How is ventilation at various depth layers of the Atlantic connected and what role do changes in ocean circulation play? Researchers from Bremen, Kiel and Edinburgh have pursued this question and their study has been published in the professional journal Nature Communications.

A team of scientists has developed a powerful new way to detect subtle magnetic signals in common metals like copper, gold, and aluminium—using nothing more than light and a clever technique. Their research, recently published in the prestigious journal Nature Communications, could pave the way for advances in everything from smartphones to quantum computing.

A new material platform has enabled scientists to create photon pairs whose entanglement can be tuned, from a layer thinner than a human hair. The photon pairs are created by a metasurface made of indium gallium phosphide (InGaP), which has a nonlinear response that can split a classical photon into two quantum photons. By tuning the wavelength of the initial photon, the two new photons can be generated as fully entangled through their polarisation, not entangled at all, or any value in between, with picosecond control.

Researchers have developed a new microscope that can visualize the optical response of surfaces at an unprecedented spatial resolution of one nanometer. This paves the way for optical microscopy of atomic-scale structures, such as single molecules and atomic defects. Such capability is important for optical engineering of nanomaterials and surfaces at angstrom scales.

Researchers have created an alloy that maintains its special ability to act as a sort of temperature-activated memory foam at temperatures as low as -200°C – making it highly suitable for space equipment regularly exposed to these challenging, cold environments.

Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), researchers from the Shanghai Astronomical Observatory of the Chinese Academy of Sciences, in collaboration with other institutes, has uncovered an unexpectedly complex and dynamic filamentary network within a very-high-velocity cloud (VHVC) in the Milky Way.

At the forefront of discovery, where cutting-edge scientific questions are tackled, we often don’t have much data. Conversely, successful machine learning (ML) tends to rely on large, high quality data sets for training. So how can researchers harness AI effectively to support their investigations? Published in Physical Review Research, scientists describe an approach for working with ML to tackle complex questions in condensed matter physics. Their method tackles hard problems which were previously unsolvable by physicist simulations or by ML algorithms alone.