Wastewater from washing machines is considered a major source of microplastics – tiny plastic particles that are suspected of harming human and animal health. Researchers at the University of Bonn now have developed a filter to curb this problem. Their filter was inspired by the gill arch system in fish. In initial tests, the now patent-pending filter was able to remove over 99 percent of plastic fibers from washing machine wastewater. The results now have been published in the journal npj Emerging Contaminants. 

The SECURED project aims at generating libraries and machine learning tools to foster innovation in the fight against blood cancers while preserving the highest privacy standards for sensitive patient data. Dr Eduard Porta, head of the Cancer Immunogenomics team at the Josep Carreras Leukaemia Research Institute is part of this Horizon Europe-funded collaboration that will bring the most sophisticated technologies into the real world.

Future events such as the weather or satellite trajectories are computed in tiny time steps, so the computation must be both efficient and as accurate as possible at each step lest errors pile up. A Kobe University team now introduces a new method that uses deep learning for creating tailored, accurate simulations that respect physical laws, while also being more computationally efficient.

Hydrogen, with its carbon-free composition and the availability of abundant renewable energy sources for its production, holds significant promise as a fuel for internal combustion engines (ICEs). Its wide flammability limits and high flame speeds enable ultra-lean combustion, which is a promising strategy for reducing NOx emissions and improving thermal efficiency. However, lean hydrogen-air flames, characterized by low Lewis numbers, experience thermo-diffusive instabilities that can significantly influence flame propagation and emissions. To address this challenge, it is crucial to gain a deep understanding of the fundamental flame dynamics of hydrogen-fueled engines. This study uses high-speed planar SO2-LIF to investigate the evolutions of the early flame kernels in hydrogen and methane flames, and analyze the intricate interplay between flame characteristics, such as flame curvature, the gradients of SO2-LIF intensity, tortuosity of flame boundary, the equivalent flame speed, and the turbulent flow field. Differential diffusion effects are particularly pronounced in H2 flames, resulting in more significant flame wrinkling. In contrast, CH4 flames, while exhibiting smoother flame boundaries, are more sensitive to turbulence, resulting in increased wrinkling, especially under stronger turbulence conditions. The higher correlation between curvature and gradient of H2 flames indicates enhanced reactivity at the flame troughs, leading to faster flame propagation. However, increased turbulence can mitigate these effects. Hydrogen flames consistently exhibit higher equivalent flame speeds due to their higher thermo-diffusivity, and both hydrogen and methane flames accelerate under high turbulence conditions. These findings provide valuable insights into the distinct flame behaviors of hydrogen and methane, highlighting the importance of understanding the interactions between thermo-diffusive effects and turbulence in hydrogen-fueled engine combustion.

A University of Texas at Dallas researcher and his collaborators have developed an artificial intelligence (AI)-assisted tool that makes it possible for visually impaired computer programmers to create, edit and verify 3D models independently.

The tool, A11yShape, addresses a challenge for blind and low-vision programmers by providing a method for editing and verifying complex models without assistance from sighted individuals. The first part of the tool’s name is a numeronym, a number-based contracted word that stands for “accessibility” and is pronounced “al-ee.”