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ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
A novel strategy was developed to solve the limitations of the current sorbent systems in CO2 chemisorption in terms of energy consumption in CO2 release and improved CO2 uptake capacity.
The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).
This invention introduces a novel sintering approach to produce hard carbon with a finely tuned microstructure, derived from biomass and plastic waste.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
The increasing demand for high-purity lanthanides, essential for advanced technologies such as electronics, renewable energy, and medical applications, presents a significant challenge due to their similar chemical properties.
With the ever-growing reliance on batteries, the need for the chemicals and materials to produce these batteries is also growing accordingly. One area of critical concern is the need for high quality graphite to ensure adequate energy storage capacity and battery stability.
The QVis Quantum Device Circuit Optimization Module gives users the ability to map a circuit to a specific quantum devices based on the device specifications.
QVis is a visual analytics tool that helps uncover temporal and multivariate variations in noise properties of quantum devices.
Electrochemistry synthesis and characterization testing typically occurs manually at a research facility.