A new method to measure the essential properties of spin waves in graphene

Spin waves, a change in the spin of electrons that propagates through a material, could fundamentally change the way devices store and transport information. These waves, also called magnons, do not disperse and do not couple with other particles. Under the right conditions, they can even act as a superfluid, moving through material without any loss of energy.

But the very properties that make them so powerful also make them almost impossible to measure. In a previous study, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) demonstrated the ability to both excite and detect spin waves in a two-dimensional graphene magnet, but they did not ‘were not able to measure any of the specific wave properties.

Today, SEAS researchers have demonstrated a new way to measure the essential properties of spin waves in graphene.

In previous experiments, we only knew that we could generate spin waves, but we didn’t know anything about their properties quantitatively “, said Amir Yacoby, professor of physics and applied physics at SEAS and lead author of the article. “With this new work, we can determine all of these quantitative numbers, including the energy and number of spin waves, their chemical potential and their temperature. It is an extremely important tool that we can use to explore new ways to generate magnons and get closer to achieve spin superfluidity. “

The research is published in Physics of nature.

Measuring the properties of a spin wave is like measuring the properties of a tidal wave if the water itself was undetectable. If you couldn’t see the water, how could you measure the speed, height, or number of tidal waves? One way would be to feed something into the system that you can measure, like a surfer. The speed of the tidal wave can be detected by measuring the speed of the surfer.

In this case, Yacoby and his team used an electron surfer.

The researchers started with a quantum Hall-effect ferromagnetic. Quantum Hall effect ferro-magnets are magnets made from 2D materials, in this case graphene, where all the spins of the electrons are in the same direction. If an electron with a different spin is introduced into this system, it will use energy to try to return the spins of its neighbors.

But the research team found that when they injected an electron with a different spin into the system and then generated spin waves, the energy the electron needed to tip its neighbors decreased.

“It is striking that in one way or another, the electrons that we put into the system are sensitive to the presence of spin waves”, said Andrew T. Pierce, graduate student at SEAS and co-first author of the study. “It’s almost as if these electrons are grabbing onto the wave and using it to help reverse the spins of their neighbors.”

“Spin waves don’t like to interact with anything, but by using electrons and this energy cost as a proxy to probe the properties of a spin wave, we can determine the chemical potential, which in combination with the knowledge of temperature and some other properties, gives us a complete description of the magnon “, said Yonglong Xie, postdoctoral researcher at SEAS and co-first author of the study. “This is essential to know if the wave is approaching the limit where it reaches superfluidity. “

The research could also provide a general approach to study other exotic systems that are difficult to measure, such as the recently discovered moiré materials that are expected to support a variety of waves like the spin wave studied in this work..

This research was co-authored by Seung Hwan Lee, Patrick R. Forrester, Di S. Wei,

Kenji Watanabe, Takashi Taniguchi and Bertrand I. Halperin. It was supported in part by the United States Department of Energy, the Bureau of Basic Energy Sciences, the Division of Materials Science and Engineering under grant DE-SC0001819, the Gordon and Betty Moore Foundation and the National Science Foundation, under grant DMR-1231319.

Source: https://www.seas.harvard.edu/

Source link

About Donald P. Hooten

Check Also

Distributed deep learning method without sharing sensitive data

Data sharing is one of the major challenges of machine learning models. The advent of …