FIP Student Speakers Seminar: Luca Menozzi & Renee George

Presenter: Luca Menozzi, PhD Candidate, BME, Duke University and Renee George, ECE, Duke University

This is a two part seminar. There are two FIP students awarded to speak during the seminar:

1)Luca Menozzi "Diffractive Acoustic Tomography: A Novel Method for Isotropic 3D Optical and Acoustic Bioimaging 
Photoacoustic (PA) and ultrasound (US) imaging systems have many options for the method of ultrasonic detection. Linear-array transducers provide the benefits of low cost, large field-of-view, and fast scanning. However, a traditional scanning linear-array system achieves low resolution and sensitivity along the scanning dimension (elevational axis) due to the fixed acoustic lens in both PA and US imaging. As a solution, we present diffractive acoustic tomography (DAT), a technique that employs a focused transducer array with a secondary aperture to alter the far-field acoustic beam pattern, ultimately allowing for PA and US volumes with isotropic resolution and sensitivity (see Fig. 1a, b). Furthermore, we have developed a novel fast focal line (FFL) image reconstruction technique based on sparse matrix multiplication. FFL reduces the complete 3D reconstruction time by nearly two orders of magnitude in comparison with previously described methods, making DAT a practical real-time imaging method. We have applied our DAT system to three preliminary life science and medical studies (see Fig. 1c). First, volumetric estimations of biliverdin binding serpin (BBS) and hemoglobin (Hb) concentrations in six species of South American glass frogs. Second, dynamic tracking of gold nano stars (GNS) in a tumor mouse model. Third, longitudinal imaging of placenta and embryo blood oxygenation throughout pregnancy.

2) Renee George "Synergy of Machine Learning and Mie-tronics: Experimental Verification of Inversely Designed Meta-atomsDimensional Sensing Applications"

Dielectric, Mie resonant nanostructures have attracted attention for their ability to induce magnetic and electric multipoles with low losses at optical frequencies. These nanostructures can function as unit cells, or meta-atoms, for sub-wavelength thick, patterned materials with tailored properties, called Metasurfaces, that confine and interact strongly with light. A majority of reported dielectric Metasurfaces are composed of relatively simple meta-atoms such as cubes and cylinders, whose electromagnetic response is dominated by the electric dipole. However, magnetic dipoles and higher-order multipoles induced by irregularly shaped meta-atoms enable several functionalities including directional scattering, beam steering, and new frequency generation. Recently, Professor Natalia Litchinitser’s Nanophotonics and Nonlinear Optics (NANO) group at Duke has developed a machine learning toolbox including an inverse design module (IDM) that predicts meta-atoms geometries with desired multipolar responses at operating wavelengths (see figure below). Experimental verification of the optical response of these meta-atoms is necessary to determine the applicability of the design to real life implementations. Here, I apply the IDM to predict three nanoparticle shapes with electric dipole, magnetic dipole, and magnetic quadrupole resonances at three operating wavelengths. Using finite-element-based numerical simulations implemented in COMSOL Multiphysics, I analyze different realistic conditions that may affect the optical response of the meta-atoms such as the inclusion of a glass substrate, and a periodic array of meta-atoms. I then fabricate periodic arrays of titanium dioxide meta-atoms on top of a glass substrate via electron beam lithography and measure the optical response using a white light spectroscopy setup (see figure below). Comparing the spectral features of the scattering cross-section of the fabricated samples with the desired multipolar response, I found the suppression and/or emergence of scattering modes is robust against changes in the dielectric environment and periodic configuration. Our findings serve as the first experimental verification of ML-based Mie-tronics, and will possibly facilitate fast, versatile, and accurate ML-based toolkits for nanophotonic design with tailored optical responses.