Matching Items (20)
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- All Subjects: engineering
- Genre: Doctoral Dissertation
- Creators: Goryll, Michael
- Creators: Papandreou-Suppappola, Antonia
Description
Tracking a time-varying number of targets is a challenging
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
ContributorsEbenezer, Samuel P (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2015
Description
This work considers the problem of multiple detection and tracking in two complex time-varying environments, urban terrain and underwater. Tracking multiple radar targets in urban environments is rst investigated by exploiting multipath signal returns, wideband underwater acoustic (UWA) communications channels are estimated using adaptive learning methods, and multiple UWA communications users are detected by designing the transmit signal to match the environment. For the urban environment, a multi-target tracking algorithm is proposed that integrates multipath-to-measurement association and the probability hypothesis density method implemented using particle filtering. The algorithm is designed to track an unknown time-varying number of targets by extracting information from multiple measurements due to multipath returns in the urban terrain. The path likelihood probability is calculated by considering associations between measurements and multipath returns, and an adaptive clustering algorithm is used to estimate the number of target and their corresponding parameters. The performance of the proposed algorithm is demonstrated for different multiple target scenarios and evaluated using the optimal subpattern assignment metric. The underwater environment provides a very challenging communication channel due to its highly time-varying nature, resulting in large distortions due to multipath and Doppler-scaling, and frequency-dependent path loss. A model-based wideband UWA channel estimation algorithm is first proposed to estimate the channel support and the wideband spreading function coefficients. A nonlinear frequency modulated signaling scheme is proposed that is matched to the wideband characteristics of the underwater environment. Constraints on the signal parameters are derived to optimally reduce multiple access interference and the UWA channel effects. The signaling scheme is compared to a code division multiple access (CDMA) scheme to demonstrate its improved bit error rate performance. The overall multi-user communication system performance is finally analyzed by first estimating the UWA channel and then designing the signaling scheme for multiple communications users.
ContributorsZhou, Meng (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Kovvali, Narayan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.
ContributorsRicher, Joshua Amos (Author) / Johnston, Stephen A. (Thesis advisor) / Woodbury, Neal (Committee member) / Stafford, Phillip (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2014
Description
Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.
ContributorsKrishnamoorthi, Harish (Author) / Spanias, Andreas (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
Polymer and polymer matrix composites (PMCs) materials are being used extensively in different civil and mechanical engineering applications. The behavior of the epoxy resin polymers under different types of loading conditions has to be understood before the mechanical behavior of Polymer Matrix Composites (PMCs) can be accurately predicted. In many structural applications, PMC structures are subjected to large flexural loadings, examples include repair of structures against earthquake and engine fan cases. Therefore it is important to characterize and model the flexural mechanical behavior of epoxy resin materials. In this thesis, a comprehensive research effort was undertaken combining experiments and theoretical modeling to investigate the mechanical behavior of epoxy resins subject to different loading conditions. Epoxy resin E 863 was tested at different strain rates. Samples with dog-bone geometry were used in the tension tests. Small sized cubic, prismatic, and cylindrical samples were used in compression tests. Flexural tests were conducted on samples with different sizes and loading conditions. Strains were measured using the digital image correlation (DIC) technique, extensometers, strain gauges, and actuators. Effects of triaxiality state of stress were studied. Cubic, prismatic, and cylindrical compression samples undergo stress drop at yield, but it was found that only cubic samples experience strain hardening before failure. Characteristic points of tensile and compressive stress strain relation and load deflection curve in flexure were measured and their variations with strain rate studied. Two different stress strain models were used to investigate the effect of out-of-plane loading on the uniaxial stress strain response of the epoxy resin material. The first model is a strain softening with plastic flow for tension and compression. The influence of softening localization on material behavior was investigated using the DIC system. It was found that compression plastic flow has negligible influence on flexural behavior in epoxy resins, which are stronger in pre-peak and post-peak softening in compression than in tension. The second model was a piecewise-linear stress strain curve simplified in the post-peak response. Beams and plates with different boundary conditions were tested and analytically studied. The flexural over-strength factor for epoxy resin polymeric materials were also evaluated.
