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- Genre: Academic theses
- Creators: Liu, Huan
- Creators: Yu, Hongbin
- Resource Type: Text
- Status: Published
Description
Inductors are fundamental components that do not scale well. Their physical limitations to scalability along with their inherent losses make them the main obstacle in achieving monolithic system-on-chip platform (SoCP). For past decades researchers focused on integrating magnetic materials into on-chip inductors in the quest of achieving high inductance density and quality factor (QF). The state of the art on-chip inductor is made of an enclosed magnetic thin-film around the current carrying wire for maximum flux amplification. Though the integration of magnetic materials results in enhanced inductor characteristics, this approach has its own challenges and limitations especially in power applications. The current-induced magnetic field (HDC) drives the magnetic film into its saturation state. At saturation, inductance and QF drop to that of air-core inductors, eliminating the benefits of integrating magnetic materials. Increasing the current carrying capability without substantially sacrificing benefits brought on by the magnetic material is an open challenge in power applications. Researchers continue to address this challenge along with the continuous improvement in inductance and QF for RF and power applications.
In this work on-chip inductors incorporating magnetic Co-4%Zr-4%Ta -8%B thin films were fabricated and their characteristics were examined under the influence of an externally applied DC magnetic field. It is well established that spins in magnetic materials tend to align themselves in the same direction as the applied field. The resistance of the inductor resulting from the ferromagnetic film can be changed by manipulating the orientation of magnetization. A reduction in resistance should lead to decreases in losses and an enhancement in the QF. The effect of externally applied DC magnetic field along the easy and hard axes was thoroughly investigated. Depending on the strength and orientation of the externally applied field significant improvements in QF response were gained at the expense of a relative reduction in inductance. Characteristics of magnetic-based inductors degrade with current-induced stress. It was found that applying an externally low DC magnetic field across the on-chip inductor prevents the degradation in inductance and QF responses. Examining the effect of DC magnetic field on current carrying capability under low temperature is suggested.
In this work on-chip inductors incorporating magnetic Co-4%Zr-4%Ta -8%B thin films were fabricated and their characteristics were examined under the influence of an externally applied DC magnetic field. It is well established that spins in magnetic materials tend to align themselves in the same direction as the applied field. The resistance of the inductor resulting from the ferromagnetic film can be changed by manipulating the orientation of magnetization. A reduction in resistance should lead to decreases in losses and an enhancement in the QF. The effect of externally applied DC magnetic field along the easy and hard axes was thoroughly investigated. Depending on the strength and orientation of the externally applied field significant improvements in QF response were gained at the expense of a relative reduction in inductance. Characteristics of magnetic-based inductors degrade with current-induced stress. It was found that applying an externally low DC magnetic field across the on-chip inductor prevents the degradation in inductance and QF responses. Examining the effect of DC magnetic field on current carrying capability under low temperature is suggested.
ContributorsKhdour, Mahmoud (Author) / Yu, Hongbin (Thesis advisor) / Pan, George (Committee member) / Goryll, Michael (Committee member) / Bearat, Hamdallah (Committee member) / Arizona State University (Publisher)
Created2014
Description
A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention.
As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications?
The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media.
As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications?
The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media.
ContributorsTang, Jiliang (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Aggarwal, Charu (Committee member) / Arizona State University (Publisher)
Created2015
Description
US Senate is the venue of political debates where the federal bills are formed and voted. Senators show their support/opposition along the bills with their votes. This information makes it possible to extract the polarity of the senators. Similarly, blogosphere plays an increasingly important role as a forum for public debate. Authors display sentiment toward issues, organizations or people using a natural language.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
ContributorsGokalp, Sedat (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Liu, Huan (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2015
Description
Recent efforts in data cleaning have focused mostly on problems like data deduplication, record matching, and data standardization; few of these focus on fixing incorrect attribute values in tuples. Correcting values in tuples is typically performed by a minimum cost repair of tuples that violate static constraints like CFDs (which have to be provided by domain experts, or learned from a clean sample of the database). In this thesis, I provide a method for correcting individual attribute values in a structured database using a Bayesian generative model and a statistical error model learned from the noisy database directly. I thus avoid the necessity for a domain expert or master data. I also show how to efficiently perform consistent query answering using this model over a dirty database, in case write permissions to the database are unavailable. A Map-Reduce architecture to perform this computation in a distributed manner is also shown. I evaluate these methods over both synthetic and real data.
