ORNL Highlights

1-10 of 235 Results

Materials scientists use ORNL’s CADES to transform big data to ‘smart data’ for rapid image analysis
— Materials scientists use ORNL’s CADES to transform big data to ‘smart data’ for rapid image analysis. ORNL material sciences researchers are collaborating with computer scientists in ORNL’s Compute and Data Environment for Science (CADES) to create a processing and analysis workflow for the expansive scanning probe and electron microscopy data generated at the Center for Nanophase Materials Sciences (CNMS).

Researchers use machine learning to find useful structural properties in neutron and x-ray data
— Using CADES compute and data resources, researchers are linking DOE experimental and computational facilities to uncover stacking faults in double-layered perovskite. Here is the title and blurb to use on the webpage: Researchers use machine learning to find useful structural properties in neutron and x-ray data. A team of ORNL researchers is using the lab’s Compute and Data Environment for Science (CADES) to analyze large volumes of neutron and x-ray scattering data to find and identify these defects—a first step to greatly reducing time researchers spend on comparing and contrasting scattering data to identify connections between structure and function.

True structure of pnictide 122 superconductors revealed
— High-resolution microscopy revealed an unexpected room-temperature crystal structure of the ‘122’ Ba(Fe1-xCox)2As2 superconductors, with domains similar to those in ferroelectrics but with nanometer size.

New model predicts formation of stable high-entropy alloys
— Researchers devised a model that can predict which combinations of 5 or more elements will form new “high-entropy alloys.” This work, which utilizes values obtained from data mining of high-throughput calculations of binary compounds, requires no experimental or empirically derived input and advances capabilities for “materials by design.

Atomic-Scale Observations Aid Mesoscale Catalyst Design
— Two phases of Mo-V-O–based oxides, M1 and M2, are promising catalysts for direct conversion of propane to acrylonitrile and are believed to act synergistically. Researchers engineered the mesoscale structure of M1- and M2-phase oxides to amplify these effects, greatly improving selectivity for propane ammoxidation.

Technique Recovers Atomic Resolution in Spectrum Images
— Researchers have demonstrated a technique for obtaining atomic-resolution information from spectrum images of thick specimens of MnFePSi compounds, which are promising for ecofriendly refrigeration. This technique allows the quantitative examination of specimens for which atomic-resolution spectroscopic analysis was previously impossible.

World's Thinnest Proton Channel
— Graphene is a single-atom thin 2-dimensional array of carbon atoms that represents a barrier that is impenetrable even to protons unless graphene membrane has macroscopic holes.

Dynamic coupling drives conformational evolution of branched polymers in solutions
— The critical overlap concentration of polymer solutions, denoted c*, is one of the most important characteristic values of a polymer solution. This geometrically defined parameter is used to identify concentration regimes with different conformational characteristics.

Electronic Excitations Transform Structure of Ceramics
Ab initio molecular dynamics calculations reveal that electronic excitations induce a structural instability that transforms Y2Ti2O7, Gd2Ti2O7 and Sm2Ti2O7 with the pyrochlore crystal structure to an amorphous state.

New Atomic Force Microscope Spectroscopy Probes Local Elasticity
— Contact resonance imaging and voltage spectroscopy based on photothermal excitation were developed to explore local bias-induced phenomena. These techniques can access nanoscale elastic properties in real time during polarization switching in ferroelectric nonvolatile memories, and during ion intercalation in batteries and supercapacitors.

 
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