Air-polluting microplastics have been found in rural environments in greater quantities than in urban locations, researchers say. 

Scientists led by the University of Leeds detected up to 500 microscopic particles of plastic per square metre per day in an area of woodland during the three-month study – almost twice as much as in a sample collected in a city center. 

They believe trees and other vegetation capture airborne microplastic particles from the atmosphere and deposit them, highlighting the impact that different landscape and weather conditions have on the spread of the particles. 

And they say that these unexpected results challenge the assumption that microplastic pollution is mainly an urban problem. 

Researchers have developed formamidinium (FA)-alloyed cesium lead iodide perovskite nanoplatelets that combine improvements in phase stability with linearly polarized red emission. By precisely controlling A-site composition and surface ligand interactions, the team achieved uniform, well-oriented superlattices that maintain optical performance under ambient conditions. This breakthrough provides a new route toward stable, directionally controlled light sources for advanced photonic and display technologies.

“We know the genes, but not their functions.” To resolve this long-standing bottleneck in microbial research, a joint research team has proposed a cutting-edge research strategy that leverages Artificial Intelligence (AI) to drastically accelerate the discovery of microbial gene functions.

KAIST announced on Jan. 12 that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Bernhard Palsson from the Department of Bioengineering at UCSD, has published a comprehensive review paper. The study systematically analyzes and organizes the latest AI-based research approaches aimed at revolutionizing the speed of gene function discovery.

A team of researchers from the Shanghai Institute of Applied Physics, Chinese Academy of Sciences, has developed an innovative bipolar cusp-like pulse-shaping algorithm to address the "pile-up" challenge prevalent in high-radiation environments. Through the implementation of real-time reconstruction on FPGA hardware, this technology enables precise discrimination between neutrons and gamma rays, even under extreme count rates. Consequently, it offers a more robust tool for applicaitons in nuclear security and fundamental physics research. 

Tokyo, Japan – Researchers from Tokyo Metropolitan University have developed a suite of algorithms to automate the counting of sister chromatid exchanges (SCE) in chromosomes under the microscope. Conventional analysis requires trained personnel and time, with variability between different people. The team’s machine-learning-based algorithm boasts an accuracy of 84% and gives a more objective measurement. This could be a game changer for diagnosing disorders tied to abnormal numbers of SCEs, like Bloom syndrome.