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ContributorsJavidahmadabadi, Mahdi (Author) / Kitchen, Jennifer (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Aberle, James T., 1961- (Committee member) / Arizona State University (Publisher)
Created2015
Description
In this work a newly fabricated organic solar cell based on a composite of fullerene derivative [6,6]-phenyl-C61 butyric acid methyl ester (PCBM) and regioregular poly (3-hexylthiophene) (P3HT) with an added interfacial layer of AgOx in between the PEDOT:PSS layer and the ITO layer is investigated. Previous equivalent circuit models are discussed and an equivalent circuit model is proposed for the fabricated device. Incorporation of the AgOx interfacial layer shows an increase in fill factor (by 33%) and power conversion efficiency (by 28%). Moreover proper correlation has been achieved between the experimental and simulated I-V plots. The simulation shows that device characteristics can be explained with accuracy by the proposed model.
ContributorsHossain, Nazmul (Author) / Alford, Terry L. (Thesis advisor) / Theodore, David (Committee member) / Krause, Stephen (Committee member) / Arizona State University (Publisher)
Created2015
Towards high-efficiency thin-film solar cells: from theoretical analysis to experimental exploration
Description
GaAs single-junction solar cells have been studied extensively in recent years, and have reached over 28 % efficiency. Further improvement requires an optically thick but physically thin absorber to provide both large short-circuit current and high open-circuit voltage. By detailed simulation, it is concluded that ultra-thin GaAs cells with hundreds of nanometers thickness and reflective back scattering can potentially offer efficiencies greater than 30 %. The 300 nm GaAs solar cell with AlInP/Au reflective back scattering is carefully designed and demonstrates an efficiency of 19.1 %. The device performance is analyzed using the semi-analytical model with Phong distribution implemented to account for non-Lambertian scattering. A Phong exponent m of ~12, a non-radiative lifetime of 130 ns, and a specific series resistivity of 1.2 Ω·cm2 are determined.
Thin-film CdTe solar cells have also attracted lots of attention due to the continuous improvements in their device performance. To address the issue of the lower efficiency record compared to detailed-balance limit, the single-crystalline Cd(Zn)Te/MgCdTe double heterostructures (DH) grown on InSb (100) substrates by molecular beam epitaxy (MBE) are carefully studied. The Cd0.9946Zn0.0054Te alloy lattice-matched to InSb has been demonstrated with a carrier lifetime of 0.34 µs observed in a 3 µm thick Cd0.9946Zn0.0054Te/MgCdTe DH sample. The substantial improvement of lifetime is due to the reduction in misfit dislocation density. The recombination lifetime and interface recombination velocity (IRV) of CdTe/MgxCd1-xTe DHs are investigated. The IRV is found to be dependent on both the MgCdTe barrier height and width due to the thermionic emission and tunneling processes. A record-long carrier lifetime of 2.7 µs and a record-low IRV of close to zero have been confirmed experimentally.
The MgCdTe/Si tandem solar cell is proposed to address the issue of high manufacturing costs and poor performance of thin-film solar cells. The MBE grown MgxCd1-xTe/MgyCd1-yTe DHs have demonstrated the required bandgap energy of 1.7 eV, a carrier lifetime of 11 ns, and an effective IRV of (1.869 ± 0.007) × 103 cm/s. The large IRV is attributed to thermionic-emission induced interface recombination. These understandings can be applied to fabricating the high-efficiency low-cost MgCdTe/Si tandem solar cell.
Thin-film CdTe solar cells have also attracted lots of attention due to the continuous improvements in their device performance. To address the issue of the lower efficiency record compared to detailed-balance limit, the single-crystalline Cd(Zn)Te/MgCdTe double heterostructures (DH) grown on InSb (100) substrates by molecular beam epitaxy (MBE) are carefully studied. The Cd0.9946Zn0.0054Te alloy lattice-matched to InSb has been demonstrated with a carrier lifetime of 0.34 µs observed in a 3 µm thick Cd0.9946Zn0.0054Te/MgCdTe DH sample. The substantial improvement of lifetime is due to the reduction in misfit dislocation density. The recombination lifetime and interface recombination velocity (IRV) of CdTe/MgxCd1-xTe DHs are investigated. The IRV is found to be dependent on both the MgCdTe barrier height and width due to the thermionic emission and tunneling processes. A record-long carrier lifetime of 2.7 µs and a record-low IRV of close to zero have been confirmed experimentally.
