Researchers have developed a machine learning model that predicts Cas9 proteins that can be tailored with designer properties for therapeutic use.
A new analysis of the sky has finally confirmed where the missing half of the Universe’s visible matter has been hiding.
In the space around galaxies, it lurks as huge, invisible clouds of ionized hydrogen. Normally, this would be impossible to see – but a large international team of astronomers and astrophysicists has developed a technique that reveals its hiding places, out there in the darkness amidst the stars.
Survey programs confirm the missing half of the Universe’s material takes the form of an intergalactic mist of hydrogen expelled farther from the active cores of galaxies than anybody previously thought.
An NIH-funded project leverages advanced synapse imaging to monitor real-time neuronal changes during learning, unveiling new insights that could inspire next-generation brain-like AI systems. How do we learn something new? How do tasks at a new job, the lyrics to the latest hit song, or directio
Next-generation DNA sequencing (NGS)—the same technology which is powering the development of tailor-made medicines, cancer diagnostics, infectious disease tracking, and gene research—could become a prime target for hackers.
A study published in IEEE Access highlights growing concerns over how this powerful sequencing tool—if left unsecured—could be exploited for data breaches, privacy violations, and even future biothreats.
Led by Dr. Nasreen Anjum from the University of Portsmouth’s School of Computing, it is the first comprehensive research study of cyber-biosecurity threats across the entire NGS workflow.
Perovskite photovoltaics (PV) are poised at the brink of commercialization, yet stability remains the foremost hurdle to overcome for widespread adoption. While extensive research has addressed the degradation of perovskite PV through accelerated indoor testing, outdoor testing remains relatively underexplored and primarily focused on small cells rather than modules.
This gap underscores the urgent need to comprehensively study outdoor degradation processes. Understanding how perovskite PV modules perform under real-world environmental conditions is crucial for advancing toward commercial viability.
In our work published in ACS Energy Letters, we present a two-year outdoor evaluation of perovskite modules, shedding light on their degradation under real-world conditions. Our findings highlight a significant milestone in perovskite PV research, with the most robust module maintaining 78% of its initial performance after one year. Performance loss rates during the burn-in period were found to be about 7%–8% per month.
A technology for hydrogen (H2) production has been developed by a team of researchers led by Professors Seungho Cho and Kwanyong Seo from the School of Energy and Chemical Engineering at UNIST, in collaboration with Professor Ji-Wook Jang’s team from the Department of Materials Science and Engineering at UNIST.
Their research is published in the journal Nature Communications.
This innovative method utilizes biomass derived from sugarcane waste and silicon photoelectrodes to generate H2 exclusively using sunlight, achieving a production rate four times higher than the commercialization benchmark set by the U.S. Department of Energy (DOE).
A team of researchers from the Institute for Basic Science, Yonsei University, and the Max Planck Institute have developed a new artificial intelligence (AI) technique that brings machine vision closer to how the human brain processes images. Called Lp-Convolution, this method improves the accuracy and efficiency of image recognition systems while reducing the computational burden of existing AI models.
The human brain is remarkably efficient at identifying key details in complex scenes, an ability that traditional AI systems have struggled to replicate. Convolutional Neural Networks (CNNs)—the most widely used AI model for image recognition—process images using small, square-shaped filters. While effective, this rigid approach limits their ability to capture broader patterns in fragmented data.
More recently, vision transformers have shown superior performance by analyzing entire images at once, but they require massive computational power and large datasets, making them impractical for many real-world applications.
In an experiment reminiscent of the “Transformers” movie franchise, engineers at Princeton University have created a type of material that can expand, assume new shapes, move and follow electromagnetic commands like a remotely controlled robot, even though it lacks any motor or internal gears.
“You can transform between a material and a robot, and it is controllable with an external magnetic field,” said researcher Glaucio Paulino, the Margareta Engman Augustine Professor of Engineering at Princeton.
In an article published in Nature, the researchers describe how they drew inspiration from the folding art of origami to create a structure that blurs the lines between robotics and materials. The invention is a metamaterial, which is a material engineered to feature new and unusual properties that depend on the material’s physical structure rather than its chemical composition.
By breaking a decades-old paradigm and rethinking the role that the dimension of time plays in physics, researchers from the University of Rostock and the University of Birmingham have discovered novel flashes of light that come from and go into nothingness—like magic at first glance but with deep mathematical roots that protect against all kinds of outside perturbations. Their findings have now been published in the journal Nature Photonics.
Time is the strange dimension: Unlike its spatial siblings, it is a one-way street as the clock only ever ticks forward and never backward. Scientists have long been aware of time’s quirks, with the British astrophysicist Sir Arthur Eddington musing about this “arrow of time” in his 1927 lectures. Nevertheless, whether it be because of or despite its uniqueness, time as a dimension for physics to play out in has long received far less attention than space.
Recently though, rapid progress in the research on so-called spatiotemporal crystals, objects with repeating patterns in time and space, has inspired a rethinking of the role time should play in our understanding of physics. Additionally, this has spawned the question of whether the uniqueness of time can be more than a mere quirk and instead lead to new effects ultimately useful in applications.
QUT researchers have identified a new material which could be used as a flexible semiconductor in wearable devices by using a technique that focuses on the manipulation of spaces between atoms in crystals.
In a study published in Nature Communication, the researchers used “vacancy engineering” to enhance the ability of an AgCu(Te, Se, S) semiconductor, which is an alloy made up of silver, copper, tellurium, selenium and sulfur, to convert body heat into electricity.
Vacancy engineering is the study and manipulation of empty spaces, or “vacancies,” in a crystal where atoms are missing, to influence the material’s properties, such as improving its mechanical properties or optimizing its electrical conductivity, or thermal properties.