Leeds researchers help unlock magnetic waves for greener, faster tech
Scientists, including experts from the University of Leeds, have taken a major step toward developing more energy-efficient, high-performance electronics using magnetic waves.
Published in Nature, the international study successfully detected and mapped these waves, called magnons, at the scale of individual atoms – a breakthrough that could lead to smarter, more sustainable consumer technology.
Magnons are tiny ripples in magnetic materials that can carry information without an electrical current. They have long been seen as a potential game-changer for computing, offering ultra-fast data processing while using far less energy than traditional electronics.
A key part of the breakthrough came from work carried out at SuperSTEM, the UK’s national electron microscopy facility, directed by Professor Quentin Ramasse, Chair of Advanced Microscopy at Leeds.
He said: “This represents a major leap for electron microscopy, and another world-first for our facility enabled by the use of cutting-edge electron detectors and high-resolution instrumentation. Only a decade ago, few would have predicted the ability to detect reliably such faint signals.”
Research was led by Dr Demie Kepaptsoglou of the University of York, in collaboration with Leeds, Durham University, and Sweden’s Uppsala University. Support came from the EPSRC’s New Horizons initiative and the Royal Society, with Dr Kepaptsoglou highlighting the “rich rewards available from innovative yet high-risk fundamental research such as this.”
The Leeds team helped detect and identify subtle magnon signatures using a high-energy-resolution electron microscope. The data was validated by theoretical modelling from Uppsala researchers.
“Our simulations predict that magnons leave subtle fingerprints in the scattered electrons,” said co-lead author José Ángel Castellanos-Reyes.
This pioneering work has broad implications, from improving mobile devices to enabling future technologies like quantum computing and low-energy AI systems, all with a lower environmental impact.
