In a study published in Nature Physics, researchers pioneered, with the worldwide advent of new coherent X-ray sources, the experimental and analysis methods will enable broad application of XPCS to observe atomic-scale processes on surfaces.
In a study published in Small, Center for Nanoscale Materials researchers created a protocol for controlling shell morphology in water-processed semiconductor nanoparticles and revealed the dependence of charge separation efficiency on shell morphology.
An experiment at the Argonne Wakefield Accelerator demonstrates the potential of a novel metamaterial structure to yield higher accelerating gradients than current particle accelerator technology provides.
Two new methods reduce noise and remove errors in quantum observables by focusing on individual noise sources. They add little qubit overhead and can be used in quantum sensing and general quantum experimentation, as well as quantum computing.
In a Nature Communications article, a team led by Center for Nanoscale Materials researchers introduces a machine learning workflow of models for water transformations that increases accuracy at lower computational cost.
Using a single actuation signal, a frequency comb is generated in a micromechanical resonator from two vibrational modes, flexural and torsional, whose interactions are responsible for the unique response.