ContributorsDe, Sushovan (Author) / Kambhampati, Subbarao (Thesis advisor) / Chen, Yi (Committee member) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
As robotic technology and its various uses grow steadily more complex and ubiquitous, humans are coming into increasing contact with robotic agents. A large portion of such contact is cooperative interaction, where both humans and robots are required to work on the same application towards achieving common goals. These application scenarios are characterized by a need to leverage the strengths of each agent as part of a unified team to reach those common goals. To ensure that the robotic agent is truly a contributing team-member, it must exhibit some degree of autonomy in achieving goals that have been delegated to it. Indeed, a significant portion of the utility of such human-robot teams derives from the delegation of goals to the robot, and autonomy on the part of the robot in achieving those goals. In order to be considered truly autonomous, the robot must be able to make its own plans to achieve the goals assigned to it, with only minimal direction and assistance from the human.
Automated planning provides the solution to this problem -- indeed, one of the main motivations that underpinned the beginnings of the field of automated planning was to provide planning support for Shakey the robot with the STRIPS system. For long, however, automated planners suffered from scalability issues that precluded their application to real world, real time robotic systems. Recent decades have seen a gradual abeyance of those issues, and fast planning systems are now the norm rather than the exception. However, some of these advances in speedup and scalability have been achieved by ignoring or abstracting out challenges that real world integrated robotic systems must confront.
In this work, the problem of planning for human-hobot teaming is introduced. The central idea -- the use of automated planning systems as mediators in such human-robot teaming scenarios -- and the main challenges inspired from real world scenarios that must be addressed in order to make such planning seamless are presented: (i) Goals which can be specified or changed at execution time, after the planning process has completed; (ii) Worlds and scenarios where the state changes dynamically while a previous plan is executing; (iii) Models that are incomplete and can be changed during execution; and (iv) Information about the human agent's plan and intentions that can be used for coordination. These challenges are compounded by the fact that the human-robot team must execute in an open world, rife with dynamic events and other agents; and in a manner that encourages the exchange of information between the human and the robot. As an answer to these challenges, implemented solutions and a fielded prototype that combines all of those solutions into one planning system are discussed. Results from running this prototype in real world scenarios are presented, and extensions to some of the solutions are offered as appropriate.
Automated planning provides the solution to this problem -- indeed, one of the main motivations that underpinned the beginnings of the field of automated planning was to provide planning support for Shakey the robot with the STRIPS system. For long, however, automated planners suffered from scalability issues that precluded their application to real world, real time robotic systems. Recent decades have seen a gradual abeyance of those issues, and fast planning systems are now the norm rather than the exception. However, some of these advances in speedup and scalability have been achieved by ignoring or abstracting out challenges that real world integrated robotic systems must confront.
In this work, the problem of planning for human-hobot teaming is introduced. The central idea -- the use of automated planning systems as mediators in such human-robot teaming scenarios -- and the main challenges inspired from real world scenarios that must be addressed in order to make such planning seamless are presented: (i) Goals which can be specified or changed at execution time, after the planning process has completed; (ii) Worlds and scenarios where the state changes dynamically while a previous plan is executing; (iii) Models that are incomplete and can be changed during execution; and (iv) Information about the human agent's plan and intentions that can be used for coordination. These challenges are compounded by the fact that the human-robot team must execute in an open world, rife with dynamic events and other agents; and in a manner that encourages the exchange of information between the human and the robot. As an answer to these challenges, implemented solutions and a fielded prototype that combines all of those solutions into one planning system are discussed. Results from running this prototype in real world scenarios are presented, and extensions to some of the solutions are offered as appropriate.
