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A photonic generative adversarial network that harnesses optoelectronic noises to generate handwritten numbers is demonstrated. 

Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.

 

Chalcogenide phase change materials (PCMs) have become one of the most promising material platforms for the Optics and Photonics community. The unparalleled combination of nonvolatility and large optical property modulation promises devices with low‐energy consumption and ultra‐compact form factors. At the core of all these applications lies the difficult task of precisely controlling the glassy amorphous and crystalline domains that compose the PCM microstructure and dictate the optical response. A spatially controllable glassy‐crystalline domain distribution is desired for intermediate optical response (vs. binary response between fully amorphous and crystalline states), and temporally resolved domains are sought after for repeatable reconfiguration. In this perspective, we briefly review the fundamentals of PCM phase transition in various reconfiguring approaches for optical devices. We discuss each method's underpinning mechanisms, design, advantages, and downsides. Finally, we lay out current challenges and future directions in this field.

 

Despite the immense importance of ceria–zirconia solid solutions in heterogeneous catalysis, and the growing consensus that catalytic activity correlates with the concentration of reduced Ce 3+ species and accompanying oxygen vacancies, the extent of reduction at the surfaces of these materials, where catalysis occurs, is unknown. Using angle-resolved X-ray Absorption Near Edge Spectroscopy (XANES), we quantify under technologically relevant conditions the Ce 3+ concentration in the surface (2–3 nm) and bulk regions of ceria–zirconia films grown on single crystal yttria-stabilized zirconia, YSZ (001). In all circumstances, we observe substantial Ce 3+ enrichment at the surface relative to the bulk. Surprisingly, the degree of enhancement is highest in the absence of Zr. This behavior stands in direct contrast to that of the bulk in which the Ce 3+ concentration monotonically increases with increasing Zr content. These results suggest that while Zr enhances the oxygen storage capacity in ceria, undoped ceria may have higher surface catalytic activity. They further urge caution in the use of bulk properties as surrogate descriptors for surface characteristics and hence catalytic activity.