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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.
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.
A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.
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.
Digital twins (DTs) have emerged as essential tools for monitoring, predicting, and optimizing physical systems by using real-time data.
Simulation cloning is a technique in which dynamically cloned simulations’ state spaces differ from their parent simulation due to intervening events.
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.