The MgCdTe/Si tandem solar cell is proposed to address the issue of high manufacturing costs and poor performance of thin-film solar cells. The MBE grown MgxCd1-xTe/MgyCd1-yTe DHs have demonstrated the required bandgap energy of 1.7 eV, a carrier lifetime of 11 ns, and an effective IRV of (1.869 ± 0.007) × 103 cm/s. The large IRV is attributed to thermionic-emission induced interface recombination. These understandings can be applied to fabricating the high-efficiency low-cost MgCdTe/Si tandem solar cell.
ContributorsLiu, Shi (Author) / Zhang, Yong-Hang (Thesis advisor) / Johnson, Shane R (Committee member) / Vasileska, Dragica (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2015
Description
Structural health management (SHM) is emerging as a vital methodology to help engineers improve the safety and maintainability of critical structures. SHM systems are designed to reliably monitor and test the health and performance of structures in aerospace, civil, and mechanical engineering applications. SHM combines multidisciplinary technologies including sensing, signal processing, pattern recognition, data mining, high fidelity probabilistic progressive damage models, physics based damage models, and regression analysis. Due to the wide application of carbon fiber reinforced composites and their multiscale failure mechanisms, it is necessary to emphasize the research of SHM on composite structures. This research develops a comprehensive framework for the damage detection, localization, quantification, and prediction of the remaining useful life of complex composite structures. To interrogate a composite structure, guided wave propagation is applied to thin structures such as beams and plates. Piezoelectric transducers are selected because of their versatility, which allows them to be used as sensors and actuators. Feature extraction from guided wave signals is critical to demonstrate the presence of damage and estimate the damage locations. Advanced signal processing techniques are employed to extract robust features and information. To provide a better estimate of the damage for accurate life estimation, probabilistic regression analysis is used to obtain a prediction model for the prognosis of complex structures subject to fatigue loading. Special efforts have been applied to the extension of SHM techniques on aerospace and spacecraft structures, such as UAV composite wings and deployable composite boom structures. Necessary modifications of the developed SHM techniques were conducted to meet the unique requirements of the aerospace structures. The developed SHM algorithms are able to accurately detect and quantify impact damages as well as matrix cracking introduced.
ContributorsLiu, Yingtao (Author) / Chattopadhyay, Aditi (Thesis advisor) / Rajadas, John (Committee member) / Dai, Lenore (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2012
Description
When ferrite materials are used in antenna designs, they introduce some interesting and unique performance characteristics. One of the attractive features, for example, is the ability to reconfigure the center frequency of the antenna. In addition, ferrite materials also introduce a number of challenges in the modeling and simulation of such antennas. In order for the ferrite material to be useful in an antenna design, it usually is subjected to an external magnetic field. This field induces the internal magnetic field inside the ferrite material. The internal field plays a pivotal role in the radiation characteristics of the antenna. Thus, from the numerical point of view, accurate computation of this field is critical to the overall accuracy of the analysis. Usually the internal field is non-uniform and its computation is often a rather complex and non-trivial task. Therefore, to facilitate the modeling, simplifying assumptions, which introduce some kind of averaging, are often made. In this study, ferrite-loaded cavity-backed slot antennas are used to demonstrate that averaging procedures can lead to very unsatisfactory results. For instance, it is common practice to assume that the external field is uniform by averaging its distribution. One of the pivotal points in this study is the demonstration that the external magnetic field plays a very significant role and should be included in the modeling without averaging, if the accurate results are to be attained. Results presented in this study clearly support this argument. A procedure which avoids such averaging is presented and verified by comparing simulations with measurements. In contrast to the previous formulations, the modeling methodology developed in this dissertation leads to accurate results which compare very well with measurements for both uniform and non-uniform field distributions. The utility of this methodology is especially evident for the case when the magnetic field is severely non-uniform.
