Physicists have reached a long-sought goal. The catch is that their room-temperature superconductor requires crushing pressures to keep from falling apart.
Compressing simple molecular solids with hydrogen at extremely high pressures, University of Rochester engineers and physicists have, for the first time, created material that is superconducting at room temperature.
Featured as the cover story in the journal Nature, the work was conducted by the lab of Ranga Dias, an assistant professor of physics and mechanical engineering.
Continue reading “Researchers synthesize room temperature superconducting material” »
Einstein’s theory of special relativity gave us the speed limit of the Universe — that of light in a vacuum. But the absolute top speed of sound, through any medium, has been somewhat trickier to constrain.
It’s impossible to measure the speed of sound in every single material in existence, but scientists have now managed to pin down an upper limit based on fundamental constants, the universal parameters by which we understand the physics of the Universe.
That speed limit, according to the new calculations, is 36 kilometres per second (22 miles per second). That’s about twice the speed of sound travelling through diamond.
Could this be the energy source of the future?
The secret to the SPARC reactor is that its magnets will be built from new high-temperature superconductors that require much less cooling and can produce far more powerful magnetic fields. That means the reactor can be ten times more compact than ITER while achieving similar performance.
As with any cutting-edge technology, converting principles into practice is no simple matter. But the analysis detailed in the papers suggests that the reactor will achieve its goal of producing more energy than it sucks up. So far, all fusion experiments have required more energy to heat the plasma and sustain it than has been generated by the reaction itself.
Continue reading “New Reactor Design Could Produce First Ever Energy-Positive Fusion Reaction” »
The prize was awarded half to Roger Penrose for showing how black holes could form and half to Reinhard Genzel and Andrea Ghez for discovering a supermassive object at the Milky Way’s center.
Three scientists have won this year’s Nobel Prize in Physics for advancing our understanding of black holes, the all-consuming monsters that lurk in the darkest parts of the universe.
Applying a temperature gradient and a charge current to an electrical conductor leads to the release and absorbtion of heat. This is called the Thomson effect. In a first, NIMS and AIST have directly observing the magneto-Thomson effect, which is the magnetic-field-induced modulation of the Thomson effect. This success may contribute to the development of new functions and technologies for thermal energy management and to advances in fundamental physics and materials science on magneto-thermoelectric conversion.
The Seebeck effect and the Peltier effect have been extensively investigated for their application to thermoelectric conversion technologies. Along with these effects, the Thomson effect has long been known as a fundamental thermoelectric effect in metals and semiconductors. Although the influence of magnetic fields and magnetism on the Seebeck and Peltier effects has been well understood as a result of many years of research, the influence on the Thomson effect has not been clarified because it is difficult to measure and evaluate.
This NIMS-led research team observed heat release and absorption induced in an electrical conductor by simultaneously creating a temperature gradient across it, passing a charge current through the gradient, and applying a magnetic field. The team precisely measured temperature changes in the conductor associated with the heat release and absorption using a heat detection technique called lock-in thermography. As a result, the amount of heat released and absorbed was found to be proportional to both the magnitude of the temperature gradient and charge current. In addition, the team observed strong enhancement of the resultant temperature change when a magnetic field was applied to the conductor. The systematic measurements performed in this study demonstrated that the heat release and absorption signals detected under a magnetic field were indeed generated by the magneto-Thomson effect.
They can be made up of just two surfaces, bouncing the wave between them, but the more surfaces that are added, the more resonance is achieved. The ultimate is therefore to create a perfect sphere, creating surfaces in every direction within a three-dimensional object. At that point, the creation of a resonator moves from being a physics question to one of engineering, since even a stem holding the sphere can create distortion that reduces the impact of the resonator.
According to the Technion, the world’s first micro-resonator was demonstrated in the 1970s by Arthur Ashkin, winner of the 2018 Nobel Prize in Physics, who presented a floating resonator. Yet, despite the success of his innovation, the research direction was soon abandoned.
Now graduate student Jacob Kher-Alden, under the supervision of Prof. Tal Carmon, has built upon Ashkin’s work, creating a floating resonator which can exhibit resonant enhancement by ten million circulations of light, compared to about 300 circulations in Ashkin’s resonator.
Researchers have performed one of the most precise measurements yet to determine the proportion of matter in the universe.
The first artificial neural networks weren’t abstractions inside a computer, but actual physical systems made of whirring motors and big bundles of wire. Here I’ll describe how you can build one for yourself using SnapCircuits, a kid’s electronics kit. I’ll also muse about how to build a network that works optically using a webcam. And I’ll recount what I learned talking to the artist Ralf Baecker, who built a network using strings, levers, and lead weights.
I showed the SnapCircuits network last year to John Hopfield, a Princeton University physicist who pioneered neural networks in the 1980s, and he quickly got absorbed in tweaking the system to see what he could get it to do. I was a visitor at the Institute for Advanced Study and spent hours interviewing Hopfield for my forthcoming book on physics and the mind.
The type of network that Hopfield became famous for is a bit different from the deep networks that power image recognition and other A.I. systems today. It still consists of basic computing units—“neurons”—that are wired together, so that each responds to what the others are doing. But the neurons are not arrayed into layers: There is no dedicated input, output, or intermediate stages. Instead the network is a big tangle of signals that can loop back on themselves, forming a highly dynamic system.
