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Description
The semiconductor field of Photovoltaics (PV) has experienced tremendous growth, requiring curricula to consider ways to promote student success. One major barrier to success students may face when learning PV is the development of misconceptions. The purpose of this work was to determine the presence and prevalence of misconceptions students

The semiconductor field of Photovoltaics (PV) has experienced tremendous growth, requiring curricula to consider ways to promote student success. One major barrier to success students may face when learning PV is the development of misconceptions. The purpose of this work was to determine the presence and prevalence of misconceptions students may have for three PV semiconductor phenomena; Diffusion, Drift and Excitation. These phenomena are emergent, a class of phenomena that have certain characteristics. In emergent phenomena, the individual entities in the phenomena interact and aggregate to form a self-organizing pattern that can be observed at a higher level. Learners develop a different type of misconception for these phenomena, an emergent misconception. Participants (N=41) completed a written protocol. The pilot study utilized half of these protocols (n = 20) to determine the presence of both general and emergent misconceptions for the three phenomena. Once the presence of both general and emergent misconceptions was confirmed, all protocols (N=41) were analyzed to determine the presence and prevalence of general and emergent misconceptions, and to note any relationships among these misconceptions (full study). Through written protocol analysis of participants' responses, numerous codes emerged from the data for both general and emergent misconceptions. General and emergent misconceptions were found in 80% and 55% of participants' responses, respectively. General misconceptions indicated limited understandings of chemical bonding, electricity and magnetism, energy, and the nature of science. Participants also described the phenomena using teleological, predictable, and causal traits, indicating participants had misconceptions regarding the emergent aspects of the phenomena. For both general and emergent misconceptions, relationships were observed between similar misconceptions within and across the three phenomena, and differences in misconceptions were observed across the phenomena. Overall, the presence and prevalence of both general and emergent misconceptions indicates that learners have limited understandings of the physical and emergent mechanisms for the phenomena. Even though additional work is required, the identification of specific misconceptions can be utilized to enhance semiconductor and PV course content. Specifically, changes can be made to curriculum in order to limit the formation of misconceptions as well as promote conceptual change.
ContributorsNelson, Katherine G (Author) / Brem, Sarah K. (Thesis advisor) / Mckenna, Ann F (Thesis advisor) / Hilpert, Jonathan (Committee member) / Honsberg, Christiana (Committee member) / Husman, Jenefer (Committee member) / Arizona State University (Publisher)
Created2014
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Description
A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values

A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values are computed at each level within a sample. If at least one of the P-values is less than a pre-specified significance level, the chart signals out-of-control. The primary advantage of our approach is that only one control chart is required to monitor several parameters simultaneously: the intercept, slope(s), and the error standard deviation. A comprehensive comparison of the proposed method and the existing KMW-Shewhart method for monitoring linear profiles is conducted. In addition, the effect that the number of observations within a sample has on the performance of the proposed method is investigated. The proposed method was also compared to the T^2 method discussed in Kang and Albin (2000) for multivariate, polynomial, and nonlinear profiles. A simulation study shows that overall the proposed P-value method performs satisfactorily for different profile types.
ContributorsAdibi, Azadeh (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Li, Jing (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Increasing interest in individualized treatment strategies for prevention and treatment of health disorders has created a new application domain for dynamic modeling and control. Standard population-level clinical trials, while useful, are not the most suitable vehicle for understanding the dynamics of dosage changes to patient response. A secondary analysis of

Increasing interest in individualized treatment strategies for prevention and treatment of health disorders has created a new application domain for dynamic modeling and control. Standard population-level clinical trials, while useful, are not the most suitable vehicle for understanding the dynamics of dosage changes to patient response. A secondary analysis of intensive longitudinal data from a naltrexone intervention for fibromyalgia examined in this dissertation shows the promise of system identification and control. This includes datacentric identification methods such as Model-on-Demand, which are attractive techniques for estimating nonlinear dynamical systems from noisy data. These methods rely on generating a local function approximation using a database of regressors at the current operating point, with this process repeated at every new operating condition. This dissertation examines generating input signals for data-centric system identification by developing a novel framework of geometric distribution of regressors and time-indexed output points, in the finite dimensional space, to generate sufficient support for the estimator. The input signals are generated while imposing “patient-friendly” constraints on the design as a means to operationalize single-subject clinical trials. These optimization-based problem formulations are examined for linear time-invariant systems and block-structured Hammerstein systems, and the results are contrasted with alternative designs based on Weyl's criterion. Numerical solution to the resulting nonconvex optimization problems is proposed through semidefinite programming approaches for polynomial optimization and nonlinear programming methods. It is shown that useful bounds on the objective function can be calculated through relaxation procedures, and that the data-centric formulations are amenable to sparse polynomial optimization. In addition, input design formulations are formulated for achieving a desired output and specified input spectrum. Numerical examples illustrate the benefits of the input signal design formulations including an example of a hypothetical clinical trial using the drug gabapentin. In the final part of the dissertation, the mixed logical dynamical framework for hybrid model predictive control is extended to incorporate a switching time strategy, where decisions are made at some integer multiple of the sample time, and manipulation of only one input at a given sample time among multiple inputs. These are considerations important for clinical use of the algorithm.
ContributorsDeśapāṇḍe, Sunīla (Author) / Rivera, Daniel E. (Thesis advisor) / Peet, Matthew M. (Committee member) / Si, Jennie (Committee member) / Tsakalis, Konstantinos S. (Committee member) / Arizona State University (Publisher)
Created2014
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Description

