Researchers are exploring how to produce more sustainable chemicals and fuels.

A new research paper introduces a paradigm shift in twisted materials, moving beyond the traditional K-point twisting to explore the M-point of electron momentum. This new approach unlocks a class of twisted quantum materials with unique properties, including flattened electron bands and the potential for realizing elusive quantum spin liquids. The research, involving a global collaboration, has already led to the synthesis of candidate materials, paving the way for experimental realization and potential technological applications.

Researchers have developed a new photonic neural network architecture that significantly improves task accuracy by leveraging the physical properties of light. Unlike traditional designs that mimic digital neural networks, this system uses physical transformations and multisynaptic optical paths to process information directly, avoiding errors from digital modeling and simulation. Tested on benchmark datasets including MNIST, Fashion-MNIST, and CIFAR-10, the network achieved classification accuracies of 99.79, 98.26, and 90.29 percent, respectively—outperforming both digital counterparts and existing hardware systems. The findings mark a major step toward more efficient and accurate AI hardware.

Controlling regioselectivity remains one of the major challenges in hydrocyanation. When an unsymmetrical alkyne undergoes this reaction, it typically yields a mixture of regioisomers. This complicates product separation, making it more difficult and costly. Now, scientists from the Institute of Physical Chemistry of the Polish Academy of Sciences have demonstrated a novel approach that addresses this problem. Their breakthrough enables full control over the reaction outcome — making hydrocyanation greener, more efficient, and more economical than ever before. Here is how it works.

Researchers at Southern University of Science and Technology developed an adaptive beam shaping method for laser micro-grooving to shape tiny grooves with sub-micron accuracy—even in hard-to-machine materials like silicon carbide.

By combining smart simulations and real-time adjustments, their system “teaches” lasers to self-compensation deviation between experimental and target results caused by diffraction and polarization, achieving 5× higher precision than traditional patterned laser ablation methods.

“Here, you can think of the laser as a shaped knife, and you can achieve the desired groove shape with a single stroke”, says Prof. Shaolin Xu.