A research team at the Politecnico di Milano has developed an innovative single-atom catalyst capable of selectively adapting its chemical activity. This is a crucial step forward in sustainable chemistry and the design of more efficient and programmable industrial processes.

The study was published in the Journal of the American Chemical Society, one of the world’s most authoritative scientific journals in chemistry.

Even a toddler knows that plants need water. It’s perhaps the first thing we learn about these green lifeforms. But how plants budget this resource varies considerably. The kapok trees of the Amazon have adopted vastly different strategies than the switchgrass of the American plains. Unfortunately, it’s hard to directly measure which ones prevail in different ecosystem types and how they shift under changing conditions.

Industrial anomaly detection is crucial for maintaining quality control and reducing production errors, but traditional supervised models require extensive datasets. While embedding-based methods are promising for unsupervised anomaly detection, they are highly memory-intensive and unsuited to low-light conditions. In a new study, researchers developed a new unsupervised model that utilizes both well-lit and low-light images to achieve computationally efficient and memory-friendly industrial anomaly detection.