ContributorsTalamadupula, Kartik (Author) / Kambhampati, Subbarao (Thesis advisor) / Baral, Chitta (Committee member) / Liu, Huan (Committee member) / Scheutz, Matthias (Committee member) / Smith, David E. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Group III-nitride semiconductors have been commercially used in the fabrication of light-emitting diodes and laser diodes, covering the ultraviolet-visible-infrared spectral range and exhibit unique properties suitable for modern optoelectronic applications. InGaN ternary alloys have energy band gaps ranging from 0.7 to 3.4 eV. It has a great potential in the application for high efficient solar cells. AlGaN ternary alloys have energy band gaps ranging from 3.4 to 6.2 eV. These alloys have a great potential in the application of deep ultra violet laser diodes. However, there are still many issues with these materials that remain to be solved. In this dissertation, several issues concerning structural, electronic, and optical properties of III-nitrides have been investigated using transmission electron microscopy. First, the microstructure of InxGa1-xN (x = 0.22, 0.46, 0.60, and 0.67) films grown by metal-modulated epitaxy on GaN buffer /sapphire substrates is studied. The effect of indium composition on the structure of InGaN films and strain relaxation is carefully analyzed. High luminescence intensity, low defect density, and uniform full misfit strain relaxation are observed for x = 0.67. Second, the properties of high-indium-content InGaN thin films using a new molecular beam epitaxy method have been studied for applications in solar cell technologies. This method uses a high quality AlN buffer with large lattice mismatch that results in a critical thickness below one lattice parameter. Finally, the effect of different substrates and number of gallium sources on the microstructure of AlGaN-based deep ultraviolet laser has been studied. It is found that defects in epitaxial layer are greatly reduced when the structure is deposited on a single crystal AlN substrate. Two gallium sources in the growth of multiple quantum wells active region are found to cause a significant improvement in the quality of quantum well structures.
ContributorsWei, Yong (Author) / Ponce, Fernando (Thesis advisor) / Chizmeshya, Andrew (Committee member) / McCartney, Martha (Committee member) / Menéndez, Jose (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Writing instruction poses both cognitive and affective challenges, particularly for adolescents. American teens not only fall short of national writing standards, but also tend to lack motivation for school writing, claiming it is too challenging and that they have nothing interesting to write about. Yet, teens enthusiastically immerse themselves in informal writing via text messaging, email, and social media, regularly sharing their thoughts and experiences with a real audience. While these activities are, in fact, writing, research indicates that teens instead view them as simply "communication" or "being social." Accordingly, the aim of this work was to infuse formal classroom writing with naturally engaging elements of informal social media writing to positively impact writing quality and the motivation to write, resulting in the development and implementation of Sparkfolio, an online prewriting tool that: a) addresses affective challenges by allowing students to choose personally relevant topics using their own social media data; and b) provides cognitive support with a planner that helps develop and organize ideas in preparation for writing a first draft. This tool was evaluated in a study involving 46 eleventh-grade English students writing three personal narratives each, and including three experimental conditions: a) using self-authored social media post data while planning with Sparkfolio; b) using only data from posts authored by one's friends while planning with Sparkfolio; and c) a control group that did not use Sparkfolio. The dependent variables were the change in writing motivation and the change in writing quality that occurred before and after the intervention. A scaled pre/posttest measured writing motivation, and the first and third narratives were used as writing quality pre/posttests. A usability scale, logged Sparkfolio data, and qualitative measures were also analyzed. Results indicated that participants who used Sparkfolio had statistically significantly higher gains in writing quality than the control group, validating Sparkfolio as effective. Additionally, while nonsignificant, results suggested that planning with self-authored data provided more writing quality and motivational benefits than data authored by others. This work provides initial empirical evidence that leveraging students' own social media data (securely) holds potential in fostering meaningful personalized learning.
ContributorsSadauskas, John (Author) / Atkinson, Robert K (Thesis advisor) / Savenye, Wilhelmina (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing.
Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects.
Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation.
Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects.
Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation.
ContributorsGao, Huiji (Author) / Liu, Huan (Thesis advisor) / Xue, Guoliang (Committee member) / Ye, Jieping (Committee member) / Caverlee, James (Committee member) / Arizona State University (Publisher)
Created2014
Description
Sarcasm is a nuanced form of language where usually, the speaker explicitly states the opposite of what is implied. Imbued with intentional ambiguity and subtlety, detecting sarcasm is a difficult task, even for humans. Current works approach this challenging problem primarily from a linguistic perspective, focusing on the lexical and syntactic aspects of sarcasm. In this thesis, I explore the possibility of using behavior traits intrinsic to users of sarcasm to detect sarcastic tweets. First, I theorize the core forms of sarcasm using findings from the psychological and behavioral sciences, and some observations on Twitter users. Then, I develop computational features to model the manifestations of these forms of sarcasm using the user's profile information and tweets. Finally, I combine these features to train a supervised learning model to detect sarcastic tweets. I perform experiments to extensively evaluate the proposed behavior modeling approach and compare with the state-of-the-art.
