For over 100 years, physicists, chemists, and materials scientists have developed extensive theoretical and experimental machinery to predict and characterize the electronic properties of magnetic ...
Understanding the influence of quasiperiodicity on magnetic fluctuations could ultimately enable the design of materials with ...
A research team at The University of Tokyo has developed a powerful machine learning algorithm that predicts the properties and structures of unknown samples from an electron spectrum. This process ...
Separating heavy product from light has never been easier, thanks to General Kinematics’ DE-STONER ® Air Classifier. The DE-STONER ® uses an air classification system that makes it uniquely adaptable ...
Researchers have developed a deep learning-based approach that significantly streamlines the accurate identification and classification of two-dimensional (2D) materials through Raman spectroscopy. In ...
Carbon-based stimuli-responsive nanomaterials are gaining much attention due to their versatility, including disease diagnosis and treatment. They work under endogenous (pH, temperature, enzyme, and ...
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