The Nereis virens worm inspired new research out of the MIT Laboratory for Atomistic and Molecular Mechanics. Its jaw is made of soft organic material, but is as strong as harder materials such as human dentin. Photo: Alexander Semenov/Wikimedia Commons

A new material that naturally adapts to changing environments was inspired by the strength, stability, and mechanical performance of the jaw of a marine worm. The protein material, which was designed and modeled by researchers from the Laboratory for Atomistic and Molecular Mechanics (LAMM) in the Department of Civil and Environmental Engineering (CEE), and synthesized in collaboration with the Air Force Research Lab (AFRL) at Wright-Patterson Air Force Base, Ohio, expands and contracts based on changing pH levels and ion concentrations. It was developed by studying how the jaw of Nereis virens, a sand worm, forms and adapts in different environments.

The resulting pH- and ion-sensitive material is able to respond and react to its environment. Understanding this naturally-occurring process can be particularly helpful for active control of the motion or deformation of actuators for soft robotics and sensors without using external power supply or complex electronic controlling devices. It could also be used to build autonomous structures.

“The ability of dramatically altering the material properties, by changing its hierarchical structure starting at the chemical level, offers exciting new opportunities to tune the material, and to build upon the natural material design towards new engineering applications,” wrote Markus J. Buehler, the McAfee Professor of Engineering, head of CEE, and senior author of the paper.

The research, recently published in ACS Nano, shows that depending on the ions and pH levels in the environment, the protein material expands and contracts into different geometric patterns. When the conditions change again, the material reverts back to its original shape. This makes it particularly useful for smart composite materials with tunable mechanics and self-powered roboticists that use pH value and ion condition to change the material stiffness or generate functional deformations.

Finding inspiration in the strong, stable jaw of a marine worm

In order to create bio-inspired materials that can be used for soft robotics, sensors, and other uses — such as that inspired by the Nereis — engineers and scientists at LAMM and AFRL needed to first understand how these materials form in the Nereis worm, and how they ultimately behave in various environments. This understanding involved the development of a model that encompasses all different length scales from the atomic level, and is able to predict the material behavior. This model helps to fully understand the Nereis worm and its exceptional strength.

“Working with AFRL gave us the opportunity to pair our atomistic simulations with experiments,” said CEE research scientist Francisco Martin-Martinez. AFRL experimentally synthesized a hydrogel, a gel-like material made mostly of water, which is composed of recombinant Nvjp-1 protein responsible for the structural stability and impressive mechanical performance of the Nereis jaw. The hydrogel was used to test how the protein shrinks and changes behavior based on pH and ions in the environment.

The Nereis jaw is mostly made of organic matter, meaning it is a soft protein material with a consistency similar to gelatin. In spite of this, its strength, which has been reported to have a hardness ranging between 0.4 and 0.8 gigapascals (GPa), is similar to that of harder materials like human dentin. “It’s quite remarkable that this soft protein material, with a consistency akin to Jell-O, can be as strong as calcified minerals that are found in human dentin and harder materials such as bones,” Buehler said.

At MIT, the researchers looked at the makeup of the Nereis jaw on a molecular scale to see what makes the jaw so strong and adaptive. At this scale, the metal-coordinated crosslinks, the presence of metal in its molecular structure, provide a molecular network that makes the material stronger and at the same time make the molecular bond more dynamic, and ultimately able to respond to changing conditions. At the macroscopic scale, these dynamic metal-protein bonds result in an expansion/contraction behavior.

Combining the protein structural studies from AFRL with the molecular understanding from LAMM, Buehler, Martin-Martinez, CEE Research Scientist Zhao Qin, and former PhD student Chia-Ching Chou ’15, created a multiscale model that is able to predict the mechanical behavior of materials that contain this protein in various environments. “These atomistic simulations help us to visualize the atomic arrangements and molecular conformations that underlay the mechanical performance of these materials,” Martin-Martinez said.

Specifically, using this model the research team was able to design, test, and visualize how different molecular networks change and adapt to various pH levels, taking into account the biological and mechanical properties.

By looking at the molecular and biological makeup of a the Nereis virens and using the predictive model of the mechanical behavior of the resulting protein material, the LAMM researchers were able to more fully understand the protein material at different scales and provide a comprehensive understanding of how such protein materials form and behave in differing pH settings. This understanding guides new material designs for soft robots and sensors.