ContributorsRajadesingan, Ashwin (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Pon-Barry, Heather (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation aims to demonstrate a new approach to fabricating solar cells for spectrum-splitting photovoltaic systems with the potential to reduce their cost and complexity of manufacturing, called Monolithically Integrated Laterally Arrayed Multiple Band gap (MILAMB) solar cells. Single crystal semiconductor alloy nanowire (NW) ensembles are grown with the alloy composition and band gap changing continuously across a broad range over the surface of a single substrate in a single, inexpensive growth step by the Dual-Gradient Method. The nanowire ensembles then serve as the absorbing materials in a set of solar cells for spectrum-splitting photovoltaic systems.
Preliminary design and simulation studies based on Anderson's model band line-ups were undertaken for CdPbS and InGaN alloys. Systems of six subcells obtained efficiencies in the 32-38% range for CdPbS and 34-40% for InGaN at 1-240 suns, though both materials systems require significant development before these results could be achieved experimentally. For an experimental demonstration, CdSSe was selected due to its availability. Proof-of-concept CdSSe nanowire ensemble solar cells with two subcells were fabricated simultaneously on one substrate. I-V characterization under 1 sun AM1.5G conditions yielded open-circuit voltages (Voc) up to 307 and 173 mV and short-circuit current densities (Jsc) up to 0.091 and 0.974 mA/cm2 for the CdS- and CdSe-rich cells, respectively. Similar thin film cells were also fabricated for comparison. The nanowire cells showed substantially higher Voc than the film cells, which was attributed to higher material quality in the CdSSe absorber. I-V measurements were also conducted with optical filters to simulate a simple form of spectrum-splitting. The CdS-rich cells showed uniformly higher Voc and fill factor (FF) than the CdSe-rich cells, as expected due to their larger band gaps. This suggested higher power density was produced by the CdS-rich cells on the single-nanowire level, which is the principal benefit of spectrum-splitting. These results constitute a proof-of-concept experimental demonstration of the MILAMB approach to fabricating multiple cells for spectrum-splitting photovoltaics. Future systems based on this approach could help to reduce the cost and complexity of manufacturing spectrum-splitting photovoltaic systems and offer a low cost alternative to multi-junction tandems for achieving high efficiencies.
Preliminary design and simulation studies based on Anderson's model band line-ups were undertaken for CdPbS and InGaN alloys. Systems of six subcells obtained efficiencies in the 32-38% range for CdPbS and 34-40% for InGaN at 1-240 suns, though both materials systems require significant development before these results could be achieved experimentally. For an experimental demonstration, CdSSe was selected due to its availability. Proof-of-concept CdSSe nanowire ensemble solar cells with two subcells were fabricated simultaneously on one substrate. I-V characterization under 1 sun AM1.5G conditions yielded open-circuit voltages (Voc) up to 307 and 173 mV and short-circuit current densities (Jsc) up to 0.091 and 0.974 mA/cm2 for the CdS- and CdSe-rich cells, respectively. Similar thin film cells were also fabricated for comparison. The nanowire cells showed substantially higher Voc than the film cells, which was attributed to higher material quality in the CdSSe absorber. I-V measurements were also conducted with optical filters to simulate a simple form of spectrum-splitting. The CdS-rich cells showed uniformly higher Voc and fill factor (FF) than the CdSe-rich cells, as expected due to their larger band gaps. This suggested higher power density was produced by the CdS-rich cells on the single-nanowire level, which is the principal benefit of spectrum-splitting. These results constitute a proof-of-concept experimental demonstration of the MILAMB approach to fabricating multiple cells for spectrum-splitting photovoltaics. Future systems based on this approach could help to reduce the cost and complexity of manufacturing spectrum-splitting photovoltaic systems and offer a low cost alternative to multi-junction tandems for achieving high efficiencies.
ContributorsCaselli, Derek (Author) / Ning, Cun-Zheng (Thesis advisor) / Tao, Meng (Committee member) / Yu, Hongbin (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2014