Boron Nitride

A collaborative effort involving POSTECH and the University of Technology Sydney has yielded an advancement in light source technology. The team used graphene to develop a quantum light emitting diode (Quantum LED) that can precisely emit light using a single atom. This innovative technology generates light by injecting charges into a luminescent material composed of a single atom. 

The research team implemented this advanced light source technology using hexagonal boron nitride (hBN), a material known for its ability to stably confine electrons in various atomic defects. Unlike traditional quantum dots, which are composed of hundreds to thousands of atoms, the quantum LED developed by the team exhibits excellent quantum light source characteristics even at room temperature. This breakthrough addresses a significant challenge in the field, as hBN's wide bandgap has historically made it difficult to inject charges electrically, thus hindering the development of LED devices. To overcome this obstacle, the researchers designed a "graphene-hBN-graphene" van der Waals tunneling structure. 

Researchers at Korea's Pohang University of Science and Technology and Japan's National Institute for Materials Science have developed a way to control the properties of graphene by combining superconductors and graphene. 

Professor Lee Gil-ho of Pohang University of Science and Technology (POSTECH) and researchers from the Research Institute, in collaboration with Kenji Watanabe and Takashi Taniguchi from the National Institute for Materials Science (NIMS) in Japan, noted they have successfully improved the junction characteristics between graphene and superconducting electrodes. 

Researchers at Florida State University (FSU), Chinese Academy of Sciences (CAS) and Wuhan University have examined how physical manipulations of graphene, such as layering and twisting, impact its optical properties and conductivity. 

The team, led by Assistant Professor Guangxin Ni, along with Assistant Professor Cyprian Lewandowski and graduate research assistant Ty Wilson, found that the conductivity of twisted bilayer graphene is not heavily impacted by physical or chemical manipulations and instead depends more on how the material’s minute geometry structure changes by interlayer twisting — a revelation that opens the door for additional studies on how lower temperatures and frequencies impact graphene’s properties.

Polaritons are coupled excitations of electromagnetic waves with either charged particles or vibrations in the atomic lattice of a given material. One of their most attractive properties is the capacity to confine light at the nanoscale, which is even more extreme in two-dimensional (2D) materials. 2D polaritons have been investigated by optical measurements using an external photodetector. However, their effective spectrally resolved electrical detection via far-field excitation remains unexplored. This hinders their exploitation in crucial applications such as sensing, hyperspectral imaging, and optical spectrometry, banking on their potential for integration with silicon technologies. 

Recently, researchers from Spain's ICFO, the University of Ioannina, Universidade do Minho, the International Iberian Nanotechnology Laboratory, Kansas State University, the National Institute for Materials Science (Tsukba, Japan), POLIMA (University of Southern Denmark) and URCI (Institute of Materials Science and Computing, have reported on the electrical spectroscopy of polaritonic nanoresonators based on a high-quality 2D-material heterostructure, which serves at the same time as the photodetector and the polaritonic platform. Subsequently, the team electrically detected these mid-infrared resonators by near-field coupling to a graphene pn-junction. The nanoresonators simultaneously exhibited extreme lateral confinement and high-quality factors. 

NTT Corporation, along with The University of Tokyo and National Institute for Materials Science (NIMS), showed that graphene plasmon wave packets can be generated, manipulated and read out on-chip using terahertz electronics.  

The team managed to electrically generate and control graphene plasmon wave packets with a pulse width of 1.2 picoseconds. This result shows that the phase and amplitude of a terahertz signal can be controlled electrically by using graphene plasmons. It enables terahertz signal processing, a method different from conventional electrical circuit technology using transistors and is expected to contribute to realizing ultrahigh-speed signal processing in the future.

Van der Waals encapsulation of 2D materials in hBN stacks could be a promising way to create ultrahigh-performance electronic devices. However, current approaches for achieving van der Waals encapsulation, which involve artificial layer stacking using mechanical transfer techniques, are difficult to control, prone to contamination and unscalable. 

Researchers at Shanghai Jiao Tong University, Wuhan University, Ulsan National Institute of Science and Technology, National Institute for Materials Science and Tel Aviv University recently reported the transfer-free direct growth of high-quality graphene nanoribbons (GNRs) in hexagonal boron nitride (hBN) stacks. The as-grown embedded GNRs exhibited highly desirable features being ultralong (up to 0.25 mm), ultranarrow (<5 nm) and homochiral with zigzag edges. 

Researchers at MIT and Japan's National Institute for Materials Science (NIMS) have observed an exotic electronic state in a material made of five layers of graphene, that could enable new forms of quantum computing. 

Generally speaking, the electron is the basic unit of electricity, as it carries a single negative charge. At least, that's the case in most materials in nature. But in very special states of matter, electrons can splinter into fractions of their whole. This phenomenon, known as “fractional charge,” is extremely rare, and if it can be corralled and controlled, the exotic electronic state could help to build resilient, fault-tolerant quantum computers. To date, this effect, known to physicists as the “fractional quantum Hall effect,” has been observed a handful of times, and mostly under very high, carefully maintained magnetic fields. Now, the scientists have also seen the effect in a material that did not require such powerful magnetic manipulation. They found that when five sheets of graphene are stacked like steps on a staircase, the resulting structure inherently provides just the right conditions for electrons to pass through as fractions of their total charge, with no need for any external magnetic field.

Rice University researchers have mapped out how bits of 2D materials move in liquid ⎯ which that could help scientists assemble macroscopic-scale materials with the same useful properties as their 2D counterparts.

In order to maintain these special properties in bulk form, sheets of 2D materials have to be properly aligned ⎯ a process that often occurs in solution phase. The Rice team focused on graphene and hexagonal boron nitride, a material with a similar structure to graphene but composed of boron and nitrogen atoms.

Universidad Politécnica de Madrid researchers have reported the development of an electrically tunable graphene-based biosensor that leverages sound waves to provide unprecedented infrared sensitivity and specificity at the single layer limit. By precisely matching the tunable graphene plasmon frequency to target molecular vibrations, even faint spectral fingerprints emerge clearly.

This acoustically activated approach enables precise in situ study of angstrom-scale films, unlocking new infrared applications across chemistry, biology and medicine.

Researchers at Northwestern University, MIT, Harvard University, CIFAR Azrieli Global Scholars Program and Japan's National Institute for Materials Science have developed a graphene-based synaptic transistor capable of higher-level thinking.

The device simultaneously processes and stores information just like the human brain. In new experiments, the researchers demonstrated that the transistor goes beyond simple machine-learning tasks to categorize data and is capable of performing associative learning.