Cooperative interactions in dense thermal Rb vapour confined in nm-scale cells

This thesis presents an investigation of the fundamental interaction between light and matter, realised with a rubidium vapour confined in a cell whose thickness (in the propagation direction) is less than the optical wavelength. This confinement allows observation of spectroscopic features not found in longer cells, such as Dicke narrowing. These effects are measured experimentally and a theoretical model is developed, which allows the characterisation of the medium in terms of the atomic electric susceptibility. Interactions between the atoms and their surroundings, whether this be the walls of the cell or other nearby atoms, are explored. In the frequency domain we ob- serve broadening and shifts of the spectral features due to these interactions. The atom-surface interaction shifts the spectral lines, following the expected 1/r^3 van-der-Waals behaviour. The interatomic dipole-dipole interactions are more complex, and we find cooperative effects play an important role. We present an experimental verification of the full spatial dependence of the cooperative Lamb shift, which follows the theoretical prediction made 40 years ago, an important demonstration of coherent interactions in a thermal ensemble. The interactions also play a role in determining the refractive index of the medium, limiting the maximum near-resonant index to n = 1.31. Using heterodyne interferometry, we experimentally measure an index of n = 1.26± 0.02. This index enhancement leads to large bandwidth regions where a significant slow- or fast-light effect is present. We verify the fast-light effect in the time domain by observing the superluminal propagation of a sub- nanosecond optical pulse, and measure the group index of the medium to be ng = −1.0 ± 0.1 × 10^5, the largest negative group index measured to date. We investigate the radiative decay rate using time-domain fluorescence, and we observe radiation trapping effects in a millimetre-thickness vapour. Fi- nally, we present results on sub-nanosecond coherent dynamics in the system which are achieved by pumping the medium with a strong optical pulse

Mining for cosmological information: Simulation-based methods for Redshift Space Distortions and Galaxy Clustering

The standard model of cosmology describes the complex large scale structure of the Universe through less than 10 free parameters. However, concordance with observations requires that about 95\% of the energy content of the universe is invisible to us. Most of this energy is postulated to be in the form of a cosmological constant, $\Lambda$, which drives the observed accelerated expansion of the Universe. Its nature is, however, unknown. This mystery forces cosmologists to look for inconsistencies between theory and data, searching for clues. But finding statistically significant contradictions requires extremely accurate measurements of the composition of the Universe, which are at present limited by our inability to extract all the information contained in the data, rather than being limited by the data itself. In this Thesis, we study how we can overcome these limitations by i) modelling how galaxies cluster on small scales with simulation-based methods, where perturbation theory fails to provide accurate predictions, and ii) developing summary statistics of the density field that are capable of extracting more information than the commonly used two-point functions. In the first half, we show how the real to redshift space mapping can be modelled accurately by going beyond the Gaussian approximation for the pairwise velocity distribution. We then show that simulation-based models can accurately predict the full shape of galaxy clustering in real space, increasing the constraining power on some of the cosmological parameters by a factor of 2 compared to perturbation theory methods. In the second half, we measure the information content of density dependent clustering. We show that it can improve the constraints on all cosmological parameters by factors between 3 and 8 over the two-point function. In particular, exploiting the environment dependence can constrain the mass of neutrinos by a factor of 8$ better than the two-point correlation function alone. We hope that the techniques described in this thesis will contribute to extracting all the cosmological information contained in ongoing and upcoming galaxy surveys, and provide insight into the nature of the accelerated expansion of the universe

Materials and Processes for Photocatalytic and (Photo)Electrocatalytic Removal of Bio-Refractory Pollutants and Emerging Contaminants from Waters

Water pollution from biorefractory pollutants and emerging contaminants is still a very relevant problem worldwide. Examples of these pollutants include disinfection by-products, pharmaceutical and personal care products, persistent organic chemicals, as well as their degradation products. The occurrence of these contaminants in water has raised increasing concern due to their accumulation and persistence in the environment and the threat they pose to the ecological system and human health. In this Special Issue, papers regarding the advancements in materials and processes for use in the electro- and photoelectrochemical removal of different pollutants from water are collected. The synthesis, characterization and application of materials for use in electrochemical or photoelectrochemical techniques are presented, as well as studies concerning catalytic processes and reaction kinetics

Robust Estimation Techniques for the Cosmological Analysis of Large Scale Structure

