Quantum crystal melting; machine-learning bird detection; soft robots.
Melting quantum crystal of electrons
Confirming a fundamental phase transition in quantum mechanics that was theoretically proposed more than 80 years ago but not experimentally documented until now, MIT researchers reported that they’ve observed a highly ordered crystal of electrons in a semiconducting material and documented its melting, much like ice thawing into water.
The team said it used a spectroscopy technique that relies on electron “tunneling,” a quantum mechanical process that allows researchers to inject electrons at precise energies into a system of interest — in this case, a system of electrons trapped in two dimensions. The method uses hundreds of thousands of short electrical pulses to probe a sheet of electrons in a semiconducting material cooled to extremely low temperatures, just above absolute zero.
With this technique, the researchers shot electrons into the supercooled material to measure the energy states of electrons within the semiconducting sheet. Against a background blur, they detected a sharp spike in the data. After much analysis, they determined that the spike was the precise signal that would be given off from a highly ordered crystal of electrons vibrating in unison.
As the density of electrons was increased, essentially packing them into ever tighter quarters within the sheet, they found the data spike shot up to higher energies, then disappeared entirely, precisely at an electron density at which an electronic crystal has been predicted to melt.
The researchers believe they have finally captured the process of quantum melting — a phase transition in quantum mechanics, in which electrons that have formed a crystalline structure purely through their quantum interactions melt into a more disordered fluid, in response to quantum fluctuations to their density.
The idea for a crystal of electrons was first proposed in 1934 by the Hungarian-American physicist Eugene Wigner. Normally, semiconducting metals such as silicon and aluminum are able to conduct electricity in the form of electrons that ping-pong around at lightning speeds, creating a current through the material.
However, at ultracold temperatures, electrons in these metals should grind almost to a halt, as there’s very little heat left to spur their motions. Any movements electrons do exhibit, then, should be due to quantum interactions — the invisible forces between individual electrons and other quantum, subatomic particles.
Electrons, being negatively charged, naturally repel each other. Wigner proposed that for supercooled electrons at low densities, their mutual repulsive forces should act as a sort of scaffold, holding the electrons together yet apart at equally spaced intervals, thus creating a crystal of electrons. Such a rigid arrangement, which has since been coined a Wigner crystal, should turn a metal into an insulator rather than an electrical conductor.
Recognizing birds from photos
Developed by Caltech and Cornell Tech computer-vision researchers in partnership with the Cornell Lab of Ornithology and bird enthusiasts, the Merlin Bird Photo ID mobile app has been launched and can identify hundreds of North American species it “sees” in photos thanks to machine-learning technology.
Once it is downloaded on a mobile device, Merlin Bird Photo ID can go anywhere bird watchers go—even places without cell service or Wi-Fi, the researchers said.
Merlin project leader Jessie Barry at the Cornell Lab said, “When you open the Merlin Bird Photo ID app, you’re asked if you want to take a picture with your smartphone or pull in an image from your digital camera. You zoom in on the bird, confirm the date and location, and Merlin will show you the top choices for a match from among the 650 North American species it knows.”
Designing soft robots that mimic biological moments
Designing a soft robot to move organically — to bend like a finger or twist like a wrist — has always been a process of trial and error — until now. Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences and the Wyss Institute for Biologically Inspired Engineering have developed a method to automatically design soft actuators based on the desired movement.
Rather than designing these actuators empirically, the researchers wanted a tool where motion could be plugged in, and it would tell you how to design the actuator to achieve that motion.
Designing a soft robot that can bend like a finger or knee may seem simple but the motion is actually incredibly complex.
The method developed by the team uses mathematical modeling of fluid-powered, fiber-reinforced actuators to optimize the design of an actuator to perform a certain motion. The team used this model to design a soft robot that bends like an index finger and twists like a thumb when powered by a single pressure source.
The new methodology will be included in the Soft Robotic Toolkit, an online, open-source resource developed at SEAS to assist researchers, educators and budding innovators to design, fabrication, model, characterize and control their own soft robots.