Identifying the link between environmental properties and movement in the material

The predictive model explained how the pH sensitive materials change shape and behavior, which the researchers used for designing new PH-changing geometric structures. Depending on the original geometric shape tested in the protein material and the properties surrounding it, the LAMM researchers found that the material either spirals or takes a Cypraea shell-like shape when the pH levels are changed. These are only some examples of the potential that this new material could have for developing soft robots, sensors, and autonomous structures.

Using the predictive model, the research team found that the material not only changes form, but it also reverts back to its original shape when the pH levels change. At the molecular level, histidine amino acids present in the protein bind strongly to the ions in the environment. This very local chemical reaction between amino acids and metal ions has an effect in the overall conformation of the protein at a larger scale. When environmental conditions change, the histidine-metal interactions change accordingly, which affect the protein conformation and in turn the material response.

“Changing the pH or changing the ions is like flipping a switch. You switch it on or off, depending on what environment you select, and the hydrogel expands or contracts” said Martin-Martinez.

LAMM found that at the molecular level, the structure of the protein material is strengthened when the environment contains zinc ions and certain pH levels. This creates more stable metal-coordinated crosslinks in the material’s molecular structure, which makes the molecules more dynamic and flexible.

This insight into the material’s design and its flexibility is extremely useful for environments with changing pH levels. Its response of changing its figure to changing acidity levels could be used for soft robotics. “Most soft robotics require power supply to drive the motion and to be controlled by complex electronic devices. Our work toward designing of multifunctional material may provide another pathway to directly control the material property and deformation without electronic devices,” said Qin.

By studying and modeling the molecular makeup and the behavior of the primary protein responsible for the mechanical properties ideal for Nereis jaw performance, the LAMM researchers are able to link environmental properties to movement in the material and have a more comprehensive understanding of the strength of the Nereis jaw.

The research was funded by the Air Force Office of Scientific Research and the National Science Foundation’s Extreme Science and Engineering Discovery Environment (XSEDE) for the simulations.

Link to audio file (22:01)

In this episode, we speak with Ramon Pericet and Michal Dobrzynski from EPFL about their Curved Artificial Compound Eye (CurvACE) published in the Proceedings of the National Academy of Sciences. Inspired by the fly’s vision system, their sensor can enable a large range of applications that require motion detection using a small plug-and-play device. As shown in the video below, you could use these sensors to control small robots navigating an environment, even in the dark, or equip a small autonomous flying robot with limited payload. Other applications include home automation, surveillance, medical instruments, prosthetic devices, and smart clothing.

The artificial compound eye features a panoramic, hemispherical field of view with a resolution identical to that of the fruitfly in less than 1 mm thickness. Additionally, it can extract images 3 times faster than a fruitfly, and includes neuromorphic photoreceptors that allow motion perception in a wide range of environments from a sunny day to moon light. To build the sensors, the researchers align an array of microlenses, an array of photodetectors, and a flexible PCB that mechanically supports and electrically connects the ensemble.

This work is part of the European Project Curvace which brings together a total of 15 people from four partners in France, Germany and Switzerland.

You can read our full coverage here.

Ramon Pericet Camara
Ramon Pericet Camara is the scientific coordinator for the CurvACE project and a postdoctoral researcher at the Laboratory of Intelligent Systems at EPFL. His research interests are oriented towards bio-inspired robotics, soft robotics, and soft-condensed matter physics.

Ramon received a Masters degree in Physics in 2000 from the University of Granada (Spain) and a PhD in Multidisciplinary Research from the University of Geneva (Switzerland) in 2006. Subsequently, he was granted a fellowship for prospective researchers from the Swiss National Science Foundation to join the Max Planck Institute for Polymer Research in Mainz (Germany).

Michal Dobrzynski
Michal Dobrzynski is a PhD student at the Laboratory of Intelligent Systems at EPFL. He obtained his master degree in Automatic Control and Robotics in 2006 from the Warsaw Technical University (Poland). He then joined the SGAR S.L. Company (Barcelona, Spain) as a Robot and PLC Software Engineer where his work focused on industrial robots and automatic lines programming and visualization. Next, in 2007, he joined a Numerical Method Laboratory at the University Politechnica of Bucharest (Romania) where he spent two years working in the FP6 “Early Stage Training 3″ project as a Researcher.

Links:

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• Michal Dobrzynski’s Website
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First, these machines will necessarily have sensory components. Digital cameras and microphones are practically a given, but they may also have infrared imaging, radar and/or laser scanning, chemical sensors to provide something akin to a sense of smell, pressure/stress sensors for a sense of touch, probes for soil moisture, temperature, pH, O2 content, and nutrient availability, weather instruments, and some means of locating themselves very precisely relative to the boundaries of a field or other stationary reference. Compared to most machines, they will have available a rich collection of information about their environments, rich compared even with what human senses provide.