The ΛCDM model of cosmology together with Inflation has had tremendous success over the past 30 years in explaining the increasingly rich data sets of the cosmic microwave background and large-scale structure. The next generation of large-scale structure surveys is expected to answer many open questions about the microscopic description of the Universe. In order to fully leverage those data sets, one needs exquisite theoretical predictions. Here, the main difficulty is the non-linear nature of the large-scale structure observables which, together with the exquisitely small statistical errors, cause real concern of false discoveries. In this thesis, we study two estimators that allow us to extract non-linear information from the large-scale structure while being robust against one of the leading sources of systematic uncertainties: Redshift-space distortions. By means of a bias relation, we extend the matter counts-in-cells statistic for the first time to neutral hydrogen. Neutral hydrogen is particularly interesting for counts-in-cells statistics because of the vast regions that can be covered by intensity mapping. We find percent-level accuracy when comparing the prediction for the density in spheres probability density function to the IllustrisTNG simulation. The measured density dependent clustering signal, which could be used to break the bias-amplitude degeneracies, matches theoretical expectations. Our bias model is able to capture the effect of redshift-space distortions making the estimator robust. Based on a separation idea, we present an efficient code to compute projected bispectra. The separation approach is orders of magnitude more efficient than the direct integration. This allows us to investigate the relation between biases in the estimated parameters and inaccurate modelling of non-linear redshift-space distortions for the power spectrum and bispectrum of projected galaxy density fields and lensing convergence. For a toy galaxy survey that resembles the CMASS sample of the baryon oscillation spectroscopic survey, we find that modelling non-linear redshift-space distortion only becomes necessary for galaxy bins thinner than 150 Mpc/h. In case a better radial resolution is available, errors on cosmological parameters can be improved by 20% when including an accurate non-linear RSD model that allows us to use bins of depth ∼60 Mpc/h. The separation of projection integrals proves also useful for theoretical uncertainties. We use the separability of Gaussian correlation functions to develop a consistent model for theoretical uncertainties of the projected power spectrum

PROGNOSTIC MODELING FOR RELIABILITY PREDICTIONS OF POWER ELECTRONIC DEVICES

The applications of semiconductor power electronic devices, including power and RF devices, in industry have stringent requirements on their reliability. Power devices are subject to various types of failure mechanisms under various stressors. Prognostics and health management (PHM) allows detecting signs of failures, providing warnings of failures in advance, and performing condition-based maintenance. There is a pressing need to develop a robust prognostic model to detect anomalous behavior and predict the lifetime of devices that can be applicable to different types of power transistors. In the present dissertation, a comprehensive prognostic model for remaining useful life (RUL) prediction of semiconductor power electronic devices is developed. The model consists of an anomaly detection module and a RUL prediction module including a non-linear system process model describing the evolution of parametric degradation, and a measurement model. The anomaly detection module uses principal component analysis (PCA) for dimensionality reduction and feature extraction, as well as k-means clustering to establish baseline clusters in the feature space. The novel singular-value-weighted distance (SVWD) is developed as the distance measure in the feature space, based on which Fisher criterion (FC) is used for anomaly probability calculation. The system process model incorporates variables concerning loading conditions and physics-of-failure of devices, and uses particle filter (PF) approach for process model training and RUL prediction. For PF methodology, a novel resampling technique, called MHA-replacement resampling, is developed to solve the particle degeneracy in classic PF techniques and sample impoverishment in traditional resampling techniques. The developed prognostic model is first implemented on IGBT modules for validation. It was reported that the module package of power transistors was susceptible to various types of fatigue-related failure modes due to coefficient of thermal expansion (CTE) mismatches under temperature/power cycles introducing thermomechanical stresses. The physics-of-failure "driving variable" is derived from Paris equation. The model is validated on several time-series IGBT module degradation data under power cycles from literature sources, based on SIR particle filter for RUL prediction with good accuracy. Then the model is implemented on GaN HEMTs, a representative of wide-bandgap semiconductor power devices. GaN HEMTs are susceptible to degradation mechanisms such as ohmic contact inter-diffusion that leads to voiding in the field plate at high temperature under RF accelerated life tests (ALTs). The time-series data of the physics-of-failure "driving variable" is obtained from diffusion computation in Mathematica with the temperature prole coming from COMSOL thermal simulation. The RUL prediction results based on SIR lter are also satisfactory for GaN HEMTs. For each type of device, the new resampling technique is validated through performance benchmarking against state-of-the-art resampling techniques. Another reliability threat for GaN HEMTs, especially in aerospace and nuclear applications, is the degradation due to radiation effect on the device performance. Gamma radiation has been found to lead to generation of defects in AlGaN/GaN layers, which form complexes acting as carrier traps, thus reducing carrier density and current. EPC GaN HEMTs are irradiated under a wide range of Gamma ray doses and critical DC characteristics are recorded before and after radiation to quantify their shifts during the irradiation. Future work needed to allow implementation of the developed prognostic model for RUL estimation is proposed

