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- All Subjects: Nanoindentation
- Creators: Ford, Emily Lucile
- Status: Published
Description
This dissertation is on the study of structural and optical properties of some III-V and II-VI compound semiconductors. The first part of this dissertation is a study of the deformation mechanisms associated with nanoindentation and nanoscratching of InP, GaN, and ZnO crystals. The second part is an investigation of some fundamental issues regarding compositional fluctuations and microstructure in GaInNAs and InAlN alloys. In the first part, the microstructure of (001) InP scratched in an atomic force microscope with a small diamond tip has been studied as a function of applied normal force and crystalline direction in order to understand at the nanometer scale the deformation mechanisms in the zinc-blende structure. TEM images show deeper dislocation propagation for scratches along <110> compared to <100>. High strain fields were observed in <100> scratches, indicating hardening due to locking of dislocations gliding on different slip planes. Reverse plastic flow have been observed in <110> scratches in the form of pop-up events that result from recovery of stored elastic strain. In a separate study, nanoindentation-induced plastic deformation has been studied in c-, a-, and m-plane ZnO single crystals and c-plane GaN respectively, to study the deformation mechanism in wurtzite hexagonal structures. TEM results reveal that the prime deformation mechanism is slip on basal planes and in some cases, on pyramidal planes, and strain built up along particular directions. No evidence of phase transformation or cracking was observed in both materials. CL imaging reveals quenching of near band-edge emission by dislocations. In the second part, compositional inhomogeneity in quaternary GaInNAs and ternary InAlN alloys has been studied using TEM. It is shown that exposure to antimony during growth of GaInNAs results in uniform chemical composition in the epilayer, as antimony suppresses the surface mobility of adatoms that otherwise leads to two-dimensional growth and elemental segregation. In a separate study, compositional instability is observed in lattice-matched InAlN films grown on GaN, for growth beyond a certain thickness. Beyond 200 nm of thickness, two sub-layers with different indium content are observed, the top one with lower indium content.
ContributorsHuang, Jingyi (Author) / Ponce, Fernando A. (Thesis advisor) / Carpenter, Ray W (Committee member) / Smith, David J. (Committee member) / Yu, Hongbin (Committee member) / Treacy, Michael Mj (Committee member) / Arizona State University (Publisher)
Created2013
Description
Productivity in the construction industry is an essential measure of production efficiency and economic progress, quantified by craft laborers' time spent directly adding value to a project. In order to better understand craft labor productivity as an aspect of lean construction, an activity analysis was conducted at the Arizona State University Palo Verde Main engineering dormitory construction site in December of 2016. The objective of this analysis on craft labor productivity in construction projects was to gather data regarding the efficiency of craft labor workers, make conclusions about the effects of time of day and other site-specific factors on labor productivity, as well as suggest improvements to implement in the construction process. Analysis suggests that supporting tasks, such as traveling or materials handling, constitute the majority of craft labors' efforts on the job site with the highest percentages occurring at the beginning and end of the work day. Direct work and delays were approximately equal at about 20% each hour with the highest peak occurring at lunchtime between 10:00 am and 11:00 am. The top suggestion to improve construction productivity would be to perform an extensive site utilization analysis due to the confined nature of this job site. Despite the limitations of an activity analysis to provide a complete prospective of all the factors that can affect craft labor productivity as well as the small number of days of data acquisition, this analysis provides a basic overview of the productivity at the Palo Verde Main construction site. Through this research, construction managers can more effectively generate site plans and schedules to increase labor productivity.
ContributorsFord, Emily Lucile (Author) / Grau, David (Thesis director) / Chong, Oswald (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Ultra High Performance (UHP) cementitious binders are a class of cement-based materials with high strength and ductility, designed for use in precast bridge connections, bridge superstructures, high load-bearing structural members like columns, and in structural repair and strengthening. This dissertation aims to elucidate the chemo-mechanical relationships in complex UHP binders to facilitate better microstructure-based design of these materials and develop machine learning (ML) models to predict their scale-relevant properties from microstructural information.To establish the connection between micromechanical properties and constitutive materials, nanoindentation and scanning electron microscopy experiments are performed on several cementitious pastes. Following Bayesian statistical clustering, mixed reaction products with scattered nanomechanical properties are observed, attributable to the low degree of reaction of the constituent particles, enhanced particle packing, and very low water-to-binder ratio of UHP binders. Relating the phase chemistry to the micromechanical properties, the chemical intensity ratios of Ca/Si and Al/Si are found to be important parameters influencing the incorporation of Al into the C-S-H gel.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ML algorithms for classification of cementitious phases are found to require only the intensities of Ca, Si, and Al as inputs to generate accurate predictions for more homogeneous cement pastes. When applied to more complex UHP systems, the overlapping chemical intensities in the three dominant phases – Ultra High Stiffness (UHS), unreacted cementitious replacements, and clinker – led to ML models misidentifying these three phases. Similarly, a reduced amount of data available on the hard and stiff UHS phases prevents accurate ML regression predictions of the microstructural phase stiffness using only chemical information. The use of generic virtual two-phase microstructures coupled with finite element analysis is also adopted to train MLs to predict composite mechanical properties. This approach applied to three different representations of composite materials produces accurate predictions, thus providing an avenue for image-based microstructural characterization of multi-phase composites such UHP binders. This thesis provides insights into the microstructure of the complex, heterogeneous UHP binders and the utilization of big-data methods such as ML to predict their properties. These results are expected to provide means for rational, first-principles design of UHP mixtures.
ContributorsFord, Emily Lucile (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Mobasher, Barzin (Committee member) / Chawla, Nikhilesh (Committee member) / Hoover, Christian G. (Committee member) / Maneparambil, Kailas (Committee member) / Arizona State University (Publisher)
Created2020