The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population

The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population with an array of socio-demographic and socio-economic attributes has drawn remarkable attention due to privacy and cost constraints in collecting and disclosing full scale data. Although, there has been enormous progress in producing synthetic population, there has been less progress in the development of population evolution modeling arena to forecast future year population. The objective of this dissertation is to develop a well-structured full-fledged demographic evolution modeling system, capturing migration dynamics and evolution of person level attributes, introducing the concept of new household formations and apprehending the dynamics of household level long-term choices over time. A comprehensive study has been conducted on demography, sociology, anthropology, economics and transportation engineering area to better understand the dynamics of evolutionary activities over time and their impacts in travel behavior. This dissertation describes the methodology and the conceptual framework, and the development of model components. Demographic, socio-economic, and land use data from American Community Survey, National Household Travel Survey, Census PUMS, United States Time Series Economic Dynamic data and United States Center for Disease Control and Prevention have been used in this research. The entire modeling system has been implemented and coded using programming language to develop the population evolution module named `PopEvol' into a computer simulation environment. The module then has been demonstrated for a portion of Maricopa County area in Arizona to predict the milestone year population to check the accuracy of forecasting. The module has also been used to evolve the base year population for next 15 years and the evolutionary trend has been investigated.

ContributorsPaul, Sanjay (Author) / Pendyala, Ram M. (Thesis advisor) / Kaloush, Kamil (Committee member) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Monitoring of air pollutants is critical for many applications and studies. In

order to access air pollutants with high spatial and temporal resolutions, it is

necessary

Monitoring of air pollutants is critical for many applications and studies. In

order to access air pollutants with high spatial and temporal resolutions, it is

necessary to develop an affordable, small size and weight, low power, high

sensitivity and selectivity, and wireless enable device that can provide real time

monitoring of air pollutants. Three different kind of such devices are presented, they

are targeting environmental pollutants such as volatile organic components (VOCs),

nitrogen dioxide (NO2) and ozone. These devices employ innovative detection

methods, such as quartz crystal tuning fork coated with molecularly imprinted

polymer and chemical reaction induced color change colorimetric sensing. These

portable devices are validated using the gold standards in the laboratory, and their

functionality and capability are proved during the field tests, make them great tools

for various air quality monitoring applications.
ContributorsChen, Cheng, Ph.D (Author) / Tao, Nongjian (Thesis advisor) / Kiaei, Sayfe (Committee member) / Zhang, Yanchao (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Organic light emitting diodes (OLEDs) are a promising approach for display and solid state lighting applications. However, further work is needed in establishing the availability of efficient and stable materials for OLEDs with high external quantum efficiency's (EQE) and high operational lifetimes. Recently, significant improvements in the internal quantum efficiency

Organic light emitting diodes (OLEDs) are a promising approach for display and solid state lighting applications. However, further work is needed in establishing the availability of efficient and stable materials for OLEDs with high external quantum efficiency's (EQE) and high operational lifetimes. Recently, significant improvements in the internal quantum efficiency or ratio of generated photons to injected electrons have been achieved with the advent of phosphorescent complexes with the ability to harvest both singlet and triplet excitons. Since then, a variety of phosphorescent complexes containing heavy metal centers including Os, Ni, Ir, Pd, and Pt have been developed. Thus far, the majority of the work in the field has focused on iridium based complexes. Platinum based complexes, however, have received considerably less attention despite demonstrating efficiency's equal to or better than their iridium analogs. In this study, a series of OLEDs implementing newly developed platinum based complexes were demonstrated with efficiency's or operational lifetimes equal to or better than their iridium analogs for select cases.

In addition to demonstrating excellent device performance in OLEDs, platinum based complexes exhibit unique photophysical properties including the ability to form excimer emission capable of generating broad white light emission from a single emitter and the ability to form narrow band emission from a rigid, tetradentate molecular structure for select cases. These unique photophysical properties were exploited and their optical and electrical properties in a device setting were elucidated.

