Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

Structure, Morphology, and Electrochemical Properties of Transition Metal Oxide, Hydroxide, and Phosphate Nanomaterials for Energy Storage

Energy storage technologies are crucial for efficient utilization of electricity. Supercapacitors and rechargeable batteries are of currently available energy storage systems. Transition metal oxides, hydroxides, and phosphates are the most intensely investigated electrode materials for supercapacitors and rechargeable batteries due to their high theoretical charge storage capacities resulted from reversible electrochemical reactions. Their insulating nature, however, causes sluggish electron transport kinetics within these electrode materials, hindering them from reaching the theoretical maximum. The conductivity of these transition metal based-electrode materials can be improved through three main approaches; nanostructuring, chemical substitution, and introducing carbon matrices. These approaches often lead to unique electrochemical properties when combined and balanced.

Ethanol-mediated solvothermal synthesis we developed is found to be highly effective for controlling size and morphology of transition metal-based electrode materials for both pseudocapacitors and batteries. The morphology and the degree of crystallinity of nickel hydroxide are systematically changed by adding various amounts glucose to the solvothermal synthesis. Nickel hydroxide produced in this manner exhibited increased pseudocapacitance, which is partially attributed to the increased surface area. Interestingly, this morphology effect on cobalt doped-nickel hydroxide is found to be more effective at low cobalt contents than at high cobalt contents in terms of improving the electrochemical performance.

Moreover, a thin layer of densely packed nickel oxide flakes on carbon paper substrate was successfully prepared via the glucose-assisted solvothermal synthesis, resulting in the improved electrode conductivity. When reduced graphene oxide was used for conductive coating on as-prepared nickel oxide electrode, the electrode conductivity was only slightly improved. This finding reveals that the influence of reduced graphene oxide coating, increasing the electrode conductivity, is not that obvious when the electrode is already highly conductive to begin with.

We were able to successfully control the interlayer spacing and reduce the particle size of layered titanium hydrogeno phosphate material using our ethanol-mediated solvothermal reaction. In layered structure, interlayer spacing is the key parameter for fast ion diffusion kinetics. The nanosized layered structure prepared via our method, however, exhibited high sodium-ion storage capacity regardless of the interlayer spacing, implying that interlayer space may not be the primary factor for sodium-ion diffusion in nanostructured materials, where many interstitials are available for sodium-ion diffusion.

Our ethanol-mediated solvothermal reaction was also effective for synthesis of NaTi2(PO4)3 nanoparticles with uniform size and morphology, well connected by a carbon nanotube network. This composite electrode exhibited high capacity, which is comparable to that in aqueous electrolyte, probably due to the uniform morphology and size where the preferable surface for sodium-ion diffusion is always available in all individual particles.

Fundamental understandings of the relationship between electrode microstructures and electrochemical properties discussed in this dissertation will be important to design high performance energy storage system applications.

A Search for a New Particle Decaying to Pairs of Weak Gauge Bosons with the ATLAS Detector

A search for new heavy resonances decaying to boson pairs (WZ, WW or ZZ) using 20.3 inverse femtobarns of proton-proton collision data at a center of mass energy of 8 TeV is presented. The data were recorded by the ATLAS detector at the Large Hadron Collider (LHC) in 2012. The analysis combines several search channels with the leptonic, semi-leptonic and fully hadronic final states. The diboson invariant mass spectrum is studied for local excesses above the Standard Model background prediction, and no significant excess is observed for the combined analysis. 95$\%$ confidence limits are set on the cross section times branching ratios for three signal models: an extended gauge model with a heavy W boson, a bulk Randall-Sundrum model with a spin-2 graviton, and a simplified model with a heavy vector triplet. Among the individual search channels, the fully-hadronic channel is predominantly presented where boson tagging technique and jet substructure cuts are used. Local excesses are found in the dijet mass distribution around 2 TeV, leading to a global significance of 2.5 standard deviations. This deviation from the Standard Model prediction results in many theory explanations, and the possibilities could be further explored using the LHC Run 2 data.

Ground and Electronic Excited States from Pairing Matrix Fluctuation and Particle-Particle Random Phase Approximation

The accurate description of ground and electronic excited states is an important and challenging topic in quantum chemistry. The pairing matrix fluctuation, as a counterpart of the density fluctuation, is applied to this topic. From the pairing matrix fluctuation, the exact electron correlation energy as well as two electron addition/removal energies can be extracted. Therefore, both ground state and excited states energies can be obtained and they are in principle exact with a complete knowledge of the pairing matrix fluctuation. In practice, considering the exact pairing matrix fluctuation is unknown, we adopt its simple approximation --- the particle-particle random phase approximation (pp-RPA) --- for ground and excited states calculations. The algorithms for accelerating the pp-RPA calculation, including spin separation, spin adaptation, as well as an iterative Davidson method, are developed. For ground states correlation descriptions, the results obtained from pp-RPA are usually comparable to and can be more accurate than those from traditional particle-hole random phase approximation (ph-RPA). For excited states, the pp-RPA is able to describe double, Rydberg, and charge transfer excitations, which are challenging for conventional time-dependent density functional theory (TDDFT). Although the pp-RPA intrinsically cannot describe those excitations excited from the orbitals below the highest occupied molecular orbital (HOMO), its performances on those single excitations that can be captured are comparable to TDDFT. The pp-RPA for excitation calculation is further applied to challenging diradical problems and is used to unveil the nature of the ground and electronic excited states of higher acenes. The pp-RPA and the corresponding Tamm-Dancoff approximation (pp-TDA) are also applied to conical intersections, an important concept in nonadiabatic dynamics. Their good description of the double-cone feature of conical intersections is in sharp contrast to the failure of TDDFT. All in all, the pairing matrix fluctuation opens up new channel of thinking for quantum chemistry, and the pp-RPA is a promising method in describing ground and electronic excited states.