Recent posts – Page 7

2018-07-192018-07-19

Seeing Inside Sand: Visualizing Force Chains with Photoelastic Disks

Original Article: Contact force measurements and stress-induced anisotropy in granular materials

As their name suggests, so-called “granular materials” are made up of “grains” — small (but macroscopic) pieces of sand, glass beads, coffee grounds, or almost any other solid you can think of. Granular materials can flow like a liquid (like sand in an hourglass), resist deformation like a solid (like the sand under your feet at the beach), or quickly transition between these states (like pebbles in a rockslide).

Granular materials have properties that have no equivalent in regular materials like wood, metal, or rubber. In solids like these — the kind we learn about in materials science class — a force applied to the surface propagates through the material smoothly and predictably. If a uniform force is applied to the surface of a material, every equally sized cross-section of that material bears the same amount of load. In granular materials, however, the situation is very different: in a sand pile under stress (that is, when a force is applied to its surface), the force is distributed unevenly — some individual sand grains bear far more load than others. Surprisingly, this remains true even when the sand grains themselves are identical. What’s more, the load-bearing grains connect to one another to make a fractal, lightning-like pattern inside the material, like that shown in Figure 1. These string-like arrangements of load-bearing grains are called force chains.

Screen Shot 2018-07-19 at 12.08.53 PM — **Figure 1 – Force chains in a computer simulation of a sand pile. The thickness of a black line indicates the magnitude of the force at that point inside the sand pile. (From** **Nadukuru & Michalowski (2012)**.)

As Figure 1 shows, force chains are easy to identify in a computer simulation. But can you “see” forces inside a real material? Today’s paper — which is from 2005 but has already proven to be a classic in the field — shows us how it can be done. The secret lies in a clever choice of “grain”: in their experiments, Majmudar and Behringer use about 2,500 transparent plastic disks, each about a centimeter in diameter and half a centimeter tall. These disks are placed in a thin container that confines them to a single plane — this experiment is similar to the board game Connect Four, but without the vertical rails.

Crucially, these plastic disks have a property called photoelasticity: when they are stretched or squeezed, they deform, and when they deform they alter the polarization state of light passing through them. For instance, linearly polarized light might be converted into circularly polarized light, or light that’s still linearly polarized, but along a different axis than before. Thus, placed between crossed (perpendicularly oriented) polarizers, an unstressed disk will appear dark, but a squeezed or stretched disk will appear bright, since any alteration of the polarization state of the incoming light will allow some of it to pass through the second polarizer. What’s more, the pattern of light — like that shown in Figure 2 — can be used to infer the normal and tangential forces acting on each disk.

Screen Shot 2018-07-19 at 12.14.35 PM — Figure 2 – Two plastic disks placed between crossed polarizers [1]. For an unstressed disk (top), the polarization state of the light is unaltered, and no light gets through the second polarizer. For a disk under load (bottom), the polarization state of transmitted light is altered — in this case, the polarization axis is rotated — allowing some light to pass through the second polarizer. The forces on the disk, indicated by thick black arrows, can be inferred from images such as the one on the bottom right

By imaging lots of disks at the same time, photoelasticity can be used to infer the overall stress pattern inside a granular material. Majmudar and Behringer are especially interested in two particularly simple situations: isotropic compression and shear. Under isotropic compression, the collection of disks is squeezed equally from all sides, while under shear, the collection of disks is squeezed on top and bottom, but allowed to expand by an exactly equal amount at the sides.

Interestingly, the system responds very differently to these two types of load: for isotropic compression, the force pattern, shown in the left panel of Figure 3, resembles a random network — short chains of highly stressed disks connect over distances of a few diameters. For shear (Figure 3, right panel), the situation is very different: long force chains, tens of disk diameters in length, extend in the direction along which the system is being squeezed. This phenomenon, where an applied stress causes the material itself to change in a direction-dependent manner, is called stress-induced anisotropy; it is not captured by the linear elasticity theory that students typically learn, even in advanced material science classes.

In the decades since this paper was published, the techniques pioneered by Majmudar and Behringer have allowed scientists to better understand properties of granular materials: under what circumstances force chains form, how they depend on properties of the disk such as shape and friction coefficient, and how they influence behaviors such as jamming – the rapid transition from a flowing state to a rigid one.

Screen Shot 2018-07-19 at 12.18.06 PM — **Figure 3 – A granular material under isotropic compression (left), and shear (right). In the sheared system, long, oriented force chains are clearly visible.**

Postscript: On the day of publication, we learned of the recent death of the PI of this paper, Bob Behringer, at the age of 69. This post highlights just one of the many contributions of this widely respected scientist to the field of soft matter physics. For a more detailed overview of Behringer’s life and work, see here.

Notes:

[1] The experiment described in the paper used crossed (oppositely oriented) circular polarizers rather than the linear ones shown here, but the principle is the same.

2018-06-272018-06-25

Putting the controversy over atomic-molecular theory to rest

Original paper: Einstein, Perrin, and the reality of atoms: 1905 revisited

There are many things that we “know” about the world around us. We know that the Earth revolves around the Sun, that gravity makes things fall downward, and that the apparently empty space around us is actually filled with the air that we breathe. We take for granted that these things are true. But how often do we consider whether we have seen evidence that supports these truths instead of trusting our sources of scientific knowledge?

Students in school are taught from an early age that matter is made of atoms and molecules. However, it wasn’t so long ago that this was a controversial belief. In the early 20th century, many scientists thought that atoms and molecules were just fictitious objects. It was only through the theoretical work of Einstein [1] and its experimental confirmation by Perrin [2] in the first decade of the 20th century that the question of the existence of atoms and molecules was put to rest. Today’s paper by Newburgh, Peidle, and Rueckner at Harvard University revisits these momentous developments with a holistic viewpoint that only hindsight can provide. In addition to re-examining Einstein’s theoretical analysis, the researchers also repeat Perrin’s experiments and demonstrate what an impressive feat his measurement was at that time.

In the mid-1800s, the botanist Robert Brown observed that small particles suspended in a liquid bounce around despite being inanimate objects. In an effort to explain this motion, Einstein started his 1905 paper on the motion of particles in a liquid with the assumption that liquids are, in fact, made of molecules. According to his theory, the molecules would move around at a speed determined by the temperature of the liquid: the warmer the liquid, the faster the molecules would move. And if a larger particle were suspended in the liquid, it would be bounced around by the molecules in the liquid.

Einstein knew that a particle moving through a liquid should feel the drag. Anyone who has been in a swimming pool has probably felt this; it is much harder to move through water than through air. The drag should increase with the viscosity, or thickness, of the fluid. Again, this makes sense: it is harder to move something through honey than through water. It is also harder to move a large object through a liquid than a small object, so the drag should increase with the size of the particle.

Assuming that Brownian motion was caused by collisions with molecules, and balancing it with the drag force, Einstein determined an expression for the mean square displacement of a particle suspended in a liquid. This relationship indicates how far a particle moves, on average, from its starting point in a given amount of time. He concluded that it should be given by

$latex \langle \Delta x (\tau) ^2 \rangle = \frac{RT}{3 \pi \eta N_A r} \tau$

where R is the gas constant, T is the temperature, $latex \eta$ is the viscosity of the liquid, $latex N_A$ is Avogadro’s number [3], r is the radius of the suspended particle, and $latex \tau$ is the time between measurements [4]. With this result, Einstein did not claim to have proven that the molecular theory was correct. Instead, he concluded that if someone could experimentally confirm this relationship, it would be a strong argument in favor of the atomistic viewpoint.

