Chemists from The University of Manchester and The Australian National University (ANU) have engineered a new type of molecule that can store information at temperatures as cold as the dark side of the moon at night, with major implications for the future of data storage technologies.  

The findings, published in Nature, could pave the way for next-generation hardware about the size of a postage stamp that can store 100 times more digital data than current technologies. 

New research published in the American Physical Society June 24 and led by Rice University physicist Christopher Tunnell and postdoctoral researcherDorian Amaral, the study’s first author and lead analyst, sees the first direct search for ultralight dark matter using a magnetically levitated particle. In collaboration with physicists from Leiden University, the team suspended a microscopic neodymium magnet inside a superconducting enclosure cooled to near absolute zero. The setup was designed to detect subtle oscillations believed to be caused by dark matter waves moving through Earth.

Semiconductor nanolasers are emerging as key components for next-generation optical systems requiring ultra-low power and compact design. Traditional lasers face limitations at the nanoscale, prompting researchers to explore innovative nanolaser architectures. A recent study outlines breakthroughs in photonic crystal nanolasers, deep subwavelength cavities, and Fano lasers. These technologies enable enhanced light confinement and energy efficiency, making them ideal for applications in on-chip communication, neuromorphic computing, and hybrid optical-electronic systems.

Isosbestic behavior is a term used in spectroscopy, or the study of light and electromagnetic spectra, and references the specific wavelength in which the complete absorption of a solution is constant throughout the reaction, leading to a stable rate of absorbance throughout the entirety of the reaction. This type of behavior is typically viewed as an indicator that a chemical reaction has happened and the starting materials (reactants) have changed into the end materials (product) without any intermediates in between. However, researchers have found that this isn’t necessarily the case by using magic size clusters (MSCs) and precursors to reveal a relatively transparent intermediate involved in the reaction. 

Comparing brains to understand how neurons connect and how these connections can vary between species or individuals is one of the great areas of research in neuroscience. Now, a research team from the Department of Chemical Engineering at the Universitat Rovira i Virgili (URV) has developed a probabilistic model that allows different complex networks to be aligned at the same time in order to find common patterns. This method, which has been published in the scientific journal Nature Communications, not only substantially improves on current methods, but also represents a revolutionary approach to the problem that will allow us in the near future to align networks of hundreds of thousands of neurons in an effective way, something that cannot be done at present.