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Thesis (Ph. D.)--Georgia State University, 1997. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (p. 79-83).

Thesis (Ph. D.)--School of Pharmacy and School of Dentistry. University of Missouri--Kansas City, 1998. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--Georgia State University, 1997. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (p. 79-83).

The overall objective of this study is to develop methods for providing a statistical summary of material fatigue stress-life (S-N) data for engineering design purposes. Specific goals are: (1) Development of an analytical model for characterizing fatigue strength. This model would include: (a) a description of the trend of the data (e.g., the median curve through the data), (b) a description of the scatter of the data (e.g., the standard deviation of N as a function of S), and (c) the statistical distribution of N given S or S given N. (2) Development of an algorithm for constructing a design curve from the data. The curve should be on the safe side of the data and should reflect uncertainties in the physical process as well as statistical uncertainty associated with small sample sizes. (3) Development of a statistical model that can be applied in a structural reliability analysis in which all design factors are treated as random variables. Significant achievements are: (1) Demonstration, using representative fatigue data sets, that the bilinear model seems to provide a consistently adequate description of the trend of fatigue data. (2) Demonstration, using representative fatigue data sets, that the pure X error source model seems to provide a consistently adequate description of the uncertainties observed in heteroscedastic fatigue data. The pure X error source model is based on recognition of the uncertainties in local fatigue stress. (3) Development of a procedure for constructing a design curve using the tolerance limit concept developed by D. B. Owen. A more practical simplified or approximate Owen curve was shown to have a minimum loss of confidence level, relative to exact Owen theory, under fairly general conditions. (4) Recommendations for methods of developing a statistical model for reliability analysis. A comprehensive study of this issue was not pursued.

Graphene, the thinnest material with a stable 2D structure, is a potential alternative for silicon-based electronics. However, zero band gap of graphene causes a poor on-off ratio of current thus making it unsuitable for logic operations. This problem prompted scientists to find other suitable 2D materials. Creating vacancy defects or synthesizing hybrid 2D planar interfaces with other 2D materials, is also quite promising for modifying graphene properties. Experimental productions of these materials lead to the formation of possible defects and impurities with significant influence in device properties. Hence, a detailed understanding of the effects of impurities and defects on the properties of 2D systems is quite important. In this thesis, detailed studies have been done on the effects of impurities and defects on graphene, hybrid graphene/h-BN and graphene/graphane structures, silicene and transition metal dichalcogenides (TMDs) by ab-initio density functional theory (DFT). We have also looked into the possibilities of realizing magnetic nanostructures, trapped at the vacancy defects in graphene, at the reconstructed edges of graphene nanoribbons, at the planar hybrid h-BN graphene structures, and in graphene/graphane interfaces. A thorough investigation of diffusion of Fe adatoms and clusters by ab-initio molecular dynamics simulations have been carried out along with the study of their magnetic properties. It has been shown that the formation of Fe clusters at the vacancy sites is quite robust. We have also demonstrated that the quasiperiodic 3D heterostructures of graphene and h-BN are more stable than their regular counterpart and certain configurations can open up a band gap. Using our extensive studies on defects, we have shown that defect states occur in the gap region of TMDs and they have a strong signature in optical absorption spectra. Defects in silicene and graphene cause an increase in scattering and hence an increase in local currents, which may be detrimental for electronic devices. Last but not the least, defects in graphene can also be used to facilitate gas sensing of molecules as well as and local site selective fluorination.

Semiconductor devices based on III-V materials have been the focus of intense research due to their superior electron mobility and favorable energy direct bandgap which are applicable in infrared wavelength range optoelectronics and high speed electronic systems. The thesis presented here consists of two thrusts; the first focusing on infrared applications, and the second focusing on InP-based heterojunction bipolar transistors (HBTs). In the first thrust, we investigate type-II InAs/GaSb superlattice IR detector devices and the effect of substrate orientation on InSb and InAs nanostructure morphology. In the second thrust, we study InP-based high frequency HBTs. A low resistance InAs ohmic contact is demonstrated, and we presented along with a study of the crystalline qualities in GaAs0.5Sb0.5 films grown on tilted- axis InP substrates. Chapter 2 presents fabrication and characterization of two type-II superlattice structures with 15 monolayer (ML) InAs/12ML GaSb and 17ML InAs/7ML GaSb grown on GaSb (100) substrates by solid-source molecular beam epitaxy (MBE). The X-ray diffraction (XRD) measurements of both the 15ML InAs/12ML GaSb and 17MLInAs/7ML GaSb superlattices indicated excellent material and interface qualities. The cutoff wavelengths of 15ML InAs/12ML GaSb and 17ML InAs/7ML GaSb superlattices photodetectors were measured to be 6.6μm and 10.2μm, respectively. These different spectral ranges were achieved by growing alternating layers of varying thicknesses which allowed for bandgap engineering of the superlattices of InAs and GaSb. Lastly, a mid-IR type-II superlattice photodiode was demonstrated at 80K with a cutoff wavelength at 6.6µm. The device exhibited a near background limited performance (BLIP) detectivity at 80K and higher temperature operation up to 280K. In Chapter 3, we show that the (411) orientation, though not a naturally occurring surface, is a favorable orientation to develop a buffer layer into a super flat surface at a certain high growth temperature. The (411) surface is a combination of localized (311) and (511) surfaces but at a high growth temperature, adatoms can obtain enough energy to overcome the energy barrier between these localized (311) and (511) surfaces and form a uniform (411) surface with potential minima. This results in a super flat surface which is promising for high-density nanostructure growth. In this work, this is the first time that the highest InSb and InAs nanostructures density can be achieved on the (411) surface which is in comparison with the (100), (311), and (511) surfaces. Chapter 4 of this thesis addresses the use of an InAs layer as a low-resistance ohmic contact to InP-based heterostructure devices. Selective area crystal growth of InAs on a dielectric (Benzocyclobutene, BCB polymer) covered InP (100) substrate and direct growth of InAs on InP substrate were performed by MBE. Heavy doping of InAs using Te was carried out to determine the lowest sheet resistance. Based on scanning electron microscope (SEM) and XRD measurements, increasing substrate temperature from 210 ℃ to 350 ℃, led to an improvement in crystallinity from a polycrystalline layer to a single crystal layer with a corresponding improvement of surface morphology. Moreover, a narrow X-ray diffraction peak indicated full-relaxation of the inherent 3.3% lattice-mismatch in InAs/InP layers. Furthermore, around 290 ℃ a tradeoff was reached between crystallinity and optimized dopant incorporation of Te into InAs for the lowest sheet resistance. Lastly, Chapter 5 discusses the effect of substrate tilting on the material properties of MBE grown GaAsSb alloys closely lattice-matched to an InP substrate. InP(100) substrates tilted 0°off-(on-axis), 2°off-, 3°off-, and 4°off-axis were used for MBE growth; then the material qualities of GaAsSb epitaxial layers were compared using various techniques, including high resolution XRD, photoluminescence (PL) and transmission-line measurements (TLM). Substrate tilting improved the crystalline quality of the GaAsSb alloys, as shown by a narrower XRD linewidth and enhanced optical quality as evidenced by a strong PL peak. The results of TLM show that the lowest sheet resistance was achieved at a 2° off-axis tilt.