A man using a camera lucida to draw a picture of a small statue. — **Figure 1:** A camera lucida is an optical device allows an observer to simultaneously see an image and drawing surface and is therefore used as a drawing aid. (Source: an illustration from the Scientific American Supplement, January 11, 1879)

This is where Perrin came in. Nearly five years after Einstein’s paper was published, he successfully measured Avogadro’s number using Einstein’s equation, confirming both the relationship and the molecular theory behind it. However, with the resources available at the time, this experiment was a challenge. Perrin had to first learn how to make micron-size spherical particles that were small enough that their Brownian motion could be observed, but still large enough to see in a microscope. In order to measure the particles’ motion, he used a camera lucida attached to a microscope to see the moving particles on a surface where he could trace their outlines and measure their displacements by hand. Perrin obtained a value of $latex N_A = 7.15 \times 10^{23}$ by measuring the displacements of around 200 distinct particles in this way.

Performing this experiment in the 21st century was much simpler than it was for Perrin. Newburgh, Peidle, and Rueckner were able to purchase polystyrene microspheres of various sizes, eliminating the need to synthesize them. They also used a digital camera to record the particle positions over time instead of tracking the particles by hand. Using particles with radii of 0.50, 1.09, and 2.06 microns, they measured values of $latex 8.2 \times 10^{23}$, $latex 6.4 \times 10^{23}$, and $latex 5.7 \times 10^{23}$. Perhaps surprisingly, even with all of their modern advantages, the researchers’ results are not significantly closer to the actual value of $latex N_A = 6.02 \times 10^{23}$ than Perrin’s was a hundred years earlier.

A plot of the average mean square displacement of three different sized particles over time. — **Figure 2:** Einstein’s relationship predicts that the mean square displacement should be linear in time. By observing this relationship for three different particle sizes, the researchers use the slope to obtain three measurements of Avogadro’s number. (Newburgh et al., 2006)

For those of us who work in the field of soft matter, the existence of Brownian motion and the linear mean square displacement of a particle undergoing such motion are well-known scientific facts. The authors of this paper remind us that, not so long ago, even the existence of molecules was not generally accepted. And, although we often take for granted that these results are correct, first-hand observations can be useful for developing a deeper understanding and appreciation: “…one never ceases to experience surprise at this result, which seems, as it were, to come out of nowhere: prepare a set of small spheres which are nevertheless huge compared with simple molecules, use a stopwatch and a microscope, and find Avogadro’s number.” [5]

[1] A. Einstein, “On a new determination of molecular dimensions,” doctoral dissertation, University of Zürich, 1905.

[2] J. Perrin, “Brownian movement and molecular reality,” translated by F. Soddy Taylor and Francis, London, 1910. The original paper, “Le Mouvement Brownien et la Réalité Moleculaire” appeared in the Ann. Chimi. Phys. 18 8me Serie, 5–114 1909.

[3] Avogadro’s number is the number of atoms or molecules in one mole of a substance.

[4] In 1908, three years after Einstein’s paper, Langevin also obtained the same result using a Newtonian approach. (P. Langevin, “Sur la Theorie du Mouvement Brownien,” C. R. Acad. Sci. Paris 146, 530–533 1908.)

[5] A. Pais, Subtle Is the Lord (Oxford U. P., New York, 1982), pp. 88–92.

2018-06-22

A Storm of Oscillators

Original paper: Oscillators that sync and swarm

Japanese tree frogs follow a mating ritual that is so strange and beautiful that studying them may give rise to a new kind of science. During the rainy season, the male frogs gather near paddies and ponds and call to attract females. Researchers have found that isolated single male frogs call periodically [1], however, groups of males that are located near each other try to call out of sync. This temporal difference in calls allows the female frogs to correctly locate the male frogs. This is a natural system where spatial location of the frogs dictates the synchronization of their calls. This leads to an interesting question: What happens if you allow the synchronization of their calls to dictate their locations instead?

Scientists study synchronization to understand how an oscillating system evolves over time. However, systems ‘in sync’ do not usually move through space in a way that is related to their oscillations – consider the light flashes from a gathering of fireflies or the beating of a cardiac pacemaker. Swarming, on the other hand, is a form of self-organization where the participating individuals move through space, guided by some simple rules. In today’s paper by O’Keeffe, Hong, and Strogatz, the authors try to get a theoretical understanding of systems that show both synchronization and swarming. Previously, there had been studies of “mobile oscillators” where it was assumed that the location of an oscillator in space affected its phase. Here, the authors come up with a new theory that the phase affects where the oscillator is located in space. They name such particles swarmalators and propose a simple model to study their collective states analytically.

Imagine a set of particles on a plane, where each one is labeled by a number 1,2,3, etc. The ith particle (where i is any of the labels 1,2,3, etc.) has an internal state represented by a variable theta, $latex \mathbf{\theta}_i$, and a position represented by the vector $latex \mathbf{x}_i$. The internal state $latex \mathbf{\theta}_i$ is equivalent to a phase angle, so it can only take on values between 0 and 2?, while $latex \mathbf{x}_i$ can be any position on the plane. The key to swarmalators is that their position affects how their internal state changes and their internal state affects how their position changes. A relatively simple way to model this would be by the following set of equations:

$latex \dot{\mathbf{x}}_i = \frac{1}{N} \sum_{j \neq i}^{N} \frac{x_j-x_i}{|x_j-x_i|}(1+J cos(\theta_j-\theta_i)) – \frac{x_j-x_i}{|x_j-x_i|^2}$

$latex \dot{\mathbf{\theta_i}} = \frac{K}{N}\sum_{j \neq i}^{N}\frac{sin(\theta_j-\theta_i)}{|x_j-x_i|}$

The most interesting parameters in these equations are J and K. Let’s step through the equations to see what their effects are. The first equation says how the position of the ith swarmalator changes in time, given by the time derivative of $latex \dot{\mathbf{x}}_i$. It is affected by all the other particles. The first term in the sum is an attraction between particles and points in the direction from particle i to particle j. The parameter J controls how oscillations lead to reorganization in space. For positive J, the closer two swarmalators internal states are, the more they attract since cos(x) is maximized around x = 0. When J is negative, swarmalators are attracted to swarmaltors with as different from an internal state as possible. When J = 0, swaramalators’ spatial movements are immune to other’s internal state. The second term is simply a repulsion between particles, preventing them from occupying the same place.

Similarly, the second equation says how the internal state (the phase angle) changes in time. Like the position, it has a constant angular velocity, $latex \mathbf{\omega}_i$, and is also affected by all the other particles. The parameter K represents how well the internal state of oscillations are synchronized for swarmalators. For a positive K, the swaramalators want to be synchronized with each other, while for negative K they want to oscillate out of phase. This effect is distance dependent, so swarmalators are more affected by nearby swarmalators than ones that are further away.

The authors use a computer to solve this pair of governing equations to see how such a system will evolve over time. They report that there can be five different states based on the two parameters J and K (Figure 1).

41467_2017_1190_Fig1_HTML — Figure 1: Diagram showing the various combinations of J and K which give rise to the various states of the model.

Static synchrony: For all positive K and all J, the model predicts that swarmalators will arrange themselves in a circularly symmetric manner. Each of the swarmalators will be fully synchronized in phase (Figure 2). All static states reach an equilibrium after some time.

41467_2017_1190_MOESM4_ESM — Figure 2: This animation shows the time evolution of swarmalators when they are in a state of static synchrony. All swarmalators occupy a disk and they all have the same phase.

