Quad bike deaths are dropping, but experts say more action required
Peer-Reviewed Publication
Updates every hour. Last Updated: 23-Jan-2026 23:11 ET (24-Jan-2026 04:11 GMT/UTC)
The relentless pursuit of advanced X-ray detection technologies has been significantly bolstered by the emergence of metal halides perovskites (MHPs) and their derivatives, which possess remarkable light yield and X-ray sensitivity. This comprehensive review delves into cutting-edge approaches for optimizing MHP scintillators performances by enhancing intrinsic physical properties and employing engineering radioluminescent (RL) light strategies, underscoring their potential for developing materials with superior high-resolution X-ray detection and imaging capabilities. We initially explore into recent research focused on strategies to effectively engineer the intrinsic physical properties of MHP scintillators, including light yield and response times. Additionally, we explore innovative engineering strategies involving stacked structures, waveguide effects, chiral circularly polarized luminescence, increased transparency, and the fabrication of flexile MHP scintillators, all of which effectively manage the RL light to achieve high-resolution and high-contrast X-ray imaging. Finally, we provide a roadmap for advancing next-generation MHP scintillators, highlighting their transformative potential in high-performance X-ray detection systems.
A research team has revealed key trends, research hotspots, and emerging collaborations in herbal tea research over the past two decades.
Researchers from New Jersey Institute of Technology (NJIT) have used artificial intelligence to tackle a critical problem facing the future of energy storage: finding affordable, sustainable alternatives to lithium-ion batteries.
The NJIT team successfully applied generative AI techniques to rapidly discover new porous materials capable of revolutionizing multivalent-ion batteries. These batteries, using abundant elements like magnesium, calcium, aluminum and zinc, offer a promising, cost-effective alternative to lithium-ion batteries, which face global supply challenges and sustainability issues.
Lighting plays a crucial role when it comes to visual storytelling. Whether it’s film or photography, creators spend countless hours, and often significant budgets, crafting the perfect illumination for their shot. But once a photograph or video is captured, the illumination is essentially fixed. Adjusting it afterward, a task called “relighting,” typically demands time-consuming manual work by skilled artists.
While some generative AI tools attempt to tackle this task, they rely on large-scale neural networks and billions of training images to guess how light might interact with a scene. But the process is often a black box; users can’t control the lighting directly or understand how the result was generated, often leading to unpredictable outputs that can stray from the original content of the scene. Getting the result one envisions often requires prompt engineering and trial-and-error, hindering the creative vision of the user.
In a new paper to be presented at this year's SIGGRAPH conference in Vancouver, researchers in the Computational Photography Lab at SFU offer a different approach to relighting. Their work, “Physically Controllable Relighting of Photographs”, brings explicit control over lights, typically available in Computer Graphics software such as Blender or Unreal Engine, to image and photo editing.
A new paper published today in Cell highlights how researchers have leveraged AI-based computational protein design to create a novel synthetic ligand that activates the Notch signaling pathway, a key driver in T-cell development and function. These so-called soluble Notch agonists can be broadly applied to optimize clinical T-cell production and advance immunotherapy development.