Nuclear hyperfine interactions in W^(182), W^(183), and Pt^(195) as studies by the Mossbauer effect

The Mössbauer technique has been used to study the nuclear hyperfine interactions and lifetimes in W¹⁸² (2⁺ state) and W¹⁸³ (3/2^- and 5/2^- states) with the following results: g(5/2^-)/g(2⁺) = 1.40 ± 0.04; g(3/2^- = -0.07 ± 0.07; Q(5/2^-)/Q(2⁺) = 0.94 ± 0.04; T_1/2(3/2^-) = 0.184 ± 0.005 nsec; T_1/2(5/2^-) >̰ 0.7 nsec. These quantities are discussed in terms of a rotation-particle interaction in W¹⁸³ due to Coriolis coupling. From the measured quantities and additional information on γ-ray transition intensities magnetic single-particle matrix elements are derived. It is inferred from these that the two effective g-factors, resulting from the Nilsson-model calculation of the single-particle matrix elements for the spin operators ŝ_z and ŝ₊, are not equal, consistent with a proposal of Bochnacki and Ogaza.

The internal magnetic fields at the tungsten nucleus were determined for substitutional solid solutions of tungsten in iron, cobalt, and nickel. With g(2⁺) = 0.24 the results are: |H_eff(W-Fe)| = 715 ± 10 kG; |H_eff(W-Co)| = 360 ± 10 kG; |H_eff(W-Ni)| = 90 ± 25 kG. The electric field gradients at the tungsten nucleus were determined for WS₂ and WO₃. With Q(2⁺) = -1.81b the results are: for WS₂, eq = -(1.86 ± 0.05) 10¹⁸ V/cm²; for WO₃, eq = (1.54 ± 0.04) 10¹⁸ V/cm² and ƞ = 0.63 ± 0.02.

The 5/2^- state of Pt¹⁹⁵ has also been studied with the Mössbauer technique, and the g-factor of this state has been determined to be -0.41 ± 0.03. The following magnetic fields at the Pt nucleus were found: in an Fe lattice, 1.19 ± 0.04 MG; in a Co lattice, 0.86 ± 0.03 MG; and in a Ni lattice, 0.36 ± 0.04 MG. Isomeric shifts have been detected in a number of compounds and alloys and have been interpreted to imply that the mean square radius of the Pt¹⁹⁵ nucleus in the first-excited state is smaller than in the ground state.

An experimental study of a stellar neutron source: ^(13)C(α, n)^(16)O

Described in this thesis are measurements made of the thick-target neutron yield from the reaction ¹³C(α, n)¹⁶O. The yield was determined for laboratory bombarding energies between 0.475 and 0.700 MeV, using a stilbene crystal neutron detector and pulse-shape discrimination to eliminate gamma rays. Stellar temperatures between 2.5 and 4.5 x 10^{8 o}K are involved in this energy region. From the neutron yield was extracted the astrophysical cross-section factor S(E), which was found to fit a linear function: S(E) = [(5.48 ± 1.77) + (12.05 ± 3.91)E] x 10⁵ MeV-barns, center-of-mass system. The stellar rate of the ¹³C(α, n)¹⁶O reaction if calculated, and discussed with reference to helium burning and neutron production in the core of a giant star.

Results are also presented of measurements carried out on the reaction ⁹Be(α, n)¹²C, taken with a thin Be target. The bombarding energy-range covered was from 0.340 to 0.680 MeV, with excitation curves for the ground- and first excited-state neutrons being reported. Some angular distributions were also measured. Resonances were found at bombarding energies of E_LAB = 0.520 MeV (E_CM = 0.360 MeV, Γ ~ 55 keV CM, ωγ = 3.79 eV CM) and E_LAB = 0.600 MeV (E_CM = 0.415 MeV, Γ ˂ 4 keV CM, ωγ = 0.88 eV CM). The astrophysical rate of the ⁹Be(α, n)¹²C reaction due to these resonances is calculated.

Transonic wind tunnel for investigation of turbomachinery blades

The design of a two-stream wind tunnel was undertaken to allow the simulation and study of certain features of the flow field around the blades of high-speed axial-flow turbomachineries. The mixing of the two parallel streams with designed Mach numbers respectively equal to 1.4 and 0.7 will simulate the transonic Mach number distribution generally obtained along the tips of the first stage blades in large bypass-fan engines.

The GALCIT hypersonic compressor plant will be used as an air supply for the wind tunnel, and consequently the calculations contained in the first chapter are derived from the characteristics and the performance of this plant.

The transonic part of the nozzle is computed by using a method developed by K. O. Friedrichs. This method consists essentially of expanding the coordinates and the characteristics of the flow in power series. The development begins with prescribing, more or less arbitrarily, a Mach number distribution along the centerline of the nozzle. This method has been programmed for an IBM 360 computer to define the wall contour of the nozzle.

A further computation is carried out to correct the contour for boundary layer buildup. This boundary layer analysis included geometry, pressure gradient, and Mach number effects. The subsonic nozzle is calculated {including boundary layer buildup) by using the same computer programs. Finally, the mixing zone downstream of the splitter plate was investigated to prescribe the wall contour correction necessary to ensure a constant-pressure test section.

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.

Bicyclo[3.2.0]hepta-1,4,6-triene: synthesis, thermal rearrangement, and anion formation

In order to determine the properties of the bicycloheptatrienyl anion (Ia) (predicted to be conjugatively stabilized by Hückel Molecular Orbital Theory) the neutral precursor, bicyclo[3. 2. 0] hepta-1, 4, 6-triene (I) was prepared by the following route.

Reaction of I with potassium-t-butoxide, potassium, or lithium dicyclohexylamide gave anion Ia in very low yield. Reprotonation of I was found to occur solely at the 1 or 5 position to give triene II, isolated as to its dimers.

A study of the acidity of I and of other conjugated hydrocarbons by means of ion cyclotron resonance spectroscopy resulted in determination of the following order of relative acidities:

H₂S ˃ C₅H₆ ˃ CH₃NO₂ ˃ 1, 4- C₅H₈ ˃ I ˃ C₂H₅OH ˃ H₂O; cyclo-C₇H₈ ˃ C₂ H₅OH; фCH₃ ˃ CH₃OH

In addition, limits for the proton affinities of the conjugate bases were determined:

350 kcal/mole ˂ PA(C₅ H₅^-) ˂ 360 kcal/mole

362 kcal/mole ˂ PA(C₅H₇^-, Ia, cyclo-C₇H₇^-) ˂ 377 kcal/mole PA(фCH₂^-) ˂ 385 kcal/mole

Gas phase kinetics of the trans-XVIII to I transformation gave the following activation parameters: E_a = 43.0 kcal/mole, log A = 15.53 and ∆S^ǂ (220°) = 9.6 cu. The results were interpreted as indicating initial 1,2 bond cleavage to give the 1,3-diradical which closed to I. Similar studies on cis-XVIII gave results consistent with a surface component to the reaction (E_a = 22.7 kcal/mole; log A = 9.23, ∆S^ǂ (119°) = -18.9 eu).

The low pressure (0.01 to 1 torr) pyrolysis of trans-XVIII gave in addition to I, fulvenallene (LV), ethynylcyclopentadiene (LVI) and heptafulvalene (LVII). The relative ratios of the C₇H₆ isomers were found to be dependent upon temperature and pressure, higher relative pressure and lower temperatures favoring formation of I. The results were found to be consistent with the intermediacy of vibrationally excited I and subsequent reaction to give LV and LVI.