Static asynchrony: For negative J and negative K, i.e. for cases when swarmalators want to oscillate out of phase but are attracted to opposite phases spatially, they are distributed uniformly and every phase occurs everywhere. (Figure 3).

41467_2017_1190_MOESM5_ESM — Figure 3: The animation shows how swarmalators evolve and rearrange themselves in the state of static asynchrony. The swarmaltors all occupy a disk, with phases different from their neighbors, leading to all phases existing everywhere.

Static phase wave: For the special case K=0 and J>0,.when the swarmalators’ phases are frozen in time but they like to settle near other swarmalators with the same phase, an interesting phenomenon occurs. Since positive J means “like attracts like” the swarmalators arrange themselves in regions of similar phases. This leads to an annular structure where the spatial angle of each swarmalator is perfectly correlated with its phase of oscillation (Figure 4).

Figure 4: The state of static phase wave where the spatial angle of the swarmalators is directly correlated to their phase of oscillation.

Splintered phase wave: In the K<0 half-plane, and when the magnitude of K is not too large, the swarmalators tend to oscillate out of phase weakly while trying to get closer to similar phases and a non-stationary state occurs. Here the particles keep rearranging themselves into disconnected clusters of distinct phases. Unlike the earlier static states, within each cluster, the swarmalators execute small amplitude oscillations in both position and phase about their mean values.

Figure 5: The splintered phase wave state.

Active phase wave: On decreasing K further — that is, when swarmalators strongly desynchronize but try to move towards swarmalators in sync — the oscillations in phase and position increase until the swarmalators start to execute regular cycles. Although this is a new phase, the authors point out that the motion of swarmalators is reminiscent of the double milling of biological swarms where the population splits into groups that are rotating in opposite directions.

Figure 6: Swarmalators showing an active phase wave.

In this paper, O’Keeffe and his colleagues have demonstrated how a system of particles that have the freedom to move in space and match the oscillation phase of their neighboring particles can lead to a rich variety of patterns that change in space and time. Perhaps most interestingly, the authors claim that there is no known natural system where splintered phase waves and active phase waves occur. Thus, this paper provides an interesting lead for experimentalists searching for new patterns in nature.

[1] Mathematical modeling of frogs

2018-06-132018-06-11

Imagine you are a Sea Slug Larva…

Original paper: Individual-based model of larval transport to coral reefs in turbulent, wave-driven flow: behavioral responses to dissolved settlement inducer

Lost, alone, and buffeted by ocean currents: this is the beginning of life for many oceanic larvae. These tiny organisms, often only 100 micrometers in diameter, must seek a suitable new habitat by searching over length scales thousands of times their own. But searching for something you can’t see while being dragged this way and that by ocean currents can’t be easy. How do these microscopic creatures make sense of the turbulent world around them and find their home?

larvaandslug — Figure 1: The larval form (left) is about 100 micrometers in diameter and swims using the beating hairs on the stumps on at the top of the cell, whereas and the adult sea slug form (right) can grow up to 5 cm in length and stays on the coral. Left figure is taken from Koehl et. al. The right figure is taken from http://www.seaslugforum.net/find/pheslugu.

To answer this question, today’s paper studies a species of sea slug, Phestilla sibogae. These sea slugs have two forms, the baby larval form (Fig. 1 left), which travels through the ocean, and the adult sea slug form (Fig. 1 right), which lives and feeds on their coral prey. After they are born, the young larvae first swim toward light, instinctively leaving their parents’ reef. When they are old enough to settle down and become adults, they must search for a new reef to call home. The metamorphosis from larva to slug is only triggered when the larvae have settled on their coral prey.

The coral prey release a chemical that the sea slug larvae can smell. The chemical acts as an on-off switch for the larva. When there is no chemical, the larva swims in a straight path in a random direction at 170 micrometers per second. Upon encountering a strong enough chemical smell, the larva stops swimming and sinks at 130 micrometers per second. We know how the larva move but how does this movement affect how many and how quickly the larvae make it to the reef? To understand the larvae transport, we need to understand the larval environment.

larvaeinsea — Figure 2: Larva cell (inset) in turbulent waters above the reef. The streaky pattern is from measurements made by the researchers in a wavy flow tank above a reef skeleton. Within the reef skeleton, a fluorescent dye is released. When the fluorescent dye is excited by a laser sheet it emits light. More light means more dye, where the dye represents the coral chemical [1]. The figure is taken from Koehl et. al.

Not only do the larvae have to swim while being buffeted by the wavy turbulent flow, but the waves also affect how the chemical released from the reef spreads. If the coral were in still water, then the amount of chemical would increase smoothly as you travel from the surface waters to the reef due to diffusion. However, the corals live in shallow water, where waves passing over the rough reef surface lead to turbulent waters above the reef. This complicated flow pattern means the chemical smell no longer smoothly increases as you travel towards to reef. Instead, the turbulence creates streaks of very high amounts of chemical and very low amounts of the chemical, as shown in Figure 2.

To investigate the transport of larvae to the reef, Koehl and coauthors build on previous work to create a computer simulation with both the larva swimming behavior and the larva environment based on experimental measurements. To model the background flow environment, they include the net flow, waves, and turbulence. The flow parameters are fit to experimental measurements made in wavy shallow waters in Hawaii [1]. In a similar way, the researchers use experimental measurements to model the swimming behavior of the larvae.

In their simulation, the researchers are able to alter both the environment and the larva swimming behavior and ask what, if any, advantage the on-off swimming behavior brings. The advantage is measured using the steady state larva transport rate, defined as the percentage of the larvae that make it to the reef each minute. With the steady-state approximation, the percentage of larvae that make it to the reef each minute is constant over time.

bothconcpatterns — Figure 3: Turbulent concentration pattern in A shows streaks of high and low concentration while the time-averaged concentration pattern in B smoothly increases towards the bottom of the reef. Figure adapted from Koehl et. al.

First, the researchers investigate whether or not the streakiness of the concentration pattern is an important factor in determining how many larvae reach the coral. When trying to understand how larvae reach the coral, previous researchers made the simplifying assumption that the concentration pattern of the chemical the larvae follow is smooth and uniform over time. As we saw in the streaky pattern in Figure 2, this is not a realistic assumption. But just how wrong is this it? To answer this question, the researchers compare the chemical distribution measured at a single moment in time (Fig. 3A) to the chemical distribution obtained by taking the average of the distribution measured at many different times. This averaging process produces a smoother distribution than would be seen in reality (Fig. 3B). On comparing the two different chemical distributions, the researchers find the larvae transport rates are overestimated by up to 10% in the unrealistic time-averaged environment.

Secondly, because the concentration pattern affects the transport rate, the on-off swimming behavior must affect the transport of the larvae to the reef. In their simulation, the transport rate for naturally swimming and sinking larvae is 45% per minute. The researchers test how the larva behavior affects this transport rate by separately turning off the swimming and sinking behavior of their simulated larva. If a larva sinks but does not swim, the transport rate changes to 20% per minute. If the larva swims but doesn’t sink, the transport rate changes to 25% per minute. Without their on-off switch, the larvae are reliant on the background flow or randomly swimming downwards to be transported to the coral.

