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Video showing the blinking quantum dots from the experiment. The researchers turned a laser field on and off to drive them far from equilibrium and modulate their blinking. | Shen, Y., Chen, C., Ma, H., et al. "Non-equilibrium entropy production and information dissipation in a non-Markovian quantum dot." Nat. Phys. (2026), https://doi.org/10.1038/s41567-026-03177-8 In order to build the computers and devices of tomorrow, we have to understand how they use energy today. That’

Scanning electron microscope (SEM) image of the team’s miniaturized, 3D-printed ion trap. Forty calcium ions are trapped in the space between the four poles that create an oscillating electrical potential. @Xiaoxing Xia/LLNL Researchers at Lawrence Livermore National Laboratory (LLNL), the University of California (UC) Berkeley, UC Riverside and UC Santa Barbara have miniaturized quadrupole ion traps for the first time with 3D printing — a breakthrough in one of the most p

Electronic order in quantum materials often emerges not uniformly, but through subtle and complex patterns that vary from place to place. One prominent example is the charge density wave (CDW), an ordered state in which electrons arrange themselves into periodic patterns at low temperatures. Although CDWs have been studied for decades, how their strength and spatial coherence evolve across a phase transition has remained largely inaccessible experimentally. Now, a team led by

ORNL scientists design a magnetic material as a stepping stone toward revealing new quantum phenomena.

This study addresses a longstanding challenge in flexible OLED technology, namely, the durability of its luminescence after repeated mechanical flexion. While the advances creating flexible light-emitting diodes have been substantial, progress has leveled off in the last decade due to limitations introduced by the transparent conductor layer, limiting their stretchability.

In a landmark achievement at the confluence of artificial intelligence and fundamental physics, a team of researchers from Tongji University, the Chinese university of Hong Kong and Nanyang Technological University has developed an AI algorithm capable of classifying complex topological phases of matter without relying on the mathematical tools that have limited human scientists for decades. This breakthrough, which tackles the notoriously difficult realm of non-Hermitian sys