Twisting atom thin materials reveals new way to save computing energy
Peer-Reviewed Publication
Updates every hour. Last Updated: 8-Jun-2026 17:15 ET (8-Jun-2026 21:15 GMT/UTC)
A recent study shows a new and potentially more energy efficient way for information to be transmitted inside electronic systems, including computers and phones—without relying on electric currents or external magnetic fields. Researchers at KTH Royal Institute of Technology and international collaborators demonstrate that simply twisting two layers of certain atom thin magnetic materials allows magnetic signals to carry information instead of relying on electrical currents to do the work.
Sultan Qaboos University researchers develop a compact diagnostic device with applications in food safety, public health, and environmental monitoring
Are we ready for solar storms, submarine cable cuts, satellite disruptions, and extreme weather to disrupt communication networks and potentially trigger a “digital pandemic"?
A new report – “When digital systems fail: The hidden risks of our digital world" – outlines risk scenarios on Earth, at sea, and in space, analysing the fragility of interconnected digital systems and offering a roadmap for preparedness.
Experts brought together by the International Telecommunication Union (ITU), the United Nations Office for Disaster Risk Reduction (UNDRR), and Sciences Po, call for coordinated action between countries to improve digital resilience and protect essential services like healthcare, finance, and emergency response.
Chocolate is more than a treat; it is Theobroma cacao, the "food of the gods." But our global craving for cocoa is putting a divine strain on the planet. As demand surges, tropical forests are often cleared to make room for plantations, destroying biodiversity and releasing stored carbon.
Isabella Steeley, a researcher from the University of Sheffield, is investigating a ground-breaking solution that could boost chocolate yields while fighting climate change: Enhanced Rock Weathering (ERW).
Microplastics and nanoplastics now contaminate every human compartment that has been examined. Decedent brain tissue carries seven to thirty times the concentration found in liver or kidney. The burden rose by approximately fifty percent between 2016 and 2024. The heaviest loads sit in the brains of donors with documented dementia. Recent prospective cohort data link these particles to fourfold increases in the composite risk of myocardial infarction, stroke, or death. A new Perspective in the inaugural issue of Brain Health, published by Genomic Press, argues that the field must now move past alarm and toward the three priorities that follow from the evidence: validated measurement, polymer-specific mechanism, and population-scale removal.
Artificial intelligence systems based on neural networks — such as ChatGPT, Claude, DeepSeek or Gemini — are extraordinarily powerful, yet their internal workings remain largely a “black box”. To better understand how these systems produce their responses, a group of physicists at Harvard University has developed a simplified mathematical model of learning in neural networks that can be analysed mathematically using the tools of statistical physics.
“Toy models”, like the one presented in the study just published in the Journal of Statistical Mechanics: Theory and Experiment (JSTAT), provide researchers with a controlled theoretical laboratory for investigating the fundamental mechanisms of neural networks. A deeper understanding of how these systems work could help design artificial intelligence systems that are more efficient and reliable, while also addressing some of the current challenges.