From these transport rates, we can understand the relative importance of larval behavior and larval environment. For example, we now know that if the environment was no longer turbulent or if the larvae could no longer swim, the larvae’s rate of transport to the reef would change significantly. This impacts both how many larvae survive to adulthood and where in the ocean the adult sea slugs end up. Building on this work, predictions have also been made for many different species of larvae [2]. From these studies, we not only can get an idea of how local and global populations spread in their natural environments but also how a simple on-off process can help an organism to successfully navigate a complex environment.

[1] See https://academic.oup.com/icb/article/50/4/539/652640, for an overview of how researchers characterize the larva environment.

[2] See https://link.springer.com/article/10.1007%2Fs00227-015-2713-x for more details. Here, the researchers measure both the concentration of chemical and the flow above the reef simultaneously (as described in [1]). With this, they look more generally at the problem of settling on surfaces, investigating a variety of swimming properties and settling sites rather than a specific species.

2018-06-062018-06-05

Are squid the key to invisibility?

Original paper: Adaptive infrared-reflecting systems inspired by cephalopods

While many today would associate a “cloak of invisibility” with Harry Potter, the idea of a magical item that renders the wearer invisible is not a new one. In Ancient Greek, Hades was gifted a cap of invisibility in order to overthrow the Titans, whereas, in Japanese folklore, Momotar? loots a straw-cloak of invisibility from an ogre, a story which is strangely similar to the English fairytale Jack the Giant-Slayer. Looking to the future in Star Trek, Gene Roddenberry imagined a terrible foe known as the Klingons, a war-driven race that could appear at any moment from behind their cloaking devices – indeed, any modern military would bite your arm off to get hold of this kind of device. Clearly, invisibility is a concept that has captured minds across many cultures, genres, and eras, so it should be no wonder that scientists are working on making it a reality.

As is often the case in materials science, a good starting point for inspiration is to look to biology, after all, life has had billions of years of competition-driven evolution to craft its tools. To that end, Gorodetsky and his team at of the University of California, Irvine, have been attempting to replicate the master of disguise: the cephalopod. From this class of mollusk, squid and octopuses in particular excel at adaptively altering the color, texture, and patterning of their skin to camouflage against a wide variety of oceanic backdrops (see Figure 1). They accomplish this primarily by stretching and contracting pigment-containing skin cells. More importantly to this research, some species of squid have additional skin cells called ‘iridocytes’, which are structured reflective cells that resemble a microscopic comb or folded pleats. These folds reflect light at specific wavelengths that correspond to the fold size. By actively stretching and contracting these cells on-demand, the squid effectively becomes a self-modifying bioelectronic display.

SquidCamouflage (1) — Figure 1: Squid before (left) and after (right) deploying camouflage. Images reproduced from
a video by H. Steenfeldt under the YouTube Creative Commons Attribution license.

Following this technique of stretch-induced camouflage, the authors devise a technique for replicating some squid-like properties in an artificial material. The procedure consists of using electron-beam evaporation[1] to deposit an aluminum layer onto a stretched polymer film held under strain. When they release the strain, the metallically coated material shrinks and buckles to form microscopic wrinkles that are analogous to the structures in squid iridocyte cells. The polymer film of choice is an excellent proton conductor, so when mounted to electrodes, it can be stretched back to the flat state simply by applying a voltage.

When stretched, the aluminum coated film will reflect infrared light like a perfect mirror, whereas when wrinkled, the incoming light is reflected diffusively in multiple directions, like sunlight hitting the moon. To see this effect, the scientists shine infrared light – a heat source – at the material and position an infrared camera at a specific angle so that when flat, it reflects all the incoming radiation toward the camera and appears hot; when relaxed and wrinkled, much less of the light is scattered towards the camera, making the material appears to take on the thermal properties of the background and disappear from view.

Perhaps as a head-nod to their biological inspiration, the team then recreate this material in the likeness of a squid and watch as its infrared silhouette disappears from the camera’s view (Figure 2).

InfraInvisibleSquidShape_Fixed (1) — Figure 2: The flexible squid-shaped material, viewed under an infrared camera. When relaxed (left), very little infrared is reflected towards the camera, and it appears cold. When stretched (right), it reflects most incoming infrared light towards the camera and appears hot. Image edited from Fig. 5 in the manuscript.

The authors conclude that this ready-for-manufacturing material will have immediate applications in heat-regulating technology, and while it is currently limited to the infrared part of the spectrum, they also note that there is no reason why this technique couldn’t be adapted to the optical range.

Squid haven’t solved our desires for a cloak of invisibility just yet, but these mysterious creatures may hold more secrets than we realize. We would be wise to keep an eye on them … if we can.

[1] Electron beam evaporation is the technique of bombarding a solid metal with energetic electrons, causing it to evaporate. This metal vapor then cools and condenses uniformly on all nearby surfaces, forming a uniform metallic coating.

2018-05-302018-05-31

Mob mentality improves animal sensing

Original paper: Emergent Sensing of Complex Environments by Mobile Animal Groups

Imagine you forget to bring money for lunch, and you overhear a teacher mention that there is free pizza somewhere on the third floor of your school. If you’re alone, you might walk around the third floor, trying to detect signs of pizza – does a room smell delicious? Do you see a suspicious stack of pizza boxes by the door to the gym? Just by using your senses, you can find the pizza. However, it is likely that there are other students on the third floor who also want free food. Maybe if you follow a crowd of students all walking in the same direction and talking about whether they want a Hawaiian or pepperoni slice, they might lead you directly to the pizza!

Which of these methods will be more effective? Following environmental signals, such as the smell of cheese, or social signals, such as the people all heading in the direction of potential pizza? In “Emergent Sensing of Complex Environments by Mobile Animal Groups,” Andrew Berdahl and colleagues seek to find out how searching in groups enhances the sensing ability of animals.

The researchers used a fish called the golden shiner to study this kind of mob mentality. These fish live in large schools in shallow water and prefer darker habitats. Fish school together for many reasons. For example, it helps them avoid predators and gain advantages in hunting. In this experiment, the researchers asked whether schooling helps the fish find their preferred darker spots in the water. A school of golden shiners searching for dark spots in water is a convenient model system, but the researchers stressed that the results from this study can be applied to any group of organisms looking for any environmental cue.

Berdahl and his colleagues set up a large, shallow tank for fish to swim in. The tank was in a dark room, and a projector was used to impose light patterns on it. The patterns consisted of a bright tank (similar to an overcast day) with dark patches (similar to twilight). The dark patches moved around randomly at a constant speed, with the fish expected to follow the patches.

Fish were tested as individuals and as groups from 2 to 256 fish. To track the fish in both light and dark regions, the researchers used infrared (IR) light that the fish can’t see and took videos of the fish with an IR camera. The fish could then be tracked using image analysis. You can see the visible light and IR images of the fish in a dark spot in Figure 1.

How well the fish stayed in the dark was measured with a performance metric, $latex \Psi$ (psi). This number measures how good the fish are at staying in the dark. Specifically, it measures the average inverse of brightness at all the fish positions averaged over time [1]. If $latex \Psi = 1$, the fish did not try to stay in the dark at all; the performance was better as $latex \Psi$ increased. The data in Figure 2 shows that $latex \Psi$ increases with group size – bigger groups make the fish better at tracking dark patches.

Figure 2: Ability of fish to track the dark patches in the tank improves with group size. Points with error bars represent the averages over all the experiments at each group size, and the blue line is the statistical fit. The red line is the results from simulations implementing the rules of the collective behavior of the fish. Image adapted from Berdahl and colleagues’ original paper.