ContributorsKononov, Victor G (Author) / Balanis, Constantine A. (Thesis advisor) / Pan, George (Committee member) / Rajan, Subramaniam D. (Committee member) / Aberle, James T. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Research on developing new algorithms to improve information on brain functionality and structure is ongoing. Studying neural activity through dipole source localization with electroencephalography (EEG) and magnetoencephalography (MEG) sensor measurements can lead to diagnosis and treatment of a brain disorder and can also identify the area of the brain from where the disorder has originated. Designing advanced localization algorithms that can adapt to environmental changes is considered a significant shift from manual diagnosis which is based on the knowledge and observation of the doctor, to an adaptive and improved brain disorder diagnosis as these algorithms can track activities that might not be noticed by the human eye. An important consideration of these localization algorithms, however, is to try and minimize the overall power consumption in order to improve the study and treatment of brain disorders. This thesis considers the problem of estimating dynamic parameters of neural dipole sources while minimizing the system's overall power consumption; this is achieved by minimizing the number of EEG/MEG measurements sensors without a loss in estimation performance accuracy. As the EEG/MEG measurements models are related non-linearity to the dipole source locations and moments, these dynamic parameters can be estimated using sequential Monte Carlo methods such as particle filtering. Due to the large number of sensors required to record EEG/MEG Measurements for use in the particle filter, over long period recordings, a large amounts of power is required for storage and transmission. In order to reduce the overall power consumption, two methods are proposed. The first method used the predicted mean square estimation error as the performance metric under the constraint of a maximum power consumption. The performance metric of the second method uses the distance between the location of the sensors and the location estimate of the dipole source at the previous time step; this sensor scheduling scheme results in maximizing the overall signal-to-noise ratio. The performance of both methods is demonstrated using simulated data, and both methods show that they can provide good estimation results with significant reduction in the number of activated sensors at each time step.
ContributorsMichael, Stefanos (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Windows based mobile application for m-health and environmental monitoring sensor devices were developed and tested. With the number of smartphone users exponentially increasing, the applications developed for m-health and environmental monitoring devices are easy to reach the general public, if the applications are simple, user-friendly and personalized. The sensing device uses Bluetooth to communicate with the smartphone, providing mobility to the user. Since the device is small and hand-held, the user can put his smartphone in his pocket, connected to the device in his hand and can move anywhere with it. The data processing performed in the applications is verified against standard off the shelf software, the results of the tests are discussed in this document. The user-interface is very simple and doesn't require many inputs from the user other than during the initial setting when they have to enter their personal information for the records. The m-health application can be used by doctors as well as by patients. The response of the application is very quick and hence the patients need not wait for a long time to see the results. The environmental monitoring device has a real-time plot displayed on the screen of the smartphone showing concentrations of total volatile organic compounds and airborne particle count in the environment at the location of the device. The programming was done with Microsoft Visual Studio and was written on VB.NET platform. On the applications, the smartphone receives data as raw binary bytes from the device via Bluetooth and this data is processed to obtain the final result. The final result is the concentration of Nitric Oxide in ppb in the Asthma Analyzer device. In the environmental monitoring device, the final result is the concentration of total Volatile Organic Compounds and the count of airborne Particles.
ContributorsGanesan, Srisivapriya (Author) / Tao, Nongjian (Thesis advisor) / Zhang, Yanchao (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2012
Description
Increasing the conversion efficiencies of photovoltaic (PV) cells beyond the single junction theoretical limit is the driving force behind much of third generation solar cell research. Over the last half century, the experimental conversion efficiency of both single junction and tandem solar cells has plateaued as manufacturers and researchers have optimized various materials and structures. While existing materials and technologies have remarkably good conversion efficiencies, they are approaching their own limits. For example, tandem solar cells are currently well developed commercially but further improvements through increasing the number of junctions struggle with various issues related to material interfacial defects. Thus, there is a need for novel theoretical and experimental approaches leading to new third generation cell structures. Multiple exciton generation (MEG) and intermediate band (IB) solar cells have been proposed as third generation alternatives and theoretical modeling suggests they can surpass the detailed balance efficiency limits of single junction and tandem solar cells. MEG or IB solar cell has a variety of advantages enabling the use of low bandgap materials. Integrating MEG and IB with other cell types to make novel solar cells (such as MEG with tandem, IB with tandem or MEG with IB) potentially offers improvements by employing multi-physics effects in one device. This hybrid solar cell should improve the properties of conventional solar cells with a reduced number of junction, increased light-generated current and extended material selections. These multi-physics effects in hybrid solar cells can be achieved through the use of nanostructures taking advantage of the carrier confinement while using existing solar cell materials with excellent characteristics. This reduces the additional cost to develop novel materials and structures. In this dissertation, the author develops thermodynamic models for several novel types of solar cells and uses these models to optimize and compare their properties to those of existing PV cells. The results demonstrate multiple advantages from combining MEG and IB technology with existing solar cell structures.
