Researchers at Koç University have developed an open-access, machine learning–assisted tool that automates microfluidic chip design, lowering the technical barrier for nonexpert users. Published in Science Advances and led by Assoc. Prof. Dr. Savaş Taşoğlu, the study introduces μFluidicGenius (μFG), a user-friendly platform that enables rapid design of microfluidic circuits based on target flow conditions, producing 3D-printable designs with approximately 90% accuracy.

Two-dimensional semiconductors are promising alternatives to silicon for future electronics, but controlling their atomic-scale growth remains a major challenge. Now, researchers from Okayama University, in collaboration with Shinshu University and Keio University, have directly observed in real time how monolayer transition metal dichalcogenides form in a confined microreactor. By visualizing molten precursor droplets during growth, the team identified distinct growth regimes determining crystal size and quality, offering strategies for designing high-performance semiconductor materials.

Electromagnetic pulses (EMPs) generated from nuclear explosions, high-power electromagnetic pulse devices, and intentional electromagnetic interference can significantly impact civilian and military infrastructure. Recently, researchers from the Republic of Korea and the USA have developed a transparent glass window based on an asymmetric hexagonal metal mesh film with ultra-wideband EMP-shielding capabilities for infrastructure protection. Notably, the proposed innovation is resistant to humidity, mechanical abrasion, and corrosive species.

By changing the physical structure of gold at the nanoscale, researchers can drastically change how the material interacts with light – and, as a result, its electronic and optical properties. This is shown by a study from Umeå University published in Nature Communications.

The first study to test a digital tool designed to help people with Long COVID manage their energy levels has been developed by a team of researchers.

The paper published in Nature Communications is entitled “A Digital Platform with Activity Tracking for Energy Management Support in Long COVID: A Randomised Controlled Trial”.

In this study, funded by the National Institute for Health and Care Research (NIHR), people with Long COVID tried out a new app called “Pace Me” to help manage their energy levels.

Researchers at The Hong Kong University of Science and Technology (HKUST) School of Engineering have achieved a major breakthrough in computational neural engineering. They have developed a novel reinforcement learning-based generative model to predict neural signals, creating an artificial information pathway that effectively bypasses damaged brain areas. This groundbreaking research opens up new possibilities for neural rehabilitation in patients suffering from motor or cognitive impairments caused by conditions such as stroke or spinal cord injury. Their study, titled "A generative spike prediction model using behavioral reinforcement for re-establishing neural functional connectivity", has been published in the prestigious journal Nature Computational Science.