Stardust Solutions has raised $60 million to develop technology that could artificially cool the planet by reflecting sunlight. The substantial private funding represents a significant departure from previous academic-led geoengineering efforts and highlights growing investor interest in radical cli
Google’s president and chief investment officer Ruth Porat made bold predictions about AI’s potential to solve humanity’s biggest challenges at the Fortune Global Forum. She argued the technology represents far more than chatbots, pointing to breakthroughs in healthcare and the urgent need for infra
Researchers at Lawrence Berkeley National Laboratory have pioneered ionocaloric cooling, a novel refrigeration technique that uses electrical currents and salt to manipulate material phases. The method achieved a 25°C temperature shift using less than one volt, potentially offering a sustainable alternative to current refrigerants with high global warming potential.
Scientists at Lawrence Berkeley National Laboratory and UC Berkeley have reportedly developed what could be the most promising alternative to conventional refrigeration in decades. Their ionocaloric cooling method represents a fundamental shift from the vapor-compression systems that have dominated refrigeration for over a century, according to research published in Science.
Scientists have uncovered the mechanism by which bacterial immune systems detect viral invaders. New research reveals that Thoeris defense systems directly recognize phage capsid proteins to initiate a protective response that halts viral replication through NAD+ depletion.
Researchers have identified how bacterial immune systems directly detect viral invaders by recognizing specific capsid proteins, according to a recent study published in Nature Microbiology. The findings reveal that Thoeris defense systems, found in approximately 4% of bacterial and archaeal genomes, function as structural and functional analogues to innate immunity in animals and plants.
Researchers have created a novel AI system that identifies emerging scientific topics using a unique approach inspired by emperor penguin huddling behavior. The framework combines multiple advanced algorithms to help scholars navigate the rapidly expanding landscape of academic literature more effectively.
Scientists have developed an innovative artificial intelligence system that reportedly revolutionizes how research topics are detected across scientific domains, according to recent reports. The hybrid framework combines multiple advanced algorithms, including one inspired by emperor penguin behavior, to help researchers identify emerging trends and popular topics within massive scientific databases.
A Colorado technology company has developed a catalytic system that destroys PFAS contamination at the molecular level. The breakthrough represents a paradigm shift in addressing the persistent environmental threat of forever chemicals.
PFAS chemicals, commonly known as ‘forever chemicals,’ have emerged as one of the most persistent environmental contaminants worldwide, according to environmental reports. These synthetic compounds, used extensively in manufacturing, firefighting foams, and consumer products, resist natural degradation and accumulate in ecosystems and human bodies over time. Sources indicate that regulatory agencies globally are increasingly recognizing the serious ecological and health risks posed by these substances.
Chemists have developed a groundbreaking computational tool that offers unprecedented visibility into RNA behavior within living cells. The publicly available model could transform understanding of cellular malfunctions linked to devastating diseases like ALS and cancer.
Researchers at the University of Massachusetts Amherst have developed what sources indicate is a revolutionary computational tool that provides an unrivaled view into the mysterious world of RNA within living cells. The tool, called iConRNA, reportedly offers scientists their clearest look yet at how RNA molecules behave in the crowded cellular environment and could help solve longstanding mysteries about how devastating diseases develop.
Advanced computational models suggest ultracold polar molecules can form previously unseen self-bound states, including superfluid membranes and 2D crystals. These findings, based on realistic experimental parameters, may soon be testable in laboratory settings.
Recent simulations indicate that ultracold polar molecules could form novel strongly correlated states of matter, according to research published in Physical Review Letters. Scientists from TU Wien and the Vienna Center for Quantum Science and Technology reportedly used advanced computational methods to model behaviors in Bose-Einstein condensates (BECs) of polar molecules, which were first experimentally realized in 2023. The study suggests these systems may self-organize into quantum droplets, superfluid layers, and crystalline structures without external confinement.
A new diagnostic approach using fused machine learning models shows promise in improving preoperative classification of ovarian tumors. The integrated system combines blood test analysis with MRI interpretation to help distinguish between borderline and malignant cases.
Researchers have developed an innovative artificial intelligence approach that reportedly improves preoperative classification accuracy for ovarian tumors, according to recent findings published in Scientific Reports. The multimodal integration method combines machine learning analysis of blood tests with deep learning interpretation of MRI scans to better distinguish between borderline ovarian tumors (BOTs) and malignant ovarian tumors (MOTs) before surgery.
New research compares deep learning approaches for predicting lithium-ion battery degradation. The study reveals Multilayer Perceptron and Temporal Convolutional Network models achieve exceptional accuracy while maintaining computational efficiency suitable for embedded systems.
Recent scientific research has demonstrated significant advances in predicting lithium-ion battery health using deep learning architectures, according to reports published in Scientific Reports. The study comprehensively evaluated four neural network models for state of health (SoH) estimation, a critical parameter for ensuring reliability in electric vehicles and energy storage systems. Sources indicate that the findings could have substantial implications for battery management systems and predictive maintenance platforms.