Utilizing the unique properties of a tridentate Pt complex, Pt-16, a highly efficient white device employing a single emissive layer exhibited a peak EQE of over 20% and high color quality with a CRI of 80 and color coordinates CIE(x=0.33, y=0.33). Furthermore, by employing a rigid, tetradentate platinum complex, PtN1N, with a narrow band emission into a microcavity organic light emitting diode (MOLED), significant enhancement in the external quantum efficiency was achieved. The optimized MOLED structure achieved a light out-coupling enhancement of 1.35 compared to the non-cavity structure with a peak EQE of 34.2%. In addition to demonstrating a high light out-coupling enhancement, the microcavity effect of a narrow band emitter in a MOLED was elucidated.
ContributorsEcton, Jeremy David (Author) / Li, Jian (Thesis advisor) / Adams, James (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Owner organizations in the architecture, engineering, and construction (AEC) industry are presented with a wide variety of project delivery approaches. Implementation of these approaches, while enticing due to their potential to save money, reduce schedule delays, or improve quality, is extremely difficult to accomplish and requires a concerted change management

Owner organizations in the architecture, engineering, and construction (AEC) industry are presented with a wide variety of project delivery approaches. Implementation of these approaches, while enticing due to their potential to save money, reduce schedule delays, or improve quality, is extremely difficult to accomplish and requires a concerted change management effort. Research in the field of organizational behavior cautions that perhaps more than half of all organizational change efforts fail to accomplish their intended objectives. This study utilizes an action research approach to analyze change message delivery within owner organizations, model owner project team readiness and adoption of change, and identify the most frequently encountered types of resistance from lead project members. The analysis methodology included Spearman's rank order correlation, variable selection testing via three methods of hierarchical linear regression, relative weight analysis, and one-way ANOVA. Key findings from this study include recommendations for communicating the change message within owner organizations, empirical validation of critical predictors for change readiness and change adoption among project teams, and identification of the most frequently encountered resistive behaviors within change implementation in the AEC industry. A key contribution of this research is the recommendation of change management strategies for use by change practitioners.
ContributorsLines, Brian (Author) / Sullivan, Kenneth (Thesis advisor) / Wiezel, Avi (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2014
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Description
Quantum resilience is a pragmatic theory that allows systems engineers to formally characterize the resilience of systems. As a generalized theory, it not only clarifies resilience in the literature, but also can be applied to all disciplines and domains of discourse. Operationalizing resilience in this manner permits decision-makers to compare

Quantum resilience is a pragmatic theory that allows systems engineers to formally characterize the resilience of systems. As a generalized theory, it not only clarifies resilience in the literature, but also can be applied to all disciplines and domains of discourse. Operationalizing resilience in this manner permits decision-makers to compare and contrast system deployment options for suitability in a variety of environments and allows for consistent treatment of resilience across domains. Systems engineers, whether planning future infrastructures or managing ecosystems, are increasingly asked to deliver resilient systems. Quantum resilience provides a way forward that allows specific resilience requirements to be specified, validated, and verified.

Quantum resilience makes two very important claims. First, resilience cannot be characterized without recognizing both the system and the valued function it provides. Second, resilience is not about disturbances, insults, threats, or perturbations. To avoid crippling infinities, characterization of resilience must be accomplishable without disturbances in mind. In light of this, quantum resilience defines resilience as the extent to which a system delivers its valued functions, and characterizes resilience as a function of system productivity and complexity. System productivity vis-à-vis specified “valued functions” involves (1) the quanta of the valued function delivered, and (2) the number of systems (within the greater system) which deliver it. System complexity is defined structurally and relationally and is a function of a variety of items including (1) system-of-systems hierarchical decomposition, (2) interfaces and connections between systems, and (3) inter-system dependencies.

Among the important features of quantum resilience is that it can be implemented in any system engineering tool that provides sufficient design and specification rigor (i.e., one that supports standards like the Lifecycle and Systems Modeling languages and frameworks like the DoD Architecture Framework). Further, this can be accomplished with minimal software development and has been demonstrated in three model-based system engineering tools, two of which are commercially available, well-respected, and widely used. This pragmatic approach assures transparency and consistency in characterization of resilience in any discipline.
ContributorsRoberts, Thomas Wade (Author) / Allenby, Braden (Thesis advisor) / Chester, Mikhail (Committee member) / Anderies, John M (Committee member) / Arizona State University (Publisher)
Created2015
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Description
For decades, microelectronics manufacturing has been concerned with failures related to electromigration phenomena in conductors experiencing high current densities. The influence of interconnect microstructure on device failures related to electromigration in BGA and flip chip solder interconnects has become a significant interest with reduced individual solder interconnect volumes. A survey

For decades, microelectronics manufacturing has been concerned with failures related to electromigration phenomena in conductors experiencing high current densities. The influence of interconnect microstructure on device failures related to electromigration in BGA and flip chip solder interconnects has become a significant interest with reduced individual solder interconnect volumes. A survey indicates that x-ray computed micro-tomography (µXCT) is an emerging, novel means for characterizing the microstructures' role in governing electromigration failures. This work details the design and construction of a lab-scale µXCT system to characterize electromigration in the Sn-0.7Cu lead-free solder system by leveraging in situ imaging.