ContributorsYekani Fard, Masoud (Author) / Chattopadhyay, Aditi (Thesis advisor) / Dai, Lenore (Committee member) / Li, Jian (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genomic and proteomic sequences, which are in the form of deoxyribonucleic acid (DNA) and amino acids respectively, play a vital role in the structure, function and diversity of every living cell. As a result, various genomic and proteomic sequence processing methods have been proposed from diverse disciplines, including biology, chemistry, physics, computer science and electrical engineering. In particular, signal processing techniques were applied to the problems of sequence querying and alignment, that compare and classify regions of similarity in the sequences based on their composition. However, although current approaches obtain results that can be attributed to key biological properties, they require pre-processing and lack robustness to sequence repetitions. In addition, these approaches do not provide much support for efficiently querying sub-sequences, a process that is essential for tracking localized database matches. In this work, a query-based alignment method for biological sequences that maps sequences to time-domain waveforms before processing the waveforms for alignment in the time-frequency plane is first proposed. The mapping uses waveforms, such as time-domain Gaussian functions, with unique sequence representations in the time-frequency plane. The proposed alignment method employs a robust querying algorithm that utilizes a time-frequency signal expansion whose basis function is matched to the basic waveform in the mapped sequences. The resulting WAVEQuery approach is demonstrated for both DNA and protein sequences using the matching pursuit decomposition as the signal basis expansion. The alignment localization of WAVEQuery is specifically evaluated over repetitive database segments, and operable in real-time without pre-processing. It is demonstrated that WAVEQuery significantly outperforms the biological sequence alignment method BLAST for queries with repetitive segments for DNA sequences. A generalized version of the WAVEQuery approach with the metaplectic transform is also described for protein sequence structure prediction. For protein alignment, it is often necessary to not only compare the one-dimensional (1-D) primary sequence structure but also the secondary and tertiary three-dimensional (3-D) space structures. This is done after considering the conformations in the 3-D space due to the degrees of freedom of these structures. As a result, a novel directionality based 3-D waveform mapping for the 3-D protein structures is also proposed and it is used to compare protein structures using a matched filter approach. By incorporating a 3-D time axis, a highly-localized Gaussian-windowed chirp waveform is defined, and the amino acid information is mapped to the chirp parameters that are then directly used to obtain directionality in the 3-D space. This mapping is unique in that additional characteristic protein information such as hydrophobicity, that relates the sequence with the structure, can be added as another representation parameter. The additional parameter helps tracking similarities over local segments of the structure, this enabling classification of distantly related proteins which have partial structural similarities. This approach is successfully tested for pairwise alignments over full length structures, alignments over multiple structures to form a phylogenetic trees, and also alignments over local segments. Also, basic classification over protein structural classes using directional descriptors for the protein structure is performed.
ContributorsRavichandran, Lakshminarayan (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Spanias, Andreas S (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created2011
Description
Biosensors aiming at detection of target analytes, such as proteins, microbes, virus, and toxins, are widely needed for various applications including detection of chemical and biological warfare (CBW) agents, biomedicine, environmental monitoring, and drug screening. Surface Plasmon Resonance (SPR), as a surface-sensitive analytical tool, can very sensitively respond to minute changes of refractive index occurring adjacent to a metal film, offering detection limits up to a few ppt (pg/mL). Through SPR, the process of protein adsorption may be monitored in real-time, and transduced into an SPR angle shift. This unique technique bypasses the time-consuming, labor-intensive labeling processes, such as radioisotope and fluorescence labeling. More importantly, the method avoids the modification of the biomarker’s characteristics and behaviors by labeling that often occurs in traditional biosensors. While many transducers, including SPR, offer high sensitivity, selectivity is determined by the bio-receptors. In traditional biosensors, the selectivity is provided by bio-receptors possessing highly specific binding affinity to capture target analytes, yet their use in biosensors are often limited by their relatively-weak binding affinity with analyte, non-specific adsorption, need for optimization conditions, low reproducibility, and difficulties integrating onto the surface of transducers. In order to circumvent the use of bio-receptors, the competitive adsorption of proteins, termed the Vroman effect, is utilized in this work. The Vroman effect was first reported by Vroman and Adams in 1969. The competitive adsorption targeted here occurs among different proteins competing to adsorb to a surface, when more than one type of protein is present. When lower-affinity proteins are adsorbed on the surface first, they can be displaced by higher-affinity proteins arriving at the surface at a later point in time. Moreover, only low-affinity proteins can be displaced by high-affinity proteins, typically possessing higher molecular weight, yet the reverse sequence does not occur. The SPR biosensor based on competitive adsorption is successfully demonstrated to detect fibrinogen and thyroglobulin (Tg) in undiluted human serum and copper ions in drinking water through the denatured albumin.
ContributorsWang, Ran (Author) / Chae, Junseok (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Tsow, Tsing (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2015
Description
Total dose sensing systems (or radiation detection systems) have many applications,
ranging from survey monitors used to supervise the generated radioactive waste at
nuclear power plants to personal dosimeters which measure the radiation dose
accumulated in individuals. This dissertation work will present two different types of
novel devices developed at Arizona State University for total dose sensing applications.
The first detector technology is a mechanically flexible metal-chalcogenide glass (ChG)
based system which is fabricated on low cost substrates and are intended as disposable
total dose sensors. Compared to existing commercial technologies, these thin film
radiation sensors are simpler in form and function, and cheaper to produce and operate.
