In the lab, perovskite solar cells show high efficiency in converting solar energy into electricity. In combination with silicon solar cells, they could play a role in the next generation of photovoltaic systems. Now researchers at KIT have demonstrated that machine learning is a crucial tool for improving the data analysis required needed for commercial fabrication of perovskite solar cells. They present their results in Energy and Environmental Science. DOI: 10.1039/D4EE03445G 

Breakthrough in N₂ activation! This study unveils an electron catalysis strategy that directly converts N₂ into azo compounds under mild conditions—a game-changer for nitrogen fixation. By harnessing electron flow, it lowers activation energy from 3.44 eV to 0.14 eV, making azo synthesis faster, greener, and highly efficient. This innovative approach revolutionizes nitrogen chemistry, unlocking new possibilities for sustainable chemical synthesis.

An international team of scientists has synchronized key climate records from the Atlantic and Pacific Oceans to unravel the sequence of events during the last million years before the extinction of the dinosaurs at the Cretaceous/Paleogene boundary. New high resolution geochemical records for the first time reveal when and how two major eruption phases of gigantic flood basalt volcanism had an impact on climate and biota in the late Maastrichtian era 66 to 67 million years ago. Their study was now published in Science Advance.

Biologists have discovered a low-cost way to more easily study the detailed makeup of plant cells. The ultimate goal is to help grow better crops, improving food security.

Producing high-performance titanium alloy parts — whether for spacecraft, submarines or medical devices — has long been a slow, resource-intensive process. Even with advanced metal 3D-printing techniques, finding the right manufacturing conditions has required extensive testing and fine-tuning.

What if these parts could be built more quickly, stronger and with near-perfect precision?

A team comprising experts from the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, and the Johns Hopkins Whiting School of Engineering is leveraging artificial intelligence to make that a reality. They’ve identified processing techniques that improve both the speed of production and the strength of these advanced materials — an advance with implications from the deep sea to outer space.

In an impressive breakthrough in surface science, a novel method has been developed to simulate and visualize the local dipole moments on the semiconductor surface using scanning nonlinear dielectric microscopy (SNDM) paired with density functional theory (DFT) calculations. This study, led by researchers Akira Sumiyoshi, Kohei Yamasue, Yasuo Cho, and Jun Nakamura, not only enhances the understanding of surface dielectric properties but also holds promising implications for future semiconductor device engineering and material science applications.