This research offers valuable insights into optimizing the structure of platinum-based catalysts to enhance their catalytic activity and stability. The ultra-low platinum loading ORR electrocatalyst developed in this study has the potential to significantly reduce the cost of fuel cells and zinc-air batteries, making them more viable for commercial applications. The findings pave the way for the development of more efficient and cost-effective energy conversion devices.

Using the Summing Nal Detector (SuN), a team of experimental and theoretical scientists took another step in understanding how an atomic nucleus can have two different shapes corresponding to only slightly different energy levels.

New research published by Michigan State University astrophysicists could help scientists answer a century-old question: where did galactic cosmic rays come from? 

A study in Science Advances that describes a new soft robotic system was co-led by Harvard Professor L. Mahadevan in collaboration with Professor Ho-Young Kim at Seoul National University. Their work paves new directions for future, low-power swarm robotics.

The new robots, called link-bots, are comprised of centimeter-scale, 3D-printed particles strung into V-shaped chains via notched links and are capable of coordinated, life-like movements without any embedded power or control systems. 

Researchers at Rice have developed a soft robotic arm capable of performing complex tasks such as navigating around an obstacle or hitting a ball, guided and powered remotely by laser beams without any onboard electronics or wiring.

Polarized terahertz (THz) waves offer unique advantages for diagnostic medical imaging, but scattering mechanisms in tissue remain unclear. To address this knowledge gap, researchers from Stony Brook University developed a comprehensive model of polarized THz wave scattering from biological structures. Using simulations, tissue phantoms, and porcine skin samples as validation, they showed that microscopic tissue changes can be identified from polarization measurements, offering a promising path for the early detection of cancer.

Cambridge University Press has published a new book co-authored by researchers from the University of Warsaw, offering both an introduction to machine learning and deep neural networks, and an overview of their applications in quantum physics and chemistry — from reinforcement learning for controlling quantum experiments to neural networks used as representations of many-body quantum states.The book appears at a time when artificial intelligence is becoming an increasingly recognized tool for scientific discovery — a development recently recognized with the Nobel Prize in Chemistry awarded for the AlphaFold tool. It serves as a timely guide for PhD students and researchers looking to apply modern machine learning methods to complex quantum problems.