Brownian motors: noisy transport far from equilibrium

Transport phenomena in spatially periodic systems far from thermal equilibrium are considered. The main emphasize is put on directed transport in so-called Brownian motors (ratchets), i.e. a dissipative dynamics in the presence of thermal noise and some prototypical perturbation that drives the system out of equilibrium without introducing a priori an obvious bias into one or the other direction of motion. Symmetry conditions for the appearance (or not) of directed current, its inversion upon variation of certain parameters, and quantitative theoretical predictions for specific models are reviewed as well as a wide variety of experimental realizations and biological applications, especially the modeling of molecular motors. Extensions include quantum mechanical and collective effects, Hamiltonian ratchets, the influence of spatial disorder, and diffusive transport.Comment: Revised version (Aug. 2001), accepted for publication in Physics Report

Applications of Geodesy to Geodynamics, an International Symposium

Geodetic techniques in detecting and monitoring geodynamic phenomena are reviewed. Specific areas covered include: rotation of the earth and polar motion; tectonic plate movements and crustal deformations (space techniques); horizontal crustal movements (terrestrial techniques); vertical crustal movements (terrestrial techniques); gravity field, geoid, and ocean surface by space techniques; surface gravity and new techniques for the geophysical interpretation of gravity and geoid undulation; and earth tides and geodesy

Studies of phase separable soluble polymers

The technique of phase labeling has the ability to greatly enhance synthetic protocol by simplifying purification and increasing efficiency. Traditional insoluble supports offer efficient and simple recovery of the Âphase tagged material but suffer from problems inherent to their heterogeneous nature. A solution to these problems has been to utilize phase separable soluble polymers in the design of Âsmart responsive systems that offer the option of homogenous reaction conditions with heterogeneous separation conditions. The subject of this dissertation focuses on the application of soluble polymeric phase tags in systems where the miscibility between solid-liquid and liquid-liquid systems is thermally induced. Low molecular weight poly(ethylene glycol) (PEG) oligomers were investigated as phase anchors for SCS palladacycle catalysts. The oligomeric PEG chains were sufficient to engender polar phase solubility in a heptane-DMA thermomorphic system. Microwave irradiation of these thermomorphic mixtures of palladium complexes and substrates was a viable scheme to recycle and significantly shorten reaction times for simple Heck reactions of aryl iodides. Soluble polymeric supports possessing a lower critical solution temperature (LCST) were utilized in the sequestration of the S-triazine herbicide, atrazine, from contaminated water samples. The ability of poly(N-isopropylacrylamide) to sequester hydrophobic guests like atrazine was examined. A functionalized PNIPAM derivative containing secondary cyclic amines exhibited superior sequestration ability that was credited to the covalent binding of the atrazine. In order to facilitate the design of tailored, thermally responsive, smart polymers, a high throughput temperature gradient microfluidic device was used to obtain LCST data in a fast, accurate manner. The specific ion effects of various alkali metal halide salts on the LCST of PNIPAM were investigated. The high precision in the measurements enabled more subtle effects such as changes in solvent isotope, polymer microstructure, molecular weight, and importance of end group effects on the LCST of poly(N-alkylacrylamide)s to be evaluated

Bayesian Variational Regularisation for Dark Matter Reconstruction with Uncertainty Quantification

Despite the great wealth of cosmological knowledge accumulated since the early 20th century, the nature of dark-matter, which accounts for ~85% of the matter content of the universe, remains illusive. Unfortunately, though dark-matter is scientifically interesting, with implications for our fundamental understanding of the Universe, it cannot be directly observed. Instead, dark-matter may be inferred from e.g. the optical distortion (lensing) of distant galaxies which, at linear order, manifests as a perturbation to the apparent magnitude (convergence) and ellipticity (shearing). Ensemble observations of the shear are collected and leveraged to construct estimates of the convergence, which can directly be related to the universal dark-matter distribution. Imminent stage IV surveys are forecast to accrue an unprecedented quantity of cosmological information; a discriminative partition of which is accessible through the convergence, and is disproportionately concentrated at high angular resolutions, where the echoes of cosmological evolution under gravity are most apparent. Capitalising on advances in probability concentration theory, this thesis merges the paradigms of Bayesian inference and optimisation to develop hybrid convergence inference techniques which are scalable, statistically principled, and operate over the Euclidean plane, celestial sphere, and 3-dimensional ball. Such techniques can quantify the plausibility of inferences at one-millionth the computational overhead of competing sampling methods. These Bayesian techniques are applied to the hotly debated Abell-520 merging cluster, concluding that observational catalogues contain insufficient information to determine the existence of dark-matter self-interactions. Further, these techniques were applied to all public lensing catalogues, recovering the then largest global dark-matter mass-map. The primary methodological contributions of this thesis depend only on posterior log-concavity, paving the way towards a, potentially revolutionary, complete hybridisation with artificial intelligence techniques. These next-generation techniques are the first to operate over the full 3-dimensional ball, laying the foundations for statistically principled universal dark-matter cartography, and the cosmological insights such advances may provide