The researchers wanted to find out whether the fish were responding to the changes in the environment or the behavior of their neighbors. They calculated a social vector and an environmental vector for each fish. The social vector measures what direction a fish’s closest neighbors are. If all of the fish’s neighbors are to its left, there will be a strong social vector to the left; if the neighbors are all spread out around it the social vector will be very small [2]. The environmental vector points in the direction of the darkest position near the fish[3]. The researchers calculated how correlated each vector was with the acceleration of the fish. When the magnitude of the social vector was very high – a fish’s neighbors were all located in the same direction from it – the fish listened to the social vector and swam to where their neighbors were. They did not respond as strongly to what direction the nearest dark patch was in. In other words, fish respond much more to nearby clusters of neighboring fish than to their environment, similarly to how you might pay attention to your friends in your hunt for pizza rather than smelling around.

Although fish did not respond to the changes in the lighting of the tank directly, as measured by the environmental vector, they did respond to the environment: fish swam faster in lighter regions, and slower in darker ones. They responded to the scalar brightness at their position in the tank, rather than how much the brightness was changing. If a mountain climber behaved like these fish, he would climb more slowly at higher altitudes (responding to the elevation) but not change his speed based on the steepness of the slope (not responding to the gradient in elevation).

These two main behaviors of the fish, swimming towards their neighbors and changing their speed based on the lighting, made group tracking of dark patches very effective. The researchers highlight two examples of how this could work. The first example is that of fish traveling next to a dark patch. Some of the fish are located on the brighter side of the dark patch and swim faster. Other fish are in the darker region and swim slower. This causes the whole group to turn towards the slower fish and therefore into the dark patch. The second example is of a group of fish traveling into a dark patch. As fish enter the dark patch, they slow down. The rest of the group follows them and slows down as well. This increases the number of fish in darker regions.

The researchers created a computer model that simulated behaviors of fish with these two rules: following their neighbors and changing the speed according to the brightness of the light at their positions. Although they did not build in any explicit response to the changing light gradient, the groups of simulated fish responded the same as the real fish in the experiment, as seen in Figure 2. Berdahl and his colleagues conclude that the response of the fish to the environment arose simply from those two rules, and the ability of groups to track dark patches increased with larger numbers of fish.

The researchers emphasize many times that the results of this study are applicable to any group sensing any field, not just fish in a light field. The results could apply to bacteria seeking food, or robots seeking a resource to collect. If, for some reason, a group of animals is broken up – for example, there are fewer fish in a school due to overfishing – the remainder of the fish in the school might not be as well equipped to seek out darker patches to hide from predators. This study highlights the importance of paying attention to your neighbors and the advantages all living organisms gain from working in groups – like helping a hungry student find some pizza!

[1] The performance metric is defined as:
$latex \Psi=\frac{\langle \langle 1-L \rangle_{fish}\rangle}{\psi_{null}}$
where L represents the light level and $latex \psi_{null}$ normalizes $latex \Psi$ so that $latex \Psi = 1$ implies that fish do not track the light at all. The inner angle brackets represent the average of the darkness, $latex 1- L$, taken over all fish, and the outer angle brackets represent the average taken over all time

[2] The social vector is defined for each fish as:
$latex S_i=\sum \frac{c_j-c_i}{|c_j-c_i|}$
where ci represents the position of the ith fish, so cj–ci is the difference between the positions of the ith fish and its neighbor, the jth fish. It is normalized by the magnitude of that distance. The sum is taken over all the fish within seven body lengths of the ith fish, for each fish

[3]The environmental vector is the negative of the gradient of the light field for each fish, i:
$latex G_i = -\nabla L\mid_i$

2018-05-232018-05-21

Elastogranularity and how soil may shape the roots of plants

Original paper: Elastogranular Mechanics: Buckling, Jamming, and Structure Formation

How an elastic beam deforms under load has been a question for as long as there have been engineers to ask it. In some cases, the force on a beam is approximated as a single point. For example, if a diving board is large enough, a diver at the end can be treated as a point mass on the beam. Another common approximation is to consider the force to be a continuous pressure along its length. Treating wind that bends a tree branch as a continuous pressure along the branch’s length is much simpler than adding up the force from every molecule of air on the branch. However, consider the case of a root growing into a granular material like soil. As the root burrows through the soil it will bend due to varying point-like forces along its length. The result is a branching and twisting root system that tries to grow along the path of least resistance. An example of the diversity in plant root morphologies is shown in Figure 1 and gives a sense of how complicated and interesting the physics behind this growth can be.

Figure 1. An example of the complex and diverse morphology of several types of plants which all live in the same ecosystem [1]

With this in mind, David J. Schunter Jr. et al. from the Holmes group at Boston University have developed a beautiful experiment to study what they call “elastogranular” phenomena. In their experiment, an elastic beam is inserted into a box which is filled at a particular density with uniform beads. An image of a typical experiment is shown in Figure 2. Once the beam reaches the end of the box it will not be able to penetrate any further, and trying to push more of the beam into the box will cause a compression along the beam’s length. This resembles a classic experiment where a beam is compressed in the same manner, but in the absence of beads. Compressing the beam against the end of the box becomes increasingly difficult until eventually the beam “pops” into one large buckle. In this simpler case, the beam was free to buckle with no restrictions. Introducing beads to the system reinforces the beam non-uniformly and constrains the shapes it can take on when it buckles. This complicates the buckling event and leads to interesting new behaviors.

Screenshot 2018-05-11 at 6.13.31 PM.png — *Figure 2*. An elastic beam is inserted into a box of length $latex L_{0}$ and width $latex 2W_{0}$ filled with beads at an initial packing fraction of $latex \phi_{0} = 0.89$. After a length of beam is inserted equal to $latex L_{0}$, inserting additional length $latex \Delta$ results in buckling. In this experiment the beam takes on two buckles with wavelength $latex \lambda$, and amplitudes of $latex A_{0}$ and $latex A_{1}$ respectively.

In the experiment performed by the Holmes group, a beam is compressed against the end of the box until the beam buckles. If the packing fraction $latex \phi_{0}$ (the fraction of space within the box that is covered in beads) is low, it buckles much like one would expect in the absence of beads—one large buckle, as in Figure 3i. If $latex \phi_{0}$ is higher at the beginning of the experiment, like in Figures 3ii and 3iii, the buckling behavior becomes more complicated. The beam will form one large buckle as before, and as the buckle grows it will take up more area on one side of the box. This forces the beads to reorganize themselves, and the beads on the compressed side of the box become very tightly packed. At this point, they are in a hexagonal arrangement, and they are said to have crystallized [2]. As the beads in one side crystalize, that side becomes stiffer and suppresses further growth of the amplitude of the first buckle, $latex A_{0}$. In order to accommodate the extra length being inserted, $latex \Delta$, an additional buckle forms. The difference in buckling behavior for three values of $latex \phi_{0}$ are shown in Figure 3. [3]

**Figure 3.** Shape profiles of the inserted rod for various packing fractions ($latex \phi_{0}$) for the same normalized inserted length $latex \Delta/L_{0}$. i) At low $latex \phi_{0}$ the beam forms one large buckle. ii) As $latex \phi_{0}$ increases, the amplitude of the single buckle is suppressed, leading to a second buckle forming on the other side. iii) At even higher $latex \phi_{0}$, the buckles rotate and grow toward each other.