The sensors measure dose through resistance change and are suitable for applications
such as reactor dosimetry, radiation chemistry, and clinical dosimetry. They are ideal for
wearable devices due to the lightweight construction, inherent robustness to resist
breaking when mechanically stressed, and ability to attach to non-flat objects. Moreover,
their performance can be easily controlled by tuning design variables and changing
incorporated materials. The second detector technology is a wireless dosimeter intended
for remote total dose sensing. They are based on a capacitively loaded folded patch
antenna resonating in the range of 3 GHz to 8 GHz for which the load capacitance varies
as a function of total dose. The dosimeter does not need power to operate thus enabling
its use and implementation in the field without requiring a battery for its read-out. As a
result, the dosimeter is suitable for applications such as unattended detection systems
destined for covert monitoring of merchandise crossing borders, where nuclear material
tracking is a concern. The sensitive element can be any device exhibiting a known
variation of capacitance with total ionizing dose. The sensitivity of the dosimeter is
related to the capacitance variation of the radiation sensitive device as well as the high
frequency system used for reading. Both technologies come with the advantage that they
are easy to manufacture with reasonably low cost and sensing can be readily read-out.
ranging from survey monitors used to supervise the generated radioactive waste at
nuclear power plants to personal dosimeters which measure the radiation dose
accumulated in individuals. This dissertation work will present two different types of
novel devices developed at Arizona State University for total dose sensing applications.
The first detector technology is a mechanically flexible metal-chalcogenide glass (ChG)
based system which is fabricated on low cost substrates and are intended as disposable
total dose sensors. Compared to existing commercial technologies, these thin film
radiation sensors are simpler in form and function, and cheaper to produce and operate.
The sensors measure dose through resistance change and are suitable for applications
such as reactor dosimetry, radiation chemistry, and clinical dosimetry. They are ideal for
wearable devices due to the lightweight construction, inherent robustness to resist
breaking when mechanically stressed, and ability to attach to non-flat objects. Moreover,
their performance can be easily controlled by tuning design variables and changing
incorporated materials. The second detector technology is a wireless dosimeter intended
for remote total dose sensing. They are based on a capacitively loaded folded patch
antenna resonating in the range of 3 GHz to 8 GHz for which the load capacitance varies
as a function of total dose. The dosimeter does not need power to operate thus enabling
its use and implementation in the field without requiring a battery for its read-out. As a
result, the dosimeter is suitable for applications such as unattended detection systems
destined for covert monitoring of merchandise crossing borders, where nuclear material
tracking is a concern. The sensitive element can be any device exhibiting a known
variation of capacitance with total ionizing dose. The sensitivity of the dosimeter is
related to the capacitance variation of the radiation sensitive device as well as the high
frequency system used for reading. Both technologies come with the advantage that they
are easy to manufacture with reasonably low cost and sensing can be readily read-out.
ContributorsMahmud, Adnan, Ph.D (Author) / Barnaby, Hugh J. (Thesis advisor) / Kozicki, Michael N (Committee member) / Gonzalez-Velo, Yago (Committee member) / Goryll, Michael (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2017
Description
Over the past several decades, there has been a growing interest in the use of fluorescent probes in low-cost diagnostic devices for resource-limited environments. This dissertation details the design, development, and deployment of an inexpensive, multiplexed, and quantitative, fluorescence-based lateral flow immunoassay platform, in light of the specific constraints associated with resource-limited settings.
This effort grew out of the need to develop a highly sensitive, field-deployable platform to be used as a primary screening and early detection tool for serologic biomarkers for the high-risk human papillomavirus (hrHPV) infection. A hrHPV infection is a precursor for developing high-grade cervical intraepithelial neoplasia (CIN 2/3+). Early detection requires high sensitivity and a low limit-of-detection (LOD). To this end, the developed platform (DxArray) takes advantage of the specificity of immunoassays and the selectivity of fluorescence for early disease detection. The long term goal is to improve the quality of life for several hundred million women globally, at risk of being infected with hrHPV.
The developed platform uses fluorescent labels over the gold-standard colorimetric labels in a compact, high-sensitivity lateral flow assay configuration. It is also compatible with POC settings as it substitutes expensive and bulky light sources for LEDs, low-light CMOS cameras, and photomultiplier tubes for photodiodes, in a transillumination architecture, and eliminates the need for expensive focusing/transfer optics. The platform uses high-quality interference filters at less than $1 each, enabling a rugged and robust design suitable for field use.
