376 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Molecular sensors for evaluating substandard anti-retroviral medication using surface-enhanced raman spectroscopy.

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    Doctoral Degree. University of KwaZulu-Natal, Durban.Africa has the highest number of people living with HIV and AIDS, with South Africa housing the largest Anti-retroviral treatment (ART) program in the world. In addition, the continent is troubled by the continuing growth of substandard ART medication which is imported from external continents. The World Health Organization also states that due to the limited information on this issue, adequate remedial measures cannot be put into place. As such, this study proposed the application of surface-enhanced Raman spectroscopy (SERS) as a drug screening method for ART. Sensing platforms were synthesized using a combination of metals, crosslinker organic molecules, deposition, and self-assembly methods. The platforms were used for tailored adsorption of three ART medications in their active pharmaceutical ingredient (API) form: Tenofovir (TDF), Lamivudine (LAM) and Dolutegravir (DLG) prior to evaluation with Raman spectroscopy. Molecular interactions, signal enhancement and statistical methods such as linear regression were carried out on the analytes and data from the SERS analysis showed significant differences in the sensing capabilities of the platforms based on the calibration sensitivity, analytical sensitivity, and limit of detection. The molecular composition and chemical functionality of the sensors allowed specific adsorption and preference to the complementary functional groups of the API samples which led to enhanced Raman signals on each platform. From the results obtained, it was concluded that the synthesis of tailored platforms for molecular sensing of ART medication was successful, providing potential application of these sensors in the quality control of anti-retroviral medication. Future work will entail routine molecular screening of ARVs to monitor changes in ART quality with respect to geographical location, shelf life and formulation methods

    Beyond classical electrodynamics: mesoscale electron dynamics and nonlinear effects in hybrid nanostructured systems

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    This work investigates the optial properties of hybrid metal-dielectric and ionic-solid largely regular nanostructures in the presence of nanosized features such as gaps and thin walls in tubular structures. The fundamental optical response of plasmonic, ionic and dielectric systems is considered from a classical electromagnetic perspective, including properties of amorphous materials, rough interfaces, nonlinear and semi-classical charge interactions. We focus hereby on two aspects: (i) nonclassical effects stemming from the quantum nature of freely moving charges and (ii) nonlinear optical response. The overall aim is to realistically describe complex nanoparticle distributions and ultrathin multilayers with reliable and rapid methods of computational nanophotonics while extending its scope towards multiphysics aspects beyond classical electrodynamics. The analytical and numerical models developed over the past years are presented in this work in detail with standard, but necessary technical details available in the appendices. We often assume a multilayered system where one layer is a nanostructure with either one- or two-dimensional symmetry, i.e., a grating or laminar structure in the first and an array of nanoparticles (disks, holes, pillars, etc.) in the latter case. Given the symmetries and overall composition of the structure, our method of choice is the Fourier Modal Method (FMM) together with the scattering matrix approach to connect the different layers. The standard FMM formulation is extended to include spatial dispersion effects of conduction band electrons in metals introducing not only an additional boundary condition, but an overall third longitudinal solution to the standard transversal solutions of the electromagnetic wave equation. Furthermore, we explore the impact of higher harmonic waves, in particular second and third harmonic generation, from the local fields around the nanostructures studied

    1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

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    A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

    Machine Learning and Its Application to Reacting Flows

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    This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

    Distributed Memory, GPU Accelerated Fock Construction for Hybrid, Gaussian Basis Density Functional Theory

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    With the growing reliance of modern supercomputers on accelerator-based architectures such a GPUs, the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development of GPU accelerated, distributed memory algorithms for many-body (e.g. coupled-cluster) and spectral single-body (e.g. planewave, real-space and finite-element density functional theory [DFT]), the vast majority of GPU-accelerated Gaussian atomic orbital methods have focused on shared memory systems with only a handful of examples pursuing massive parallelism on distributed memory GPU architectures. In the present work, we present a set of distributed memory algorithms for the evaluation of the Coulomb and exact-exchange matrices for hybrid Kohn-Sham DFT with Gaussian basis sets via direct density-fitted (DF-J-Engine) and seminumerical (sn-K) methods, respectively. The absolute performance and strong scalability of the developed methods are demonstrated on systems ranging from a few hundred to over one thousand atoms using up to 128 NVIDIA A100 GPUs on the Perlmutter supercomputer.Comment: 45 pages, 9 figure
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