6,408 research outputs found

    Optical Spectroscopy of 3d and 4d correlated electron systems.

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    In the context of this work, three different materials are studied via optical spec- troscopy methods. The three materials are La2Cu2O5, Fe3O4, and Ca2RuO4, where the first one is investigated via Fourier spectroscopy, while the latter two are stud- ied via spectroscopic ellipsometry

    Neutron scattering studies of heterogeneous catalysis

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    Understanding the structural dynamics/evolution of catalysts and the related surface chemistry is essential for establishing structure–catalysis relationships, where spectroscopic and scattering tools play a crucial role. Among many such tools, neutron scattering, though less-known, has a unique power for investigating catalytic phenomena. Since neutrons interact with the nuclei of matter, the neutron–nucleon interaction provides unique information on light elements (mainly hydrogen), neighboring elements, and isotopes, which are complementary to X-ray and photon-based techniques. Neutron vibrational spectroscopy has been the most utilized neutron scattering approach for heterogeneous catalysis research by providing chemical information on surface/bulk species (mostly H-containing) and reaction chemistry. Neutron diffraction and quasielastic neutron scattering can also supply important information on catalyst structures and dynamics of surface species. Other neutron approaches, such as small angle neutron scattering and neutron imaging, have been much less used but still give distinctive catalytic information. This review provides a comprehensive overview of recent advances in neutron scattering investigations of heterogeneous catalysis, focusing on surface adsorbates, reaction mechanisms, and catalyst structural changes revealed by neutron spectroscopy, diffraction, quasielastic neutron scattering, and other neutron techniques. Perspectives are also provided on the challenges and future opportunities in neutron scattering studies of heterogeneous catalysis

    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

    Novel 129Xe Magnetic Resonance Imaging and Spectroscopy Measurements of Pulmonary Gas-Exchange

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    Gas-exchange is the primary function of the lungs and involves removing carbon dioxide from the body and exchanging it within the alveoli for inhaled oxygen. Several different pulmonary, cardiac and cardiovascular abnormalities have negative effects on pulmonary gas-exchange. Unfortunately, clinical tests do not always pinpoint the problem; sensitive and specific measurements are needed to probe the individual components participating in gas-exchange for a better understanding of pathophysiology, disease progression and response to therapy. In vivo Xenon-129 gas-exchange magnetic resonance imaging (129Xe gas-exchange MRI) has the potential to overcome these challenges. When participants inhale hyperpolarized 129Xe gas, it has different MR spectral properties as a gas, as it diffuses through the alveolar membrane and as it binds to red-blood-cells. 129Xe MR spectroscopy and imaging provides a way to tease out the different anatomic components of gas-exchange simultaneously and provides spatial information about where abnormalities may occur. In this thesis, I developed and applied 129Xe MR spectroscopy and imaging to measure gas-exchange in the lungs alongside other clinical and imaging measurements. I measured 129Xe gas-exchange in asymptomatic congenital heart disease and in prospective, controlled studies of long-COVID. I also developed mathematical tools to model 129Xe MR signals during acquisition and reconstruction. The insights gained from my work underscore the potential for 129Xe gas-exchange MRI biomarkers towards a better understanding of cardiopulmonary disease. My work also provides a way to generate a deeper imaging and physiologic understanding of gas-exchange in vivo in healthy participants and patients with chronic lung and heart disease

    Ultrafast Optical Control of Order Parameters in Quantum Materials

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    Developing protocols to realize quantum phases that are not accessible thermally and to manipulate material properties on demand is one of the central problems of modern condensed matter physics. Impulsive electromagnetic stimulus provides an extensive playground not only to exert desired control over the material macroscopic properties but also to optically detect the underlying microscopic mechanisms. Two indispensable components form the cornerstone to realize these goals: a meticulous comprehension of light-induced phenomena and a suitable and versatile platform. Abundant photoinduced phenomena emerge upon light irradiation. A collective oscillation of order parameter can be launched and probed in the weak perturbation regime; further increasing light intensity can transiently modulate the free-energy landscape, inducing a suppression, enhancement, reversal, and switch of order parameters; in the strong non-perturbative excitation regime, the system can be driven nonlinearly with microscopic coupling parameters modified. Understanding these light driven emergent phenomena lays the foundation of optical control and novel functionalities. Quantum materials, embodying a large portfolio of topological and strongly correlated compounds, afford an exceptional venue to realize optical control. Owing to the complex interplay between the charge, spin, orbital, and lattice degrees of freedom, a rich phase diagram can be generated with various phases that are selectively and independently accessible via optical perturbations. They hence offer a wealth of opportunities to not only improve our comprehension of the underlying physics but also develop the next generation of ultrafast technologies. In Chapter I of this thesis, I will first cover a multitude of light-induced emergent phenomena in quantum materials under the framework of time-dependent Landau theory, Keldysh theory, and Floquet theory, and then introduce several canonical microscopic models to quantitatively rationalize the intra- and interactions between different degrees of freedom in quantum materials. As the necessary theoretical background is established, three main experimental techniques that have been extensively utilized in my research: time-resolved reflectivity and Kerr effect, time-resolved second harmonic generation rotational anisotropy, and coherent phonon spectroscopy will be introduced in Chapter II. In Chapter III, I will demonstrate that a light-induced topological phase transition can be engendered concomitant with an inverse-Peierls structural phase transition in elemental Te. In Chapter IV, I will describe signatures of ultrafast reversal of excitonic order in excitonic insulator candidate Ta2NiSe5 and substantiate a manipulation of the reversal as well as the Higgs mode with tailored light pulses. In Chapter V, a light-induced switch of spin-orbit-coupled quadrupolar order in multiband Mott insulator Ca2RuO4 will be introduced. In Chapter VI, a Keldysh tuning of nonlinear carrier excitation and Floquet bandwidth renormalization in strongly driven Ca2RuO4 will be covered.</p

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

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    Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state of the art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, pre-processing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community
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