The Leidenfrost effect is a well-known physical phenomenon first discovered in 1756. It occurs when a liquid is in the proximity of a surface that is significantly warmer than its boiling point. This produces an insulating vapor layer that prevents the liquid from...
In recent years, electronics engineers have been experimenting with new materials that could be used to study electronic correlation phenomena. Van der Waals (vdW) moiré materials are particularly promising for examining these phenomena. VdW materials are composed of...
The S-matrix bootstrap is a numerical method that can be used to determine or constrain the scattering amplitudes of particles in quantum field theory using simple principles. Over the past few decades, some physicists have tried to use this technique to study...
A crucial goal of spintronics research is to coherently manipulate electron spins at room temperature using electrical current. This is particularly valuable as it would enable the development of numerous devices, including spin field-effect transistors.
A time crystal is a unique and exotic phase of matter first predicted by the American physicist Frank Wilczek in 2012. Time crystals are temporal analogs of more conventional space crystals, as both are based on structures characterized by repeating patterns.
Sphere packing, a mathematical problem in which non-overlapping spheres are arranged within a given space, has been widely investigated in the past. It has been proven that the densest possible packing is a face-centered cubic (FCC) crystal with a space-filling...
The LIGO/Virgo/KAGRA Collaboration, a large group of researchers at different institutes worldwide, has recently set the strongest constraints on cosmic strings to date, using the Advanced LIGO/Virgo full O3 dataset. This dataset contains the latest gravitational...
Over the past few years, many physicists and material scientists worldwide have been investigating the properties and characteristics of magic-angle twisted bilayer graphene (MATBG). MATBG is a strongly correlated material that was first experimentally realized in...
In recent years, several computer scientists and physicists have been exploring the potential of quantum-enhanced machine learning algorithms. As their name suggests, quantum machine learning approaches combine quantum algorithms with machine learning techniques.
When molecules are excited, they can give rise to a variety of energy conversion phenomena, such as light emission and photoelectric or photochemical conversion. To unlock new energy conversion functions in organic materials, researchers should be able to understand...