Engineering topics

Judge, William

Publications and source records attributed to Judge, William.

3 records

DOE OSTI2023

Deep learning at the edge enables real-time streaming ptychographic imaging

Abstract Coherent imaging techniques provide an unparalleled multi-scale view of materials across scientific and technological fields, from structural materials to quantum devices, from integrated circuits to biological cells. Driven by the construction of brighter sources and high-rate detectors, coherent imaging methods like ptychography are poised to revolutionize nanoscale materials characterization. However, these advancements are accompanied by significant increase in data and compute needs, which precludes real-time imaging, feedback and decision-making capabilities with conventional approaches. Here, we demonstrate a workflow that leverages artificial intelligence at the edge and high-performance computing to enable real-time inversion on X-ray ptychography data streamed directly from a detector at up to 2 kHz. The proposed AI-enabled workflow eliminates the oversampling constraints, allowing low-dose imaging using orders of magnitude less data than required by traditional methods.

36 MATERIALS SCIENCE↗

DOE OSTI2023

3D Quantification of Elemental Gradients within Heterostructured Particles of Battery Cathodes

Heterogenous architectures with elemental gradients tailored within particles have been pursued to combat the instabilities limiting Ni-rich cathode materials for lithium-ion batteries. The growth of different compositional layers is accomplished during the synthesis of hydroxide precursors. However, the extent to which these concentration gradients are modified during high-temperature reactions is difficult to establish in their intact, spherical form. Here, we show the entire three-dimensional structure of a secondary particle can be resolved nondestructively with differential X-ray absorption spectroscopy (XAS) through transmission X-ray microscopy (TXM). The relationship between particle location and elemental content was fully quantified, with high statistical significance, for heterostructures possessing different compositional gradients in the precursors with 90:5:5 Ni:Mn:Co core compositions. Reduced elemental heterogeneity was observed after high-temperature synthesis, but gradients remained. In conclusion, the methodology presented should be used to guide synthesis while assuring that gains in electrochemical performance are linked to precise elemental distributions at the nanoscale.

25 ENERGY STORAGE↗

DOE OSTI2022

Defect identification in simulated Bragg coherent diffraction imaging by automated AI

X-ray Bragg coherent diffraction imaging is a powerful technique for operando and in situ materials characterization and provides a unique means of quantifying the influence of one-dimensional (1D) and two-dimensional (2D) material defects on material response. However, obtaining full images from raw x-ray diffraction data is nontrivial and computationally intensive, precluding real-time experimental feedback. Here, we present a machine learning approach to identify the presence of crystalline line defects (edge and screw) in samples from the raw, 2D, coherent diffraction data without the need for image reconstruction through iterative phase retrieval. Further, we compare different approaches to designing neural networks for this application and demonstrate the potential of automated ML (autoML) approaches.

36 MATERIALS SCIENCE↗