Spin-state tuning in PrFeO3-δ perovskite boosts high-temperature oxygen evolution reaction
Peer-Reviewed Publication
Updates every hour. Last Updated: 26-Dec-2025 02:11 ET (26-Dec-2025 07:11 GMT/UTC)
From waste to valuable resource: Chemists at the University of Copenhagen have developed a method to convert plastic waste into a climate solution for efficient and sustainable CO2 capture. This is killing two birds with one stone as they address two of the world’s biggest challenges: plastic pollution and the climate crisis.From waste to valuable resource: Chemists at the University of Copenhagen have developed a method to convert plastic waste into a climate solution for efficient and sustainable CO2 capture. This is killing two birds with one stone as they address two of the world’s biggest challenges: plastic pollution and the climate crisis.
But a recent discovery by a multi-university collaboration of researchers, led by Drexel University researcher Yury Gogotsi, PhD, and Drexel alumnus Babak Anasori, PhD, who is now an associate professor at Purdue University, that sheds light on the thermodynamics undergirding the materials’ unique structure and behavior, could be the key to supercharging the development of two-dimensaional materials with artificial intelligence technology. The discovery was recently reported in the journal Science.
This article examines the potential of Artificial Intelligence-driven Distributed Acoustic Sensing (AI+DAS) technology in engineering applications. Based on fiber optic monitoring, DAS enables real-time acoustic signal monitoring by detecting disturbances along the fiber, offering long measurement distances, high spatial resolution, and a large dynamic range. The article outlines the basic principles and demodulation methods of DAS using Φ-OTDR technology, highlighting AI's role in data processing and event recognition. By integrating AI algorithms, DAS systems enhance monitoring accuracy and reliability. Additionally, the article reviews AI+DAS applications across various fields, including engineering and geology, and discusses challenges such as model complexity and resource demands. Overall, it aims to foster interdisciplinary collaboration and support digital transformation in industrial scenarios.