Xu, Xiaohui | Engineering Papers

DOE OSTI2024

Direct Visualization of Laser-Induced Coalescence and Alloying of Au into Ag Nanowires

Noble metal nanocomposites with controlled morphology and composition have been largely useful in a wide variety of applications like catalysis, plasmonics, and biological applications. Herein, we show pulsed-laser-assisted fabrication of beaded Au-Ag nanowires with a variety of morphologies and compositions via coalescence and alloying of Au nanoparticles (NP) into a Ag nanowire (NW). Direct visualization of the evolution process with in situ laser heating transmission electron microscopy (ILH-TEM) leads to unambiguous insights into the mechanism of formation for the first time. Ag, being a soft material, melts at the surface to bridge the gap between the NP-NW, forming a cup-like morphology underneath the Au NP via capillary action. Progressive laser irradiation leads to wetting of the Au NP with Ag, forming an envelope around the NP. This is associated with the formation of a valley in Ag NW around the NP, which flattens gradually by partial embedding of the NP. Interdiffusion of Au into Ag and vice versa sets in at this stage, leading to the depletion of Au from the Au-rich NP region. Prolonged irradiation leads to gradual intermixing of Au-Ag forming beaded Au-doped Ag nanowires with homogeneous composition. Such a step-by-step understanding of the coalescence and alloying process has implications in nanowelding, which holds the future in designing efficient transparent conductors and printed electronics.

Isik, Tugba↗

DOE OSTI2023

Machine learning assisted quantum super-resolution microscopy

One of the main characteristics of optical imaging systems is spatial resolution, which is restricted by the diffraction limit to approximately half the wavelength of the incident light. Along with the recently developed classical super-resolution techniques, which aim at breaking the diffraction limit in classical systems, there is a class of quantum super-resolution techniques which leverage the non-classical nature of the optical signals radiated by quantum emitters, the so-called antibunching super-resolution microscopy. This approach can ensure a factor of $\sqrt{n}$ improvement in the spatial resolution by measuring the n -th order autocorrelation function. The main bottleneck of the antibunching super-resolution microscopy is the time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed framework paves the way to the practical realization of scalable quantum super-resolution imaging devices that can be compatible with various types of quantum emitters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