The first commercial carrier fueled by recycled waste gas from a steel mill just flew across the Atlantic. In Japan, construction workers might get a break: Scientists there created a robot that can install drywall, among other building tasks. And in landlocked Indiana, researchers are looking to shrimp as an inspiration for more resilient building materials. It’s a real grab bag in this week’s most interesting scientific discoveries.
Unshellfishly, Scientists Share Findings On Bio-Inspired Building Materials
What is it? The shells of arthropods like beetles and lobsters get tougher under pressure — what if building materials could do the same? At Purdue University, researchers have been exploring ways to 3D-print a cement paste that might harness some of nature’s engineering techniques to benefit our own.
Why does it matter? These kinds of resilient materials could be used to earthquake-proof buildings, for instance, making them better able to withstand seismic shocks, as well as other infrastructure-stressing natural disasters.
How does it work? The Purdue team, which published its results in Advanced Materials, was inspired by the mantis shrimp, which boasts an appendage called a dactyl club that can withstand some pretty startling forces. (Here’s the shrimp punching through a quarter inch of glass to get at a tasty crab.) The internal structure of the shrimp’s limb is such that energy is absorbed and dissipates upon impact — a concept that, with 3D printing, scientists can now attempt to imitate. At Purdue, they printed their cement paste in an array of bio-inspired designs, each with a bespoke purpose: Their “Bouligand” structure, according to Purdue, “takes advantage of weak interfaces to make a material more crack-resistant,” whereas another pattern they came up with allows the cement to act like a spring, despite being made from brittle material. Jeffery Youngblood, a Purdue professor of materials engineering who worked on the project, said, “3D printing has removed the need for creating a mold for each type of design, so that we can achieve these unique properties of cement-based materials that were not possible before.”\
To Generate Electricity, Researchers Look To Superhydrophobia
Top and above: “The main idea behind this work is to create electrical voltage by moving ions over a charged surface,” the UC San Diego team explained in a press release. Image credit: Getty Images.
What is it? At the University of California, San Diego, researchers have created a superhydrophobic — that is, extremely water-repellant — surface that can be used to generate electricity. All it takes is water.
Why does it matter? The discovery could be used to power nanodevices, or — on a much bigger scale — water-desalination plants. With their proof-of-concept surface, the researchers were able to generate around 50 millivolts.
How does it work?“The main idea behind this work is to create electrical voltage by moving ions over a charged surface,” UC explained in a press release, “and the faster you can move these ions, the more voltage you can generate.” Under the direction of professor of mechanical and aerospace engineering Prab Bandaru, the team created a patterned surface by etching ridges into silicon substrate and filling the ridges with oil; the resulting material was so hydrophobic that it caused saltwater to move more quickly over it, facilitating a small charge. The researchers published their results in Nature Communications.
What is it? Meet HRP-5P, an autonomous humanoid robot, created by Japan’s Advanced Industrial Science and Technology laboratory (AIST) to complete simple construction tasks. Specifically, this bot is really good at installing drywall.
Why does it matter? Obviously this kind of automation could have a use anywhere, but AIST researchers quoted in Techcrunch observe that the need for it may be especially acute in Japan, a country that’s faced with a declining birth rate and an aging population: “It is expected that many industries such as the construction industry will fall into serious manual shortages in the future, and it is urgent to solve this problem by robot technology.” They hope that tech like this can free human workers to do more higher-value labor and less heavy lifting.
How does it work? In the video researchers released to accompany their findings (which are described here in Japanese), HRP-5P is able to use robotic object detection, environmental measurement and motion planning to perform tasks: It takes a look at its workspace, analyzes the terrain, then gets down to business. Using hooks in its hands, the bot picks up a piece of drywall and fits it against preinstalled joists, then uses another built-in tool to drill the sheet in place. Soon this robot house will be a robot home.
A New Low-Carbon Fuel Takes Flight
A Boeing 747 like this jet powered by four GE jet engines just used jet fuel made from a waste gas to fly from Orlando to London. Image credit: Virgin Atlantic.
What is it? This week Virgin Atlantic launched the first flight of a commercial airplane powered by recycled waste gas — carbon-rich pollution from a steel mill that’s been converted to jet fuel. No less a personage than Richard Branson was on hand to welcome transatlantic passengers from Orlando into London Gatwick airport, following a flight unremarkable in all aspects except a key environmental one.
Why does it matter? What, did you have some other plan for all that carbon pollution? “The technology not only provides a viable source of sustainable jet fuel but also reduces the amount of carbon dioxide emitted into the atmosphere,” explained John Holladay, a deputy manager at Pacific Northwest National Laboratory (PNNL), which developed the fuel in collaboration with industrial partner LanzaTech.
How does it work? Via a fermentation process similar to making beer, but instead of using sugar and yeast to create alcohol, this process uses bacteria to convert carbon-rich gases to fuels like ethanol. PNNL employs a proprietary catalyst it developed to remove oxygen from the ethanol, as it explained in a release, “and then combines the remaining hydrocarbon molecules to form chains large enough for jet fuel without forming aromatics that lead to soot when burned.” The process was approved by an international-standards body back in April, clearing the (run)way for it to be used in flight. The fuel is produced at a LanzaTech facility in Georgia, which the company plans to expand to allow for the production of millions of gallons of low-carbon juice every year.
The Robot In The Driver’s Seat Gets Even Smarter
MIT’s new model combines traditional decision-tree analysis with a neural network that’s able to explore a given environment as it goes. Image credit: MIT.
What is it? Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory, or CSAIL, developed an algorithm that helps autonomous robots navigate unfamiliar environments similarly to the way humans do — by observing and learning from the environment as they move through it.
Why does it matter? With current tech, robots are apt to make decisions about how to navigate a given space by relying on a decision tree: analyzing all possible movements from one side of a room to another, for instance, until they figure out one that works. This makes it hard for them to respond to surprises, though, and to anticipate the actions of other agents in a given situation. MIT researchers think their algorithm will be useful for self-driving cars faced with complicated situations like intersections and roundabouts, where the car has to not only navigate its way through, but also take into account what other drivers might do. Andrei Barbu, co-author of a paper the team presented at this week’s IEEE/RSJ International Conference on Intelligent Robots and Systems, explained, “Situations like roundabouts are hard, because they require reasoning about how others will respond to your actions, how you will then respond to theirs, what they will do next, and so on. You eventually discover your first action was wrong, because later on it will lead to a likely accident. This problem gets exponentially worse the more cars you have to contend with.”
How does it work? The model combines traditional decision-tree analysis with a neural network that’s able to explore a given environment as it goes. The network then makes predictions about possible choices to which it can assign levels of confidence depending on how much it knows from past experience. As MIT explains, “When the network makes a prediction with high confidence, based on learned information, it guides the robot on a new path. If the network doesn’t have high confidence, it lets the robot explore the environment instead, like a traditional planner.”