square surrounding the embedded shape will map to 10 squares of the duplicate's perimeter. Once the perimeter of the duplicate is established, the grains outside it can disconnect from their neighbors.

Rapid prototyping

The same algorithm can be varied to produce multiple, similarly sized copies of a sample shape, or to produce a single, large copy of a large object. "Say the tire rod in your car has sheared," Gilpin says. "You could duct tape it back together, put it into your system and get a new one."

The cubes -- or "smart pebbles" -- that Gilpin and Rus built to test their algorithm enact the simplified, two-dimensional version of the system. Four faces of each cube are studded with so-called electropermanent magnets, materials that can be magnetized or demagnetized with a single electric pulse. Unlike permanent magnets, they can be turned on and off; unlike electromagnets, they don't require a constant current to maintain their magnetism. The pebbles use the magnets not only to connect to each other but also to communicate and to share power. Each pebble also has a tiny microprocessor, which can store just 32 kilobytes of program code and has only two kilobytes of working memory.

The pebbles have magnets on only four faces, Gilpin explains, because, with the addition of the microprocessor and circuitry to regulate power, "there just wasn't room for two more magnets." But Gilpin and Rus performed computer simulations showing that their algorithm would work with a three-dimensional block of cubes, too, by treating each layer of the block as its own two-dimensional grid. The cubes discarded from the final shape would simply disconnect from the cubes above and below them as well as those next to them.

True smart sand, of course, would require grains much smaller than 10-millimeter cubes. But according to Robert Wood, an associate professor of electrical engineering at Harvard University, that's not an insurmountable obstacle. "Take the core functionalities of their pebbles," says Wood, who directs Harvard's Microrobotics Laboratory. "They have the ability to latch onto their neighbors; they have the ability to talk to their neighbors; they have the ability to do some computation. Those are all things that are certainly feasible to think about doing in smaller packages."

"It would take quite a lot of engineering to do that, of course," Wood cautions. "That's a well-posed but very difficult set of engineering challenges that they could continue to address in the future."

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The above story is reprinted from materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty, MIT News Office.

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