Researchers at the Center for Quantum Nanoscience (QNS) within the Institute for Basic Science (IBS) at Ewha Womans University have made a major scientific breakthrough by performing the world's smallest magnetic resonance imaging (MRI). In an international collaboration with colleagues from the U.S., QNS scientists used their new technique to visualize the magnetic field of single atoms.
More detail>>
Semiconductors are the basic building blocks of digital devices. Improvements in semiconductor functionality and performance are likewise enabling next-generation applications of semiconductors for computing, sensing and energy conversion. Yet researchers have long struggled with limitations in their ability to fully understand the electronic charges inside semiconductor devices and advanced semiconductor materials, limiting researchers' ability to drive further advances.
More detail>>
The electronic Barnett effect, first observed by Samuel Barnett in 1915, is the magnetization of an uncharged body as it is spun on its long axis. This is caused by a coupling between the angular momentum of the electronic spins and the rotation of the rod.
Using a different method from that employed by Barnett, two researchers at NYU observed an alternative version of this effect called the nuclear Barnett effect, which results from the magnetization of protons rather than electrons. Their study, published in Physical Review Letters (PRL), led to the first experimental observation of this effect.
More detail >>
Researchers at the University of Southampton and the Korea Institute for Advanced Study have recently showed that supersymmetry is anomalous in N=1 superconformal quantum field theories (SCFTs) with an anomalous R symmetry. The anomaly described in their paper, published in Physical Review Letters, was previously observed in holographic SCFTs at strong coupling, yet their work confirms that it is already present in the simplest free STFCs.
More detail >>
York University researchers have made a precise measurement of the size of the proton—a crucial step towards solving a mystery that has preoccupied scientists around the world for the past decade.
More detail >>
A team of scientists at Freie Universität Berlin has developed an Artificial Intelligence (AI) method that provides a fundamentally new solution of the "sampling problem" in statistical physics. The sampling problem is that important properties of materials and molecules can practically not be computed by directly simulating the motion of atoms in the computer because the required computational capacities are too vast even for supercomputers. The team developed a deep learning method that speeds up these calculations massively, making them feasible for previously intractable applications. "AI is changing all areas of our life, including the way we do science," explains Dr. Frank Noé, professor at Freie Universität Berlin and main author of the study. Several years ago, so-called deep learning methods bested human experts in pattern recognition—be it the reading of handwritten texts or the recognition of cancer cells from medical images. "Since these breakthroughs, AI research has skyrocketed. Every day, we see new developments in application areas where traditional methods have left us stuck for years. We believe our approach could be such an advance for the field of statistical physics." The results were published in Science.
More detail>>
