Researchers retrieved reef monitoring devices that had been placed in deep coral reefs in Guam. The devices were placed up to 330 feet below the surface.
NPR (www.npr.org)
New research from the Hebrew University of Jerusalem has revealed that light’s magnetic field plays a direct role in the Faraday effect, overturning a 180-year understanding of the phenomenon[^1][^2].
The study, published in Scientific Reports in November 2025, shows that the magnetic component of light contributes about 17% of the observed Faraday rotation at visible wavelengths and up to 75% in the infrared spectrum when using Terbium-Gallium-Garnet (TGG)[^1].
“In simple terms, it’s an interaction between light and magnetism,” explains Dr. Amir Capua. “The static magnetic field ‘twists’ the light, and the light, in turn, reveals the magnetic properties of the material. What we’ve found is that the magnetic part of light has a first-order effect, it’s surprisingly active in this process”[^2].
The researchers used the Landau-Lifshitz-Gilbert (LLG) equation to demonstrate that light’s magnetic field can generate magnetic torque inside materials, similar to a static magnetic field[^1]. This discovery challenges the traditional view that only light’s electric field contributes to the Faraday effect[^4].
The findings have potential applications in:
- Optical data storage
- Spintronics
- Light-based magnetic control
- Quantum computing technologies[^2]
[^1]: Nature - Faraday effects emerging from the optical magnetic field
[^2]: QD Latin America - New magnetic component discovered in the Faraday effect after nearly two centuries
[^4]: The Debrief - Scientists Revisiting the ‘Faraday Effect’ Have Uncovered a Surprising Magnetic Interaction Between Light and Matter
Researchers from Saarland University (UdS) have achieved an important breakthrough in Quantum Communication by demonstrating Quantum Entanglement and Teleportation over a 14 km long fiber link, the “Saarbrücken Quantum Communication Fiber Testbed”.
Researchers used an AI based on GPT architecture to map the brain, and they found it’s way more complex than we thought. Instead of the ~52 broad regions we’ve been working with, the AI identified about 1,300 distinct areas.
They trained a model called Cell Transformer on mouse brain scans. Instead of learning language, it learned the “grammar” of how brain cells are organized relative to their neighbors. It then automatically drew the borders between brain regions with high precision, revealing hidden neighborhoods we never knew existed.
With a map this detailed, researchers can now pinpoint the tiny, specific cellular areas involved in conditions like Alzheimer’s and depression. Having such a detailed map could massively speed up research and lead to much more targeted and effective treatments in the future.