Nanoscale structural characterization is critical to understanding the physical underpinnings of properties and behavior in materials with technological applications. The work herein shows how the pair distribution function technique can be applied to x-ray total scattering data for material systems which weakly scatter x-rays, a typically difficult task due to the poor signal-to-noise obtained from the structures of interest. Characterization and structural modeling are demonstrated for a variety of molecular and porous systems, along with the detection and characterization of disordered, minority phases and components. In particular, reliable detection and quantitative analysis are demonstrated for nanocrystals of an active pharmaceutical ingredient suspended in dilute solution down to a concentration of 0.25 wt. %, giving a practical limit of detection for ordered nanoscale phases within a disordered matrix. Further work shows that minority nanocrystalline phases can be detected, fingerprinted, and modeled for mixed crystalline and amorphous systems of small molecules and polymers. The crystallization of amorphous lactose is followed under accelerated aging conditions. Melt quenching is shown to produce a different local structure than spray drying or freeze drying, along with increased resistance to crystallization. The initial phases which form in the spray dried formulation are identified as a mixture of polymorphs different from the final α-lactose monohydrate form. Hard domain formation in thermoplastic polyurethanes is also characterized as a function of methylene diphenyl diisocyanate and butanediol component ratio, showing that distinct and different hard phase structures can form and are solved by indexing with structures derived from molecular dynamics relaxation. In both cases, phase fractions can be quantified in the mixed crystalline and amorphous systems by fitting with both standards or structure models. Later chapters, demonstrate pair distribution characterization of particle incorporation, structure, and synthesis of nanoporous materials. Nanoparticle size distributions are extracted from platinum nanoparticles nucleating within a zeolite matrix through structural modeling, and validated by transmission electron microscope studies. The structure of zirconium phosphonate-phosphate unconventional metal organic framework is determined to consist of turbostratically disordered nanocrystalline layers of Zr-phenylphosphonate, and the local environment of terbium intercalated between the layers is found to resemble the local environment in scheelite-type terbium phosphate. Finally, the early stages of reaction between aqueous zinc dinitrate hexahydrate and methanolic 2-methylimidazole are characterized using in situ total scattering measurements, showing that secondary building units of tetrahedrally coordinated by 2-methylimidazole initially form upon reaction. Overall, the methodologies are developed and applied toward phase detection, identification, solution, and behavior in pharmaceuticals, polymers, and nanoporous materials along with advice for carrying out experiments and analysis on such materials such that they can be extended to other similar systems.

The random nature of the micro-structural attributes in materials in general and composite material systems in particular requires expansion of material modeling in a way that will incorporate their inherent uncertainty and predict its impact on material properties and mechanical response in multiple scales. Despite the importance of capturing and modeling material randomness, there are numerous challenges in structural characterization that are yet to be addressed. The work presented in this essay takes a few steps towards an improved material modeling approach which encompasses structural randomness in order to produce a more realistic representation of material systems. For this end a computational framework was developed to generate a realistic representative volume element which reflects the inherent structural randomness. First stochastic structural elements were identified and registered from imaging data and parameters were assigned to represent those elements. Statistical characterization of the random attributes was followed by the construction of a representative volume element which shared the same structural statistical characteristics with the original material system. The resultant statistical equivalent representative volume element (SERVE) was then used in finite element simulations which provided homogenized properties and mechanical response predictions. The suggested framework was developed and then implemented on 3 different material systems. Image processing and analysis in one of the material systems extended the original scope of this work to solving a machine vision and learning problem. Object segmentation for the purpose object and pattern recognition has been a long standing subject of interest in the field of machine vision. Despite the significant attention given to the development of segmentation and recognition methods, the critical challenge of separating merged objects did not share the spotlight. A simple yet original approach to overcome this hurdle was developed using unsupervised classification and separation of objects in 3D. Lower dimensionality classifiers were joined to provide a powerful higher dimensionality classification tool. The robustness of this approach is illustrated through its implementation on two case studies of merged objects. Applications of this methodology can further extend from structural classification to general problems of clustering and classification in various fields.