Wireless Chipless Liquid Sensing using a Slotted Cylindrical Resonator

This thesis presents a comprehensive study on the application of a slotted cylindrical resonator for the wireless assessment of liquids. Using simple geometry and measurement techniques, a method for the sensing of liquids within non-metal pipes is established, allowing for the prospect of non-contact, real-time, wireless monitoring of industrial liquid processes with no requirement for samples. The main contribution of this work is the development of a thorough understanding of the geometry, as well as an extensive presentation of measured data using liquids of wide-ranging properties. A full parametric and sensitivity study obtained through theory, simulation and measurement provides analysis on every aspect of the proposed sensor, including a number of potential future research topics. The slotted cylinder is placed directly on-pipe, requiring no additional circuitry, power or support structure, and is excited wirelessly by an external antenna. Its resonant frequency is very sensitive to the permittivity within the sensor cavity, and is shown to operate well across a relatively large range of permittivity. The structure is highly adaptable, even for fixed pipe dimensions, and simple adjustments provide a method for the tuning of resonant frequency and sensitivity control. Additionally, the placement of multiple sensors in close proximity allows for the measurement of high-loss liquids, which may otherwise not be possible. A number of measurement techniques for level sensing are presented, covering both frequency and amplitude detection methods using single and multiple sensors, where the geometry is shown to be highly sensitive to very small changes in liquid level. Measurements detecting relatively small changes in liquid temperature provide a further potential application of the sensor. The simultaneous monitoring of multiple liquids is easily achieved using a single measurement system, vastly reducing the complexity inherent in large-scale industrial processes. The sensor is shown to be resilient to changes in polarisation and position relative to the measurement antenna, as well larger read distances compared with other passive sensors

A New Spin on the Weak Gravity Conjecture

The mild form of the Weak Gravity Conjecture states that quantum or higher-derivative corrections should decrease the mass of large extremal charged black holes at fixed charge. This allows extremal black holes to decay, unless protected by a symmetry (such as supersymmetry). We reformulate this conjecture as an integrated condition on the effective stress tensor capturing the effect of quantum or higher-derivative corrections. In addition to charged black holes, we also consider rotating BTZ black holes and show that this condition is satisfied as a consequence of the $c$-theorem, proving a spinning version of the Weak Gravity Conjecture. We also apply our results to a five-dimensional boosted black string with higher-derivative corrections. The boosted black string has a $\text{BTZ}\times S^2$ near-horizon geometry and, after Kaluza-Klein reduction, describes a four-dimensional charged black hole. Combining the spinning and charged Weak Gravity Conjecture we obtain positivity bounds on the five-dimensional Wilson coefficients that are stronger than those obtained from charged black holes alone.Comment: 29 pages + appendices, 4 figure

Chipless Liquid Sensing Using a Slotted Cylindrical Resonator

A method for the wireless sensing of the permittivity and level of liquids is presented. The use of a simple, thin-film slotted cylindrical cavity wrapped around a standard polytetrafluoroethylene pipe is proposed. Wireless interrogation of the slot excites a resonant mode whose frequency is dependent on the liquid currently present within the pipe. The proposed method allows for measurements to be taken in situ with no requirement for taking samples of potentially hazardous liquids. The device is capable of sensing materials of high relative permittivity, including water, as well as very lossy liquids. A comprehensive set of results is presented, including measurements of butanol, ethanol, methanol and water, for several device configurations. The proposed sensor is also shown to be sensitive to small changes in liquid level, allowing for accurate water level measurements down to 0:1 ml. This sensor is a good candidate for very low-cost, low-complexity real-time monitoring of liquids

Toward ab initio optical spectroscopy of the Fenna-Matthews-Olson complex

We present progress toward a first-principles parametrization of the Hamiltonian of the Fenna–Matthews–Olson pigment–protein complex, a molecule that has become key to understanding the role of quantum dynamics in photosynthetic exciton energy transfer. To this end, we have performed fully quantum mechanical calculations on each of the seven bacteriochlorophyll pigments that make up the complex, including a significant proportion of their protein environment (more than 2000 atoms), using linear-scaling density functional theory exploiting a recent development for the computation of excited states. Local pigment transition energies and interpigment coupling between optical transitions have been calculated and are in good agreement with the literature consensus. Comparisons between simulated and experimental optical spectra point toward future work that may help to elucidate important design principles in these nanoscale devices

Reverse engineering of force integration during mitosis in the Drosophila embryo

The mitotic spindle is a complex macromolecular machine that coordinates accurate chromosome segregation. The spindle accomplishes its function using forces generated by microtubules (MTs) and multiple molecular motors, but how these forces are integrated remains unclear, since the temporal activation profiles and the mechanical characteristics of the relevant motors are largely unknown. Here, we developed a computational search algorithm that uses experimental measurements to ‘reverse engineer' molecular mechanical machines. Our algorithm uses measurements of length time series for wild-type and experimentally perturbed spindles to identify mechanistic models for coordination of the mitotic force generators in Drosophila embryo spindles. The search eliminated thousands of possible models and identified six distinct strategies for MT–motor integration that agree with available data. Many features of these six predicted strategies are conserved, including a persistent kinesin-5-driven sliding filament mechanism combined with the anaphase B-specific inhibition of a kinesin-13 MT depolymerase on spindle poles. Such conserved features allow predictions of force–velocity characteristics and activation–deactivation profiles of key mitotic motors. Identified differences among the six predicted strategies regarding the mechanisms of prometaphase and anaphase spindle elongation suggest future experiments

Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation

Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe which host invaluable information about the cosmology and astrophysics of X-ray heating and hydrogen reionization. Radio interferometric observations of the 21-cm line at high redshifts have the potential to revolutionize our understanding of the universe during this time. However, modeling the evolution of these epochs is particularly challenging due to the complex interplay of many physical processes. This makes it difficult to perform the conventional statistical analysis using the likelihood-based Markov-Chain Monte Carlo (MCMC) methods, which scales poorly with the dimensionality of the parameter space. In this paper, we show how the Simulation-Based Inference (SBI) through Marginal Neural Ratio Estimation (MNRE) provides a step towards evading these issues. We use 21cmFAST to model the 21-cm power spectrum during CD-EoR with a six-dimensional parameter space. With the expected thermal noise from the Square Kilometre Array (SKA), we are able to accurately recover the posterior distribution for the parameters of our model at a significantly lower computational cost than the conventional likelihood-based methods. We further show how the same training dataset can be utilized to investigate the sensitivity of the model parameters over different redshifts. Our results support that such efficient and scalable inference techniques enable us to significantly extend the modeling complexity beyond what is currently achievable with conventional MCMC methods.Comment: 15 pages, 9 figures. Accepted for publication in MNRA