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1–1 of 1Researchers have found similarities in how concepts of energy, pressure, and confinement apply to atomic nuclei and superconductivity. Specifically, in both hadrons and superconductors, how particles are confined to a specific volume can be described with the same mathematical framework derived from quantum chromodynamics.
Researchers have found similarities in how concepts of energy, pressure, and confinement apply to atomic nuclei and superconductivity. Specifically, in both hadrons and superconductors, how particles are confined to a specific volume can be described with the same mathematical framework derived from quantum chromodynamics.
Polyphenols are generally toxic to microorganisms. In peatlands, scientists thought microorganisms avoided this toxicity by degrading polyphenols using an oxygen-dependent enzyme, and thus that low-oxygen conditions inhibit microbes’ carbon cycling.
Polyphenols are generally toxic to microorganisms. In peatlands, scientists thought microorganisms avoided this toxicity by degrading polyphenols using an oxygen-dependent enzyme, and thus that low-oxygen conditions inhibit microbes’ carbon cycling.
Researchers at Berkeley Lab have successfully demonstrated an innovative approach to find breakthrough materials for quantum applications. The approach uses rapid computing methods to predict the properties of hundreds of materials, identifying short lists of the most promising ones.
Researchers at the Advanced Photon Source and Center for Nanoscale Materials of the U.S. Department of Energy’s Argonne National Laboratory have developed a new technique that pairs artificial intelligence and X-ray science.
The fusion of two nuclei is a complex process influenced by the relative energy and angular momentum of the nuclei and how their structures evolve as they collide. In this study, the researchers performed the most comprehensive computation to date of fusion reaction processes.
Scientists are using AI to counter the effects of the atmosphere and provide clearer data to satellites, using physics-informed machine learning.
Researchers grew crystals containing actinium and illuminated them with X-rays to learn how the radioactive metal binds with other elements. That information could help design better cancer treatments.
Because of the number and density of neutrinos involved, it is nearly impossible to calculate the movement of neutrinos from compact astrophysical systems such as core-collapse supernovae and neutron star mergers.
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