Researchers developed a robust, cost-effective, and easy-to-use cap-based technique for spatial proteome mapping, addressing the lack of accessible proteomics technologies for studying tissue heterogeneity and microenvironments.
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.
A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.
