A switchable single-atom catalyst is activated in the presence of surface intermediates and reverts to its stable inactive form when the reaction is completed.
In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.
To improve our ability to “see” into the subsurface, scientists need to understand how different mineral surfaces respond to electrical signals at the molecular scale.
Catalysts that efficiently transfer hydrogen for storage in organic hydrogen carriers are key for more sustainable generation and use of hydrogen. New research identifies activity descriptors that can accelerate novel catalyst development.
Highly precise and controllable single-atom catalysts are affected by reaction conditions, which can alter the bonding around the atoms and the activity.