Figures 3 shows that not only are the number and amplitude of buckles significantly affected by the beads that surround the beam, but the orientations of the buckles are changed as well. When the experiment begins at a high $latex \phi_{0}$, the beam finds both sides of the box to be stiff and hard to penetrate. The initial buckle does not grow very much before the second buckle forms, and at high enough $latex \phi_{0}$ both buckles occur nearly simultaneously, forming a twin buckle. Figure 4 shows two systems with different $latex \phi_{0}$ forming twin buckles. In the top sequence where $latex \phi_{0}$ is lower, the buckles maintain a constant distance between each other as they grow since there are plenty of uncrystallized areas (light blue circles) ahead of the buckles into which they can grow. The lower sequence shows that, at higher $latex \phi_{0}$, the majority of uncrystallized beads are found in the wake of a buckle so the buckles instead grow into these regions, as demonstrated by the red lines.

*Figure 5*. Twin buckles increase in amplitude as $latex \Delta$ increases (left to right) growing into less-dense, uncrystallized regions (light blue circles). For lower $latex \phi_{0}$ (top sequence), uncrystallized beads in front of the buckles can be pushed aside, crystallizing beads away from the buckles (dark blue and yellow circles). At higher $latex \phi_{0}$ (lower sequence), the uncrystallized sections occur behind the buckles, causing the two buckles to grow closer together which is shown by the red lines.

This system bears a striking resemblance to that of plant roots growing into the soil and could be useful in understanding how environmental pressures cause plant root systems to evolve. For example, cacti need to absorb as much water as they can from their environment. One way of accomplishing this is to increase the surface area of the root system by growing wide and close to the surface, rather than deep, in order to collect water from a larger area. By developing thin roots that buckle before they can deeply penetrate the soil, many cacti are able to produce the shallow, wide-reaching roots system they need to find water.

David J. Schunter Jr. and coworkers have shown that combining two well-understood problems—buckling of a beam and reorganization of beads—can lead to unique and interesting bending dynamics. By confining a beam to a box of beads, the buckling of the beam becomes strongly influenced by the packing fraction and reorientation of the beads. This particular system shows a strong resemblance to plant root growth, but also be informative for synthetic applications involving the insertion of flexible filaments into deformable materials.

[1] Michigan Natural Shore Partnership, http://www.mishorelinepartnership.org/plants-for-inland-lakes.html

[2] When spheres crystallize in two dimensions, the hexagonal lattice is the closest possible packing with an area fraction of $latex \phi = 0.9069$. Interestingly, Figure 3iii shows a packing fraction of 0.91, which is higher than this maximum value. This is because the beads are able to pop out of the plane at very high compressions, which can lead to a calculated packing fraction larger than that of the hexagonal lattice. For more information on hexagonal packing, see Wikipedia.

[3] For more information about the specifics of how the deformation of the beam is quantified, a summary of the analysis is available here.

2018-05-162018-05-15

Anti-biofilm Material to Fight Bacterial Formation on Surfaces

Original paper: Sodium Dodecyl Sulfate (SDS)-Loaded Nanoporous Polymer as Anti-Biofilm Surface Coating Material

Are you afraid of visiting the dentist? If so, you’re probably not the only one, but unfortunately we can’t avoid it. Yearly dental check-ups are necessary to prevent tooth and gum infections. Dentists use a disturbingly sharp, noisy tool to remove dental plaque from the tooth surface. Dental plaque is caused by bacteria, and it is an example of a biofilm, which is a community of bacterial cells that stick to surfaces. Biofilms can be found everywhere, especially on wet surfaces. Biofilms cause health problems for millions of people worldwide every year, primarily because of infections during surgery or consumption of contaminated packaged foods. To prevent these problems, some scientists are developing surface coatings that will prevent biofilm formation in the first place. In this week’s paper, we will learn about a new technique for creating a microscopic “shield” against the formation and growth of biofilms.

A biofilm is a complicated microscopic world in which multiple bacterial species can coexist. Most parts of the biofilm are covered by a protective sticky slime that is produced by the bacteria themselves. It is now known that bacteria communicate with each other within the biofilm by exchanging small molecules, proteins, genes, and even electrical signals. This intercellular communication results in expression of specific genes throughout the bacterial community in response to the environment. As a result, bacteria in biofilms are able to quickly develop resistance to antibiotics, making the treatment of biofilm infections extremely challenging. Therefore, one of the most common ways of destroying biofilms is a mechanical removal by scraping. This explains why we can’t avoid going to the dentist at least once per year; a toothbrush is not strong enough to remove the biofilm layer that we know as dental plaque.

E.coli-colony-growth.gif — Figure 1. A culture of *E. coli* growing. Video courtesy of *en.wikipedia.org*.

Unfortunately, scraping is not always possible; especially in cases where biofilms are formed at surfaces inside the body or on tiny surgical and industrial tools. Li Li and co-workers from the Technical University of Denmark, in collaboration with the Nanyang Technological University in Singapore, developed a coating material with the ultimate goal of preventing the growth of multiple bacterial species. This material has a structure with many nanoscale pores (holes) able to load and release antimicrobial compounds. For this study, it was filled with a detergent compound that is part of household cleaning products and is known to kill bacteria by dissolving the bacterial cell membrane. The researchers loaded the nanoporous polymer films with the detergent and placed the films in contact with Escherichia coli (E. coli) biofilms. Before showing what happened to the E. coli biofilms, let’s discuss a little bit more about the nanoporous polymer film.

figure 1 gyroid — Figure 2. The periodic gyroid structure of the nanoporous polymer films. Video courtesy of en.wikipedia.org.

The nanoporous film used in this study is made of a polymer that has two hydrophobic (water-repelling) chains, one of which is a silicon-based material that we use in contact lenses. The polymer chains self-organize into the beautiful gyroid structure shown in Figure 2, which is a 3D interconnected surface that repeats in three directions (is triply periodic) and contains no straight lines. The most beneficial part of this structure is that it forms small, nanoscale pores after the removal of the silicon-based chains, which provide large storage space for the detergent molecules. To stabilize the final structure used in this study, the researchers add a chemical compound to remove the silicon-based chains. At this step, the interconnected polymer chains form strong covalent bonds with each other, a process called cross-linking. Figure 3 shows the process of the nanoporous film preparation (a, b) and loading of the detergent (c, d) (to learn more about the preparation, see [1]).

figure 2 nanoporous film — Figure 3. Representation of making the nanoporous polymer film and loading it with detergent by diffusion: (a) the block copolymer re-organizes into a gyroid structure, (b) the silicon-based polymer chains are removed from the nanoporous film, (c) The detergent solution is in contact with the nanoporous film and the detergent molecules attach to the pore walls (the enlargement shows that excess free detergent molecules may form small spheres between the walls), (d) the final nanoporous film loaded with detergent (red color represents the detergent layer). (Image adapted from Li Li’s paper).

What happened to the E. coli communities after being in contact with the nanoporous film loaded with detergent? The researchers tested three samples of films differing in thickness (0.5mm, 1mm, and 1.5mm). They took microscopic images after two days and after seven days of contact with the bacteria. These specific periods were chosen because it is known that within three days almost 70% of the detergent can be released from the nanopores. To compare, they also included a nanoporous surface without detergent, which is shown in Figure 4, parts A and F. On the samples without detergent the bacteria were free to grow into large biofilms. The results in Figure 4 show the astonishing difference between the biofilms with, and without contact to the detergent after two days. Only a few small areas of live bacteria (green spots) were visible on the films with detergent, and even some dead bacteria were visible (red spots). The nanoporous surface worked! In addition, thicker nanoporous surfaces were even more effective against biofilm growth, because they have more pores loaded with detergent.

figure 4 fixed — Figure 4. Images of the 2-day (A–E) and 7-day (F–J) biofilm formation by Escherichia coli on nanoporous films with (B–E, G–J) and without (A, F) detergent. Green and red cells correspond to live and dead cells, respectively. (Image adapted from Li Li’s paper).

