RESUMO
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.
RESUMO
Recycling iron and steel is critical for environmental sustainability and essential to close material loops in circular economics. A major challenge is to produce high-value products and to control impurities like carbon in the face of stringent consumer requirements and volatile markets. Here, we develop an electrorefining process that directly decarburizes molten iron by imposing an electromotive force between it and a slag electrolyte. Upon anodic polarization, oxide anions from the slag discharge directly on carbon dissolved in molten iron, evolving gaseous carbon monoxide. In a striking departure from conventional practice that highly relies on reaction with solubilized oxygen, here electrorefining achieves decarburization by direct interfacial reaction. We demonstrate that this technique produces ultra-low-carbon steels and recovers silicon as a by-product at the cathode, requiring a low energy input and no reagents. We expect this process to be scalable and integrable with secondary steel mills.
