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New AI method flags fluid flow tipping points before simulations break down
David J. Silvester, a mathematics professor at the University of Manchester, has developed a novel machine-learning method to detect sudden changes in fluid behavior, improving speed and the cost of identifying these instabilities and overcoming one of the major obstacles faced when using machine learning to simulate physical systems. The findings are published in the Journal of Computational Physics.
Computational simulations of mathematical models of fluid flow are essential for everyday applications ranging from predicting the weather to the assessment of nuclear reactor safety. The advent of this simulation capability over the past 50 years has revolutionized the development of fuel-efficient airplanes, and sail configurations on racing yachts can now be optimized in real time, providing the marginal gains needed to win races in the America’s Cup.
Optimized aerodynamics means that modern day cyclists can ride faster, golf balls fly further and Olympic swimmers consistently set world records. Computational fluid dynamics also enables the modeling of the flow of blood in the human heart, making the provision of patient-specific surgery possible.
Plant-inspired water membrane filters CO₂ with constant selectivity and adjustable permeance
Gas separation membranes are vital for carbon capture, biogas upgrading, and hydrogen purification, all of which require the separation of carbon dioxide from gases like nitrogen, methane and hydrogen. However, the membranes currently in use for these applications suffer from limitations like low throughput or performance under high pressure and humidity, low gas flow, instability, and reaction rate limits.
Plants may have inspired a solution to many of these issues with the way their leaves absorb CO2. In a new study, published in Nature Communications, a team of researchers tests out a plant-inspired, water-based membrane that offers highly selective and permeable gas separation that outperforms many other materials, while also providing a greener, safer, and potentially cheaper way to capture CO2 and purify gases.
Hydroxyl radicals in UV-exposed water reveal surprising reaction pathway
How do radicals form in aqueous solutions when exposed to UV light? This question is important for health research and environmental protection. For example, with regard to the overfertilization of water bodies by intensive agriculture. A team at BESSY II has now developed a new method of investigating hydroxyl radicals in solution. By using a clever trick, the scientists gained surprising insights into the reaction pathway. The findings are published in the Journal of the American Chemical Society.
Hydroxyl radicals (OH·) are found everywhere, from the troposphere to the cells of the human body. There, they cause oxidative stress and accelerate the aging process. They are also increasingly present in rivers and lakes, where they are formed by the photolysis of nitrogen oxides that have entered the water from over-fertilized soils. When UV radiation from sunlight strikes nitrogen oxides, hydroxyl radicals and a range of other radicals are generated. The chemistry of these radicals is extremely difficult to characterize accurately, as they react very quickly.
A team led by Professor Alexander Föhlisch of the HZB has investigated the chemistry of hydroxyl radicals formed from nitrogen oxides in water using X-ray absorption spectroscopy at the BESSY II X-ray source.
From Asgard to Earth: Tiny tubes may reveal the moment complex life began
Stromatolites—and their close relatives, microbial mats—could be mistaken for what seems like a bunch of old dark rocks. But instead, they are dense, layered communities of microbes. Long before complex life such as animals or plants existed, stromatolites breathed the first molecules of oxygen into Earth’s atmosphere. Now, in a study published in Current Biology, researchers say they may also hold insights into how complex life began.
Associate Professor Brendan Burns, an evolutionary microbiologist at UNSW Sydney, is part of a team that identified a previously unknown microbe living in close partnership with another organism inside these “living fossils.” The work, co-led with researchers from the University of Technology Sydney and The University of Melbourne, could help solve one of life’s biggest mysteries: how simple cells first combined to form more complex life.
“Stromatolites could be more than ‘just’ a cradle of life where early microbial life flourished,” says A/Prof. Burns.
Carbon nanotube fiber sensors achieve record measurement error below 0.1%
Skoltech scientists, in collaboration with colleagues from China and Iran, have taken a major step toward creating highly precise carbon nanotube fiber (CNTF)-based sensors. In a paper published in the iScience journal, the authors, for the first time, quantitatively assessed the accuracy of CNTF sensors for dual-stage, i.e., manufacturing and post-manufacturing monitoring of epoxy-based polymer nanocomposites with dispersed CNTs.
The researchers emphasize that this development paves the way for creating a cutting-edge carbon-based material for high-precision and real-time sensing applications.
Existing monitoring sensors, such as fiber optics or piezoelectric sensors, are not suitable for the dual-stage monitoring of polymer composite materials. Additionally, embedding them into the composite structure often leads to deterioration in the mechanical properties of ready-made materials, making it more vulnerable to failure.
Compression technique makes AI models leaner and faster while they’re still learning
Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational resources. Traditionally, obtaining a smaller, faster model either requires training a massive one first and then trimming it down, or training a small one from scratch and accepting weaker performance.
Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Max Planck Institute for Intelligent Systems, European Laboratory for Learning and Intelligent Systems, ETH, and Liquid AI have now developed a new method that sidesteps this trade-off entirely, compressing models during training, rather than after.
Electrofluidic fiber muscles could enable silent robotic systems
Muscles are remarkably effective systems for generating controlled force, and engineers developing hardware for robots or prosthetics have long struggled to create analogs that can approach their unique combination of strength, rapid response, scalability, and control. But now, researchers at the MIT Media Lab and Politecnico di Bari in Italy have developed artificial muscle fibers that come closer to matching many of these qualities.
Like the fibers that bundle together to form biological muscles, these fibers can be arranged in different configurations to meet the demands of a given task. Unlike conventional robotic actuation systems, they are compliant enough to interface comfortably with the human body and operate silently without motors, external pumps, or other bulky supporting hardware.
The new electrofluidic fiber muscles—electrically driven actuators built in fiber format—are described in a recent paper published in Science Robotics. The work is led by Media Lab Ph.D. candidate Ozgun Kilic Afsar; Vito Cacucciolo, a professor at the Politecnico di Bari; and four co-authors.
People use the same neurons to see and imagine objects, study shows
Why can images of things we have seen seem so real when we later recall them from memory? A new study led by Cedars-Sinai Health Sciences University investigators sheds light on the answer. The research shows that the same brain neurons are activated when we imagine something and when we perceive something. The research, led by Cedars-Sinai, is the first to provide a detailed understanding of the shared mechanism that underlies visual perception and creation of mental images in the human brain. It was published in the journal Science.
“We generate a mental image of an object that we have seen before by reactivating the brain cells we used to see it in the first place,” said Ueli Rutishauser, Ph.D., director of the Center for Neural Science and Medicine and professor of Neurosurgery, Neurology and Biomedical Sciences at Cedars-Sinai Health Sciences University, and the study’s joint senior author.
“Our study revealed the code that we use to re-create the images.”