2,237 research outputs found

    A Broadband Mid-infrared Metasurface for Polarisation Manipulation and Utilisation

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    A pair of enantiomers are distinct from each other due to chiral structural arrangement which leads to selective interaction with chiral light. Vibrational circular dichroism spectroscopy in mid-infrared region provide a powerful label-free method to distinguish chiral enantiomers. Besides, mid-infrared sensing also significantly benefit from resolving compositional information of molecules due to molecular vibrational fingerprints which holds promising application in biological and medical sensing. However, the low signal-to-noise ratio associated with weak light-matter interaction is a continuing obstacle hindering the practical application. Recent demonstrations of chiral metamaterials have shown that due to the chirality of structure, local superchiral field can be produced in the vicinity of structure to interact with molecules and enhance vibrational circular dichroism response. However, a limitation factor in development of chiral structure is the narrow effective working bandwidth and the requirement of circular polarization excitation. This thesis introduces an achiral nanorod-based metausrface that enable to overcome these limitations. First, the nanorod-based metasurface is present to achieve high efficient linear-to-circular polarization conversion in a broadband mid-infrared wavelength range in reflection mode. The model was firstly studied and optimised through simulation tool based on Finite Difference Time Domain method. The device was fabricated in a top-down approach based on electron beam lithography and characterised using Fourier transform infrared spectroscopy. We identified two distinct resonances originated from gap-plasmon mode at 3.4μm and Fabry-Perot mode at 7.9μm. The demonstration of polarization state based on the measured Stokes parameters within off-resonance range from 4-7μm show that the reflected beam has converted into circular polarization state. For practical application of vibrational circular dichroism spectroscopy, we numerically demonstrate the induced chirality in near-field under excitation of linear polarization with various polarization angles. These analysis suggest that superchiral field can be produced by nanorod-based metasurface and distributed spatially under linear polarization excitation. When polarization is parallel or orthogonal to rod, namely the symmetry exist in the combination of rod and incident polarization, the absolute chirality is zero due to the fact that same amount of optical chirality density with opposite handedness offset by each other. However it is showed that one handedness of the optical chirality density is dominant when the symmetry is broken, hence, holds potential for circular dichroism spectroscopy sensing. In an experimental feasibility study, we measured the polarization states of light in far-field and demonstrate that the absolute chirality in the far-field show similar behaviour as that in near-field. Finally, we conduct a molecular sensing measurement based on the rod-shape metausrface for enantiomers (alanine) identification through circular dichroism spectroscopy. This thesis demonstrates with FDTD simulations that the metasurface can generate superchiral fields which enable to enhance interaction with molecules upon linear polarization excitation. By simply rotating sample with 90 degree, molecules can then interact with superchiral field with opposite handedness. The circular dichroism is to record the intensity of reflected beam and characterise the differential intensity between the two. Despite the measured data do not show inverse pattern for L- and D-alanine, we confirmed that metausrface enable to enhance the light-matter interaction

    Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students

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    In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies. To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on). The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts. This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution. The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials. The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world

    Asymptotics of stochastic learning in structured networks

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    Leveraging elasticity theory to calculate cell forces: From analytical insights to machine learning

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    Living cells possess capabilities to detect and respond to mechanical features of their surroundings. In traction force microscopy, the traction of cells on an elastic substrate is made visible by observing substrate deformation as measured by the movement of embedded marker beads. Describing the substrates by means of elasticity theory, we can calculate the adhesive forces, improving our understanding of cellular function and behavior. In this dissertation, I combine analytical solutions with numerical methods and machine learning techniques to improve traction prediction in a range of experimental applications. I describe how to include the normal traction component in regularization-based Fourier approaches, which I apply to experimental data. I compare the dominant strategies for traction reconstruction, the direct method and inverse, regularization-based approaches and find, that the latter are more precise while the former is more stress resilient to noise. I find that a point-force based reconstruction can be used to study the force balance evolution in response to microneedle pulling showing a transition from a dipolar into a monopolar force arrangement. Finally, I show how a conditional invertible neural network not only reconstructs adhesive areas more localized, but also reveals spatial correlations and variations in reliability of traction reconstructions

    Multiscale modeling of synthetic and biological supramolecular systems.

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    In this thesis, we exploited the synergistic combination of multiscale molecular modeling, molecular dynamics (MD), and enhanced sampling to tackle two complex systems. In the first case study, we investigated the intrinsic dynamic behavior of a Benzene 1,3,5-TricarboxAmide (BTA) supramolecular polymer in water. In the second case study, we inquired about the effect of functionalized amphiphilic gold nanoparticles (Au NPs) on the phase behavior of a multi-component lipid membrane. Through our simulations, we gained a deeper understanding of the structure and dynamics of a class of supramolecular polymers. Additionally, we identified the factors that control the exchange of monomers between the different fibers, which can be used to inform the design of novel supramolecular materials in the future. Our simulations provided insights into the mechanisms underlying the interaction between functionalized nanoparticles and lipid membranes, extrapolating the factors that influence the stability of the membrane phase separation. The acquired knowledge can be applied in drug delivery systems or to create new hybrid materials containing ordered two-dimensional NP lattices. In particular, it is worth noting that in both studies, using coarse-grained models with the proper (sub-molecular) resolution was crucial to overcoming the limitations of classic all-atom force fields while maintaining the needed chemical specificity. Overall, the results of these studies have broad implications for materials science and biophysics and demonstrate the potential of computational modeling to inform the design of novel materials and systems

    Markov field models of molecular kinetics

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    Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

    Asymptotics of stochastic learning in structured networks

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    University of Windsor Graduate Calendar 2023 Spring

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    https://scholar.uwindsor.ca/universitywindsorgraduatecalendars/1027/thumbnail.jp

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains
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