In order to enhance the attenuation contrast observed in multi-phase material systems, a modeling approach has been developed to predict settings for the controllable imaging parameters which yield relatively high detection rates over the range of x-ray energies for which maximum attenuation contrast is expected in the polychromatic x-ray imaging system. In order to develop this predictive tool, a model has been constructed for the Bremsstrahlung spectrum of an x-ray tube, and calculations for the detector's efficiency over the relevant range of x-ray energies have been made, and the product of emitted and detected spectra has been used to calculate the effective x-ray imaging spectrum. An approach has also been established for filtering `zinger' noise in x-ray radiographs, which has proven problematic at high x-ray energies used for solder imaging. The performance of this filter has been compared with a known existing method and the results indicate a significant increase in the accuracy of zinger filtered radiographs.

The obtained results indicate the conception of a powerful means for the study of failure causing processes in solder systems used as interconnects in microelectronic packaging devices. These results include the volumetric quantification of parameters which are indicative of both electromigration tolerance of solders and the dominant mechanisms for atomic migration in response to current stressing. This work is aimed to further the community's understanding of failure-causing electromigration processes in industrially relevant material systems for microelectronic interconnect applications and to advance the capability of available characterization techniques for their interrogation.
ContributorsMertens, James Charles Edwin (Author) / Chawla, Nikhilesh (Thesis advisor) / Alford, Terry (Committee member) / Jiao, Yang (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2015
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Description
In this thesis, a novel silica nanosphere (SNS) lithography technique has been developed to offer a fast, cost-effective, and large area applicable nano-lithography approach. The SNS can be easily deposited with a simple spin-coating process after introducing a N,N-dimethyl-formamide (DMF) solvent which can produce a highly close packed SNS monolayer

In this thesis, a novel silica nanosphere (SNS) lithography technique has been developed to offer a fast, cost-effective, and large area applicable nano-lithography approach. The SNS can be easily deposited with a simple spin-coating process after introducing a N,N-dimethyl-formamide (DMF) solvent which can produce a highly close packed SNS monolayer over large silicon (Si) surface area, since DMF offers greatly improved wetting, capillary and convective forces in addition to slow solvent evaporation rate. Since the period and dimension of the surface pattern can be conveniently changed and controlled by introducing a desired size of SNS, and additional SNS size reduction with dry etching process, using SNS for lithography provides a highly effective nano-lithography approach for periodically arrayed nano-/micro-scale surface patterns with a desired dimension and period. Various Si nanostructures (i.e., nanopillar, nanotip, inverted pyramid, nanohole) are successfully fabricated with the SNS nano-lithography technique by using different etching technique like anisotropic alkaline solution (i.e., KOH) etching, reactive-ion etching (RIE), and metal-assisted chemical etching (MaCE).

In this research, computational optical modeling is also introduced to design the Si nanostructure, specifically nanopillars (NPs) with a desired period and dimension. The optical properties of Si NP are calculated with two different optical modeling techniques, which are the rigorous coupled wave analysis (RCWA) and finite-difference time-domain (FDTD) methods. By using these two different optical modeling techniques, the optical properties of Si NPs with different periods and dimensions have been investigated to design ideal Si NP which can be potentially used for thin c-Si solar cell applications. From the results of the computational and experimental work, it was observed that low aspect ratio Si NPs fabricated in a periodic hexagonal array can provide highly enhanced light absorption for the target spectral range (600 ~ 1100nm), which is attributed to (1) the effective confinement of resonant scattering within the Si NP and (2) increased high order diffraction of transmitted light providing an extended absorption length. From the research, therefore, it is successfully demonstrated that the nano-fabrication process with SNS lithography can offer enhanced lithographical accuracy to fabricate desired Si nanostructures which can realize enhanced light absorption for thin Si solar cell.
ContributorsChoi, JeaYoung (Author) / Honsberg, Christiana (Thesis advisor) / Alford, Terry (Thesis advisor) / Goodnick, Stephen (Committee member) / Arizona State University (Publisher)
Created2015