Scientists at SLAC National Accelerator Laboratory, alongside two researchers from Penn State, have demonstrated the importance of generating highly reproducible experimental data when building AI models for catalyst investigations, a framework that can help guide the reactions that do things like turn carbon dioxide into valuable fuel.

SPIE proudly announces that it is now accepting submissions for Advanced Quantum Photonics (AQP), the scholarly publisher’s newest journal. As the first peer-reviewed journal for applied quantum research, AQP fills a critical gap in the quantum research publishing landscape, enabling collaboration and further advancing this critical field. Launching AQP was a natural extension of the growing support and opportunities SPIE offers to meet the demands of this rapidly evolving field.

Two recent scientific publications from the Fraunhofer Institute for Applied Solid State Physics IAF shed light on how the concept of quantum advantage can be assessed more precisely and realistically in the future. Both papers provide theoretical tools to make claims about quantum advantage more robust. Their content is complementary: one addresses quantum chemistry beyond idealized system models, while the other examines how algorithmic advantages can be reliably demonstrated through the scaling of problem sizes.

Adding iron to the ocean can cause plankton to absorb more CO2 from the atmosphere. However, the effect depends on where in the world the method is used, and it can have far-reaching consequences for marine ecosystems and food chains, according to a new study in Nature.

A physics-guided AI framework automatically optimizes 2.4 GHz CMOS low-noise amplifiers, reducing power consumption by 62% and improving linearity by 10.7 dB compared with an expert-designed baseline, while nearly doubling the rate of successful circuit simulations under foundry design constraints.

A study by the Max Planck Institute for Marine Microbiology shows that animals such as marine worms and earthworms possess enzymes capable of breaking down the natural bioplastic PHA. This provides new insights into the carbon cycle and the biological utilisation of microbial bioplastics.

A research team from Shanghai Jiao Tong University has developed a physics-informed neural network for distributed acoustic sensing. The approach generates synthetic event data and removes background noise without relying on real-world event data. Demonstrated in public datasets and belt conveyor fault monitoring, the method achieves high accuracy and generalizes across different sites, offering a scalable solution for industrial monitoring and optical sensing applications.