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.

From smart grids to the internet of things, the modern world is increasingly reliant on connectivity between electronic devices. Thanks to University of Ottawa researchers, these devices can now be simultaneously connected and powered with a simple optical fiber over long distances, even in the harshest environments.

This significant step forward in the development of photonic power converters – devices that turn laser light into electrical power – could integrate laser-driven, remote power solutions into existing fiber optic infrastructure. This, in turn, could pave the way for improved connectivity and more reliable communication in remote locations and extreme situations.

From smart grids to the internet of things, the modern world is increasingly reliant on connectivity between electronic devices. Thanks to University of Ottawa researchers, these devices can now be simultaneously connected and powered with a simple optical fiber over long distances, even in the harshest environments. This significant step forward in the development of photonic power converters – devices that turn laser light into electrical power – could integrate laser-driven, remote power solutions into existing fiber optic infrastructure. This, in turn, could pave the way for improved connectivity and more reliable communication in remote locations and extreme situations.

The Network for the Public Communication of Science and Technology (PCST) has announced the results of its inaugural PCST Award competition, recognizing exceptional contributions to the advancement of science communication as a professional field. The top prize was awarded to Professor Bruce Lewenstein of Cornell University (USA), honoring his decades-long impact on science communication research, education, and professional infrastructure worldwide. Susana Herrera-Lima (Mexico) and Toss Gascoigne (Australia) were named runners-up for their transformative work in building science communication capacity and influence in their respective regions and worldwide. The PCST Award is generously supported by the GIST Graduate School of AI Policy and Strategy (Republic of Korea). The winner received a cash prize of 1,500 USD, and each runner-up received 700 USD.

Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications. 

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.