The tests after seven days were not as successful for the thinner films, which means most of the detergent was released from these films in less than seven days. Interestingly, the thickest nanoporous film was still effective at preventing biofilm growth after seven days. The researchers also tested the material on biofilms made by another type of bacteria, Staphylococcus epidermidis, which has a different type of cell wall. The results were not successful, and the biofilm kept growing, showing that the particular detergent is not effective in killing this type of bacteria. This shows the challenges researchers are facing, such as releasing antimicrobial compounds for longer periods of time and preventing the growth of specific bacterial species.

To conclude, this study showed that these gyroid nanoporous surfaces are effective in delivering detergent to prevent the formation and growth of E. coli biofilms. The researchers recommend further experiments with different types of detergents to target more species of bacteria. Of course, we can’t use detergents for applications in the body (detergents are highly toxic), but it is possible these nanoporous films could be used to deliver other non-toxic, antibacterial molecules. The research on the fight against biofilms keeps going! But you still have to visit your dentist every six months.

Continue reading “Anti-biofilm Material to Fight Bacterial Formation on Surfaces”

2018-05-10

Fast Flow in Tiny Tubes

Original Paper: Massive radius-dependent flow slippage in carbon nanotubes

Water is made up of many many molecules of $latex H_2O$. But when you drink it from a glass or take a shower, this doesn’t matter. A typical fluid is so much bigger than an individual molecule that you can just treat it as a continuum: if you shoot water out of a hose with some initial velocity, you can use physics to figure out where the water will land without having the consider the motion of all the molecules. Considering each molecule would get the same result, but would be a vastly more difficult calculation. However, when the fluid is very small, not much larger than the size of individual molecules, then the molecular nature of water starts to matter. The “continuum breakdown” is an intriguing aspect of fluid mechanics and physics in general, but is typically very hard to study experimentally. Recently, a group of researchers based in France overcame these difficulties and managed to study water flowing through carbon nanotubes (Figure 1).

Experiment schematic — **Figure 1:** Left: an electron microscope image of a carbon nanotube inside a glass microcapillary. Right: a cartoon of water flowing out of the tube. Adapted from Figure 1.

When does it matter that the fluid is made of molecules? One of the implications of the molecular nature of a liquid has to do with what happens when it flows along a wall (e.g. inside a pipe). There is an assumption that the friction between the wall and the fluid will halt the flow right next to the walls, and the fluid will increase in speed farther from the walls. This is called the “no-slip” boundary condition and is a pretty central concept in fluid mechanics that makes the relevant equations much simpler (Figure 2). It is known that this condition is not perfectly true at the molecular scale. There may still be some net motion of fluid right next to the walls leading to slightly faster flow than expected from no-slip conditions because the lack of zero-velocity fluid would mean the average flow rate is higher. The exact nature of the boundary condition depends on the interaction between the molecules of the fluid and the molecules of the wall, and this doesn’t matter when the fluid is so much bigger than the region near the interface.

no slip cow — **Figure 2:** The slower wind speeds near the surface of the cow are an example of no-slip boundaries in action, where friction between the surface and the air causes the air to slow down. Yes, this was the best demonstration I could find of this.

The ideal way to investigate how molecular interactions affect flow near an interface would be to send fluid through something that is both very narrow and also very long compared to its width (narrow so that a large fraction of the molecules are near the wall, long so that the molecules spend more time in the tube and can be studied more easily). A water molecule is about 0.1 nanometers wide, so ideally the potential conduit wouldn’t be too much wider (for scale, a human hair is about 50,000 nanometers thick). Carbon nanotubes, which are one atom thick like graphene but wrapped into a cylinder instead of a sheet, are pretty close to the ideal: the skinniest is as small as a few nanometers in diameter and up to thousands of times as long. Of course, “just flow water through a carbon nanotube” is easier said than done.

The way to do this is to insert it into a slightly-bigger-but-still-small tube, which in this experiment was done with a glass capillary, which, being from the Latin word capillus meaning hair, is a tube so narrow it looks like a hair. A sharp tip was used to pull a single carbon nanotube out of a big tangled mess of carbon nanotubes called a “forest” (this is how they are arranged when you buy them). Then, the researchers carefully inserted the nanotube into the narrow end of the glass capillary, and the gap between the nanotube and the capillary was sealed. All of this was monitored in real time using a microscope (Figure 3). I recommend reading the Section 1 of the Supplemental Methods, it’s quite fascinating and not too technical. There is also a video of it here.

I met one of the authors of the paper, Derek Stein of Brown University when I visited his lab in 2015. He showed me a prototype of this experiment, and ribaldly described the process of inserting the carbon nanotube into the glass capillary as “nano-sex.” My father is a urologist and might describe this as the world’s smallest catheterization.

SWNT insertion — **Figure 3:** A carbon nanotube was pulled from a “forest” and then inserted into a glass capillary tube. Adapted from Supplementary Figures 1 and 3.

Once the tiny tube (the carbon nanotube) was hooked up to the less-tiny tube (the glass capillary), it was simply a matter of connecting two fluid reservoirs with the composite tube, applying pressure to one side, and measuring the rate at which water flows into the other side. To do this, they put fluorescent beads in the water and observed their motion near the exit of the tube. From the speed of the beads near the tube exit, they were able to figure out how fast the fluid must be flowing (Figure 4).

SWNT flow — **Figure 4:** Top: A microscope image of the nozzle and some beads (I have enhanced the contrast to make the beads more visible. The big black thing at the top right is probably gunk on the microscope or camera). Bottom: The speeds of the beads (no rhyme intended) at different points around the nozzle, as determined from their motion over time. Adapted from Supplementary Figure 9.

Then, by examining the relationship between applied pressure, the measured flow velocity, and the geometry of the nanotubes, the researchers were able to measure something called the fluid permeability of the system. This quantifies how well fluids can flow through the system, analogous to the electrical conductivity of a metal. Since it is known how a fluid behaves in a tube of a given radius with perfect no-slip conditions, the team compared their measurements to those expectations. What they found was that for larger nanotubes, the results were fairly consistent with no-slip, but as the tubes got smaller and a higher proportion of the water molecules come into contact with the interface, the fluid flowed a lot faster than expected (Figure 5). In the smallest tubes, it flowed 25 times faster than expected. The velocity at the walls was not actually zero, and the flow rate was consistent with a tube with twenty times larger in diameter than the one that was actually used — a big enough result to title the paper: Massive radius-dependent flow slippage in carbon nanotubes.

no slip vs. slip — **Figure 5:** We treat fluids as if they flow like on the left, but in carbon nanotubes, it was more like on the right.

Why does the interaction between carbon and water lead to such massive slipping? This isn’t actually known, but at the atomic scale, friction is due to electrical interactions between the atoms that make up the nanotubes and the water. Carbon nanotubes are fairly conductive, meaning the electrons aren’t that strongly bound to atomic nuclei. The authors hypothesized that an insulating tube with the same chemical structure as a carbon nanotube would have different flow properties. Fortunately, such a thing does exist: boron nitride nanotubes. They did the same type of experiment with the insulating tubes and found that the water flowed much slower than the version with the conducting carbon nanotubes. This actually surprised them— they expected a difference, but not such a big one and they had no explanation for it:

“That these nearly identical channels exhibit very different surface flow dynamics is unexpected… simulations predict that the friction of water on carbon surfaces is lower than on boron nitride surfaces, but even these predictions strongly underestimate the difference observed here.”