Next, they will have significant computer processing power, sufficient to take the data streams from all of these sensory devices, find patterns in them, compare them with each other and with historical data (including the exact position of every seed and when it was planted), create and update a real time 3-dimensional model of their immediate surroundings, locate items of interest within that model, choose a course of action, and send the detailed instructions to the machine’s moving parts, closely monitoring their progress.

Finally, they will have various moving parts, likely including high resolution or specialized sensory components that can be sent in for a closer look. Those moving parts might include a range of grips, from fine tweezers to something strong enough to uproot small trees, mechanical snips, lasers with enough power to fry a meristem, high-pressure water jets capable of slicing through the stem of a plant, fingers to move other plant material out of the way, a vacuum for sampling air at ground level or removing insects, sprinklers and sprayers, trowels of various sizes, and, of course, the soil probes mentioned earlier. Such tools might be combined into sets incorporated into units which could be plugged onto the ends of articulated arms and quickly switched out.

That’s a basic outline, but we need to return to the data processing hardware and the code it runs to fill out the picture, since it can make the difference between an expensive toy and a productive machine that more than pays for itself. A major task the processor must perform is resource scheduling, and to do that effectively it must sort actions into those that can be performed without moving anything massive (slow) and without switching out tool units, those which require either movement or a tool switch but must nevertheless be accomplished before moving on, those which can be left until a future pass over the same area but not indefinitely, and those which can be left undone unless it becomes convenient to do them. Efficient scheduling also means mapping the movement of even the smallest parts so they proceed smoothly from one thing to the next, without having to retrace their paths more than is unavoidable.

An important point to be taken away from the previous paragraph is that scrimping on computing hardware and software is likely to prove counterproductive, by reducing the overall capacity of the machine disproportionately. We should expect the computing components to represent a substantial fraction of the overall cost of the machine, and we shouldn’t be surprised if they also consume a substantial fraction of its energy budget. Better to invest an extra 10-20% to make a given physical machine capable of performing the work of two, and to invest 1 or 2 kilowatt-hours to save ten.

Something which should be apparent from this mental exercise as a whole is that what’s being proposed is largely a simple extrapolation of technologies which already exist. There are already mechanical arms and mechanical grips; there are already sensors and various means of controlling machine operation. What’s mainly missing is the software which would turn data streams into a 3D model in a horticultural context, choose what to do, schedule resources, and map out the details. That’s a lot left to be done, requiring a significant investment for a long term payoff, but it’s a fairly straightforward problem, and divisible into more manageable chunks. Let’s get to it!

Reposted from Cultibotics.

Machines can make use of senses we don’t possess or which are more sensitive than those we do. Their vision can extend into the infrared and ultraviolet, as well as more finely dividing the visible spectrum, and can also be more detailed and quicker (tracking faster motion) or more accurately track changes over a period of days or weeks. Their hearing can be far sharper than our own. They can be equipped with chemical sensitivity capable of distinguishing between substances we would group together under broad categories, like sweet or acrid. They can also be equipped with radar and sonar, laser ranging and scanning, accurate measures of temperature, humidity, and insolation, and their manipulators can be made to gauge and control pressure more accurately than do our own fingertips. In short, machines can have far better data available to them than would an unassisted human gardener in the same position.

Machines can also correlate information very quickly, drawing on recorded data and expert systems to make decisions, and applying heuristics to experience to refine those expert systems. A machine might reasonably be expected to identify to species every plant within the area it was tending, to know whether they were considered crops, benign, weeds, or threatened or endangered, and treat them accordingly. It might be expected to predict to an accuracy of a few days when it could harvest a particular crop, and estimate to within a few percentage points the quantity that could be expected, barring a calamity such as hail or a tornado. It might also be expected to adapt a cropping plan to market conditions, for example putting in more of some crop that hadn’t done well elsewhere and would therefore be in demand.

Machines can whisper to each other, via radio links, over distances far greater than a human shout will carry. They can coordinate their activities precisely, cooperating toward a common goal without so much as a hiccup.

Machines can, as has recently been demonstrated by DARPA’s autonomous vehicle competitions, operate in an uncontrolled environment.

The foregoing is intended as a glimpse of how it might work once development was far along. It presumes a mature technology, some of the pieces of which aren’t yet available or only just beginning to be so.

Reposted from Cultibotics.