ContributorsLee, Jongwon (Author) / Honsberg, C. (Christiana B.) (Thesis advisor) / Bowden, Stuart (Committee member) / Roedel, Ronald (Committee member) / Goodnick, Stephen (Committee member) / Schroder, Dieter (Committee member) / Arizona State University (Publisher)
Created2014
Description
Learning by trial-and-error requires retrospective information that whether a past action resulted in a rewarded outcome. Previous outcome in turn may provide information to guide future behavioral adjustment. But the specific contribution of this information to learning a task and the neural representations during the trial-and-error learning process is not well understood. In this dissertation, such learning is analyzed by means of single unit neural recordings in the rats' motor agranular medial (AGm) and agranular lateral (AGl) while the rats learned to perform a directional choice task. Multichannel chronic recordings using implanted microelectrodes in the rat's brain were essential to this study. Also for fundamental scientific investigations in general and for some applications such as brain machine interface, the recorded neural waveforms need to be analyzed first to identify neural action potentials as basic computing units. Prior to analyzing and modeling the recorded neural signals, this dissertation proposes an advanced spike sorting system, the M-Sorter, to extract the action potentials from raw neural waveforms. The M-Sorter shows better or comparable performance compared with two other popular spike sorters under automatic mode. With the sorted action potentials in place, neuronal activity in the AGm and AGl areas in rats during learning of a directional choice task is examined. Systematic analyses suggest that rat's neural activity in AGm and AGl was modulated by previous trial outcomes during learning. Single unit based neural dynamics during task learning are described in detail in the dissertation. Furthermore, the differences in neural modulation between fast and slow learning rats were compared. The results show that the level of neural modulation of previous trial outcome is different in fast and slow learning rats which may in turn suggest an important role of previous trial outcome encoding in learning.
ContributorsYuan, Yu'an (Author) / Si, Jennie (Thesis advisor) / Buneo, Christopher (Committee member) / Santello, Marco (Committee member) / Chae, Junseok (Committee member) / Arizona State University (Publisher)
Created2014
Description
Animals learn to choose a proper action among alternatives according to the circumstance. Through trial-and-error, animals improve their odds by making correct association between their behavioral choices and external stimuli. While there has been an extensive literature on the theory of learning, it is still unclear how individual neurons and a neural network adapt as learning progresses. In this dissertation, single units in the medial and lateral agranular (AGm and AGl) cortices were recorded as rats learned a directional choice task. The task required the rat to make a left/right side lever press if a light cue appeared on the left/right side of the interface panel. Behavior analysis showed that rat's movement parameters during performance of directional choices became stereotyped very quickly (2-3 days) while learning to solve the directional choice problem took weeks to occur. The entire learning process was further broken down to 3 stages, each having similar number of recording sessions (days). Single unit based firing rate analysis revealed that 1) directional rate modulation was observed in both cortices; 2) the averaged mean rate between left and right trials in the neural ensemble each day did not change significantly among the three learning stages; 3) the rate difference between left and right trials of the ensemble did not change significantly either. Besides, for either left or right trials, the trial-to-trial firing variability of single neurons did not change significantly over the three stages. To explore the spatiotemporal neural pattern of the recorded ensemble, support vector machines (SVMs) were constructed each day to decode the direction of choice in single trials. Improved classification accuracy indicated enhanced discriminability between neural patterns of left and right choices as learning progressed. When using a restricted Boltzmann machine (RBM) model to extract features from neural activity patterns, results further supported the idea that neural firing patterns adapted during the three learning stages to facilitate the neural codes of directional choices. Put together, these findings suggest a spatiotemporal neural coding scheme in a rat AGl and AGm neural ensemble that may be responsible for and contributing to learning the directional choice task.
ContributorsMao, Hongwei (Author) / Si, Jennie (Thesis advisor) / Buneo, Christopher (Committee member) / Cao, Yu (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2014