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Briscoe, Jayson

Publications and source records attributed to Briscoe, Jayson.

15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Terahertz Detection Using Enhanced Two‐Step Absorption in Photoconductive Metasurfaces Gated at λ = 1.55 µm

Abstract Superior ultrafast photoconductive properties make low temperature grown (LT) GaAs one of the best materials for photoconductive terahertz (THz) detection. However, the large bandgap of LT‐GaAs limits its operation to gating at wavelengths shorter than 870 nm. Here, it is demonstrated for the first time that nanostructuring the LT‐GaAs into a highly absorbing metasurface enables THz photoconductive detection with a pulsed laser at λ = 1.55 µm. The very weak sub‐bandgap absorption mediated by midgap states in LT‐GaAs is strongly enhanced using the concept of perfect absorption via degenerate critical coupling. Integrated with a THz antenna, the LT‐GaAs metasurface enables high sensitivity THz detection with a high dynamic range of 60 dB and large bandwidth up to 4.5 THz. The LT‐GaAs metasurface has the potential to serve as a universal ultrafast switching element for THz applications, enabling low‐cost, turn‐key THz systems for a variety of real‐world applications.

Jung, Hyunseung↗
DOE OSTI2022

Classification of Intensity Distributions of Transmission Eigenchannels of Disordered Nanophotonic Structures Using Machine Learning

Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Micro-lensed fiber optic plate and methods of forming thereof

A method of forming a micro-lensed fiber optic plate. The method includes obtaining a fiber optic plate that has a plurality of fibers arranged in parallel. A first end of a fiber of the plurality of fibers can have a first numerical aperture. The fiber of the plurality of fibers includes a core material running along a length of the fiber that is circumscribed by a layer of cladding material. The method also includes creating a lensed surface at a second end of the fiber of the plurality of fibers. The second end is opposite the first end. The lensed surface of the fiber of the plurality of fibers can have a second numerical aperture that is different from the first numerical aperture.

Bagwell, Brett↗
DOE OSTI2021

Facile microwave synthesis of zirconium metal-organic framework thin films on gold and silicon and application to sensor functionalization

Zirconium-based metal-organic frameworks, including UiO-66 and related frameworks, have become the focus of considerable research in the area of chemical warfare agent (CWA) decontamination. However, little work has been reported exploring these metal-organic frameworks (MOFs) for CWA sensing applications. For many sensing approaches, the growth of high-quality thin films of the active material is required, and thin film growth methods must be compatible with complex device architectures. Several approaches to synthesize thin films of UiO-66 have been described but many of these existing methods are complex or time consuming. In this work, we describe the development of a simple and rapid microwave assisted synthesis of oriented UiO-66 thin films on unmodified silicon (Si) and gold (Au) substrates. Thin films of UiO-66 and UiO-66-NH 2 can be grown in as little as 2 min on gold substrates and 30 min on Si substrates. The film morphology and orientation are characterized and the effects of reaction time and temperature on thin film growth on Au are investigated. Both reaction time and temperature impact the overgrowth of protruding discrete crystallites in the thin film layer but, surprisingly, no strong correlation is observed between film thickness and reaction time or temperature. We also briefly describe the synthesis of Zr/Ce solid solution thin films of UiO-66 on Au and report the first synthesis of a solid solution thin film MOF. Finally, we demonstrate the utility of the microwave method for the facile functionalization of two sensor architectures, plasmonic nanohole arrays and microresonators, with UiO-66 thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Optimization and Prediction of Spectral Response of Metasurfaces Using Artificial Intelligence

Hot-electron generation has been a topic of intense research for decades for numerous applications ranging from photodetection and photochemistry to biosensing. Recently, the technique of hot-electron generation using non-radiative decay of surface plasmons excited by metallic nanoantennas, or meta-atoms, in a metasurface has attracted attention. These metasurfaces can be designed with thicknesses on the order of the hot-electron diffusion length. The plasmonic resonances of these ultrathin metasurfaces can be tailored by changing the shape and size of the meta-atoms. One of the fundamental mechanisms leading to generation of hot-electrons in such systems is optical absorption, therefore, optimization of absorption is a key step in enhancing the performance of any metasurface based hot-electron device. Here we utilized an artificial intelligence-based approach, the genetic algorithm, to optimize absorption spectra of plasmonic metasurfaces. Using genetic algorithm optimization strategies, we designed a polarization insensitive plasmonic metasurface with 90% absorption at 1550 nm that does not require an optically thick ground plane. We fabricated and optically characterized the metasurface and our experimental results agree with simulations. Finally, we present a convolutional neural network that can predict the absorption spectra of metasurfaces never seen by the network, thereby eliminating the need for computationally expensive simulations. Our results suggest a new direction for optimizing hot-electron based photodetectors and sensors.

77 NANOSCIENCE AND NANOTECHNOLOGY↗