Traditional solid-state physics, which deals with the electronic and magnetic properties of crystalline materials, doesn’t usually intersect with soft condensed matter physics, which deals with flowy squishier things. This experiment, showing that the way a fluid flows through a tube depends on the electrical properties of the tube, is taking a step towards bringing them together, even though its results aren’t yet fully explained.

2018-05-022018-05-02

3 Easy Steps to (almost) Curing Type 2 Diabetes

Original paper: Synthetic beta cells for fusion-mediated dynamic insulin secretion

Type 2 diabetes currently affects ~ 410 million people worldwide. It is a chronic condition caused by dysfunctioning beta cells in the pancreas. Beta cells normally secrete insulin in the pancreas to regulate blood glucose levels, and the loss of beta cell function can lead to hyperglycemia (i.e. high blood sugar), a condition with complications such as blindness and heart disease. The traditional invasive treatment involving direct insulin injection is a painstaking, never-ending process as it doesn’t properly regulate the dynamics of beta cells, just treats the symptoms. Modern treatments involve cell therapy in which functioning beta cells are transplanted from a healthy person, but this therapy faces serious challenges such as finding the right donor and suppressing the immune system after the transplantation. In this post, you will read how Chen and co-workers design an artificial version of beta cells that bypass the shortcomings of conventional cell therapy.

In our body, beta cells in the pancreas are responsible for monitoring and balancing our blood sugar level. When glucose levels are low (hypoglycemia, low sugar levels), these cells rapidly secret a polymeric (see note [1]) form of glucose. With high glucose levels (hyperglycemia) a hormone called insulin is secreted to bring down the concentration of sugar in the blood. Any disturbance to these cells, either through the body attacking itself (Type I diabetes, see note [2]) or genetic risk factors, can compromise the function of the beta cells, resulting in hypo- or hyperglycemia.

A4-fig1 — **Figure 1.** The schematic of the vesicle-in-vesicle system. The giant micron-sized lipid vesicle (the OLV) encapsulates the machinery components for the insulin release, including the smaller vesicles that carry the insulin (ISVs). The image is taken from Chen and colleagues.

In this study, the researchers mimic the cell’s machinery in beta cells that sense sugar and signal a response inside the cell. This artificial system features two differently sized lipid vesicles (see Figure 1). The larger vesicle is about a micron in size (millionths of a meter) and is called the outer layer vesicles (or OLVs). It acts as the body of the artificial beta cell, encompassing the necessary machinery to regulate the insulin release. The second, smaller lipid vesicles, a thousand times smaller than the OLVs, are held inside the OLVs and are thus called inner layer small vesicles, or ISVs. These ISVs encapsulate the insulin hormone inside.

The system acts like a computer code with a conditional “IF” command to decide whether it needs to respond or stay inactive. IF the glucose levels outside of the OLVs are normal or below normal, no signal is induced. However, IF the glucose level increases beyond the signal-inducing concentration (which can be easily tuned by chemical modification, see below) then the signal is triggered, resulting in insulin release. The entire system consists of machinery to perform three distinct steps.

The first step is the glucose sensing step. Using a glucose transporter membrane protein, the OLVs sense and absorb the glucose from their surroundings. Next, the uptaken glucose is converted into protons using two enzymes that are inside OLVs (see note [3]). Changing the concentration of protons in a liquid alters its pH. This variation in the pH of the microenvironment inside the OLVs initiates the second process.

The second step is the response. For this step to proceed, the ISVs need to get close to the inner wall of the OLVs. The surface of the ISVs, however, is decorated with giant linear molecules that prevent the ISVs from getting close to the OLVs’ inner wall. But, with a high glucose concentration, the environment inside the OLVs becomes acidic, as described above. Under acidic conditions, the ISVs’ protective coating is engineered to leave the surface of ISVs, and this step is called the de-shielding step. Now ISVs close to the inner wall of the OLVs can merge or “fuse” with the OLVs. However, the two vesicles will still not reliably fuse together, so the researchers implement an active fusion mechanism (see below).

The third step is the release of the insulin. Remember that the nanosized vesicles are already loaded with insulin. The authors use two complementary DNA strands: one on the surface of ISVs (pink strands in Figure 1) and the other one on the inner wall of the OLVs (red strands in Figure 1). These complementary strands are like a key and lock that only open when the right key is inserted in the right lock. When the environment is acidic, the ISVs are free (de-shielded happens) to reach to the inner wall of the OLVs and through the DNA strands, ISVs bind to the wall. When this binding happens, the fusing event follows. Upon the fusion of ISVs to OLVs, the insulin is released.

When the surrounding glucose levels decrease, fewer protons are created inside the OLVs, and a second membrane protein called Gramicidin A, which is constantly working to expel protons from the OLVs, can balance the pH inside the OLVs. When the pH becomes neutral, the giant linear protective molecules that were floating around when media was acidic find the ISVs and re-stick to them. Thus the cascade of events of glucose sensing, deshielding, and insulin release then ceases once the pH returns to the point that the deshielding doesn’t happen.

fig2 — **Figure 2.** The insulin release as a function of time in diabetic mice treated with artificial beta cells and the control groups. The figure is adapted from Chen and colleagues.

To test how their system actually responds in a biological medium, the authors apply a gel under the skin of mice that contains the OLVs. For a group of diabetic mice, this artificial beta cell system showed a significant effect on the measured insulin levels in the mice blood. For control groups; (i) with no insulin ($latex A \beta C_{no insulin}$), (ii) with no lipid fusion system $latex A \beta C_{PK/PE}$ (PK and PE are DNA molecules that mediate the fusion process), and (iii) with no glucose sensing machinery $latex A \beta C_{no GSM}$, the insulin release was minor over the course of 10 days (see Figure 2). But when the insulin-loaded artificial cells were administered, the mice’s insulin levels increased remarkably over the control cases.

All in all, Chen and colleagues manage to release insulin in a controlled manner. There’s no need to evade an organism’s immune system–the OLVs don’t provoke an immune response. There’s also no need to inject insulin–it’s released automatically when needed. This work gives hope of drastically improving the lives of the nearly half a billion people worldwide suffering from diabetes.

[1] You might be asking: why do pancreatic cells secrete a polymeric form of sugar in response to low blood sugar level?! Well, which one is faster and more effective to you? Releasing one-by-one a single sugar, or releasing one-by-one a bag full of sugar molecules (the polymeric form). When this polymer leaves the cell quickly, it bursts (dissociates) into single sugar molecules, later to be absorbed by relevant cells.

[2] Under some circumstances that might be due to genetics, the body’s immune system attacks the beta cells and destroys them. These are called autoimmune disorders.

[3] Glucose oxidase (GOx) and catalase (CAT) are working in parallel to transform the glucose signal into protons. GOx, with the help of an oxygen molecule, converts the glucose to gluconic acid releasing a proton. But there is a by-product of this reaction which is not favored. The hydrogen peroxide ($latex H_{2}O_{2}$) produced is very active that can mess up all the molecules inside the giant vesicles. With a nice trick, the researchers simultaneously convert the hydrogen peroxide to oxygen by adding CAT enzyme. Now, this is feeding two birds with one seed. Getting rid of ($latex H_{2}O_{2}$) while providing the oxygen for the GOx to do its job.