Reusable menstrual cups reduce waste and are more cost-effective than single-use pads and tampons. But some people avoid the cups because they require thorough cleaning and are sometimes messy to empty. To solve these problems, researchers coated a commercially available silicone cup in silicone oil and created a plant-based, absorbent tablet. These design adjustments could make menstrual cups safer and easier to use, according to a study published in ACS Applied Materials & Interfaces.

Power systems are among the most complex man-made systems. However, complexity is not inherently an advantage. In fact, complex dynamics are often the underlying cause of complicated stability issues. In the future 100% renewable power system, converter-interfaced generation (CIG) becomes the main form of power generation, the dynamic of which are dominated by control processes and can be reduced with proper control strategies. Following this idea, researchers at Tsinghua University propose a frequency-fixed grid-forming control (FF-GFM) that controls CIGs as constant voltage sources within their capability limitations. FF-GFM can reduce frequency dynamics and synchronization dynamics, greatly enhancing the stability and safety of the system.

The appearance of a hot sauce or pepper doesn’t reveal whether it’s mild or likely to scorch someone’s taste buds. So, researchers made an artificial tongue to quickly detect spiciness. Inspired by milk’s casein proteins, which bind to capsaicin and relieve the burn of spicy foods, the researchers incorporated milk powder into a gel sensor. The prototype, reported in ACS Sensors, detected capsaicin and pungent-flavored compounds (like those behind garlic’s zing) in various foods.

A team of researchers from Tianjin University has developed a novel tree-like nitrogen-doped carbon (T-NC) support structure that addresses key challenges in fuel cell technology—cost, performance, and durability. Published in Front. Energy, this innovation enables low-platinum (Pt) loaded fuel cells to deliver superior efficiency and longer lifespan, bringing the widespread commercialization of hydrogen-powered vehicles one step closer.

Crystal structure prediction (CSP) of organic molecules is a critical task, especially in pharmaceuticals and materials science. However, conventional methods are computationally intensive and time-consuming. Now, researchers from Japan have developed a new workflow: SPaDe-CSP that accelerates CSP by machine learning-based prediction of most probable space groups and crystal densities and employing an efficient neural network potential for structure refinement. It achieved faster and more reliable CSP than conventional methods.