The limit of detection (LOD) of the developed platform is within an order of magnitude of centralized laboratory diagnostic instruments. It enhances the LOD of absorbance or reflectometric and visual readout lateral flow assays by 2 - 3 orders of magnitude. This system could be applied toward any chemical or bioanalytical procedure that requires a high performance at low-cost.
The knowledge and techniques developed in this effort is relevant to the community of researchers and industry developers looking to deploy inexpensive, quantitative, and highly sensitive diagnostic devices to resource-limited settings.
This effort grew out of the need to develop a highly sensitive, field-deployable platform to be used as a primary screening and early detection tool for serologic biomarkers for the high-risk human papillomavirus (hrHPV) infection. A hrHPV infection is a precursor for developing high-grade cervical intraepithelial neoplasia (CIN 2/3+). Early detection requires high sensitivity and a low limit-of-detection (LOD). To this end, the developed platform (DxArray) takes advantage of the specificity of immunoassays and the selectivity of fluorescence for early disease detection. The long term goal is to improve the quality of life for several hundred million women globally, at risk of being infected with hrHPV.
The developed platform uses fluorescent labels over the gold-standard colorimetric labels in a compact, high-sensitivity lateral flow assay configuration. It is also compatible with POC settings as it substitutes expensive and bulky light sources for LEDs, low-light CMOS cameras, and photomultiplier tubes for photodiodes, in a transillumination architecture, and eliminates the need for expensive focusing/transfer optics. The platform uses high-quality interference filters at less than $1 each, enabling a rugged and robust design suitable for field use.
The limit of detection (LOD) of the developed platform is within an order of magnitude of centralized laboratory diagnostic instruments. It enhances the LOD of absorbance or reflectometric and visual readout lateral flow assays by 2 - 3 orders of magnitude. This system could be applied toward any chemical or bioanalytical procedure that requires a high performance at low-cost.
The knowledge and techniques developed in this effort is relevant to the community of researchers and industry developers looking to deploy inexpensive, quantitative, and highly sensitive diagnostic devices to resource-limited settings.
ContributorsObahiagbon, Uwadiae (Author) / Blain Christen, Jennifer M (Thesis advisor) / Anderson, Karen S (Committee member) / Goryll, Michael (Committee member) / Smith, Barbara S. (Committee member) / Arizona State University (Publisher)
Created2018
Description
To date, the most popular and dominant material for commercial solar cells is
crystalline silicon (or wafer-Si). It has the highest cell efficiency and cell lifetime out
of all commercial solar cells. Although the potential of crystalline-Si solar cells in
supplying energy demands is enormous, their future growth will likely be constrained
by two major bottlenecks. The first is the high electricity input to produce
crystalline-Si solar cells and modules, and the second is the limited supply of silver
(Ag) reserves. These bottlenecks prevent crystalline-Si solar cells from reaching
terawatt-scale deployment, which means the electricity produced by crystalline-Si
solar cells would never fulfill a noticeable portion of our energy demands in the future.
In order to solve the issue of Ag limitation for the front metal grid, aluminum (Al)
electroplating has been developed as an alternative metallization technique in the
fabrication of crystalline-Si solar cells. The plating is carried out in a
near-room-temperature ionic liquid by means of galvanostatic electrolysis. It has been
found that dense, adherent Al deposits with resistivity in the high 10^–6 ohm-cm range
can be reproducibly obtained directly on Si substrates and nickel seed layers. An
all-Al Si solar cell, with an electroplated Al front electrode and a screen-printed Al
back electrode, has been successfully demonstrated based on commercial p-type
monocrystalline-Si solar cells, and its efficiency is approaching 15%. Further
optimization of the cell fabrication process, in particular a suitable patterning
technique for the front silicon nitride layer, is expected to increase the efficiency of
the cell to ~18%. This shows the potential of Al electroplating in cell metallization is
promising and replacing Ag with Al as the front finger electrode is feasible.
crystalline silicon (or wafer-Si). It has the highest cell efficiency and cell lifetime out
of all commercial solar cells. Although the potential of crystalline-Si solar cells in
supplying energy demands is enormous, their future growth will likely be constrained
by two major bottlenecks. The first is the high electricity input to produce
crystalline-Si solar cells and modules, and the second is the limited supply of silver
(Ag) reserves. These bottlenecks prevent crystalline-Si solar cells from reaching
terawatt-scale deployment, which means the electricity produced by crystalline-Si
solar cells would never fulfill a noticeable portion of our energy demands in the future.
In order to solve the issue of Ag limitation for the front metal grid, aluminum (Al)
electroplating has been developed as an alternative metallization technique in the
fabrication of crystalline-Si solar cells. The plating is carried out in a
near-room-temperature ionic liquid by means of galvanostatic electrolysis. It has been
found that dense, adherent Al deposits with resistivity in the high 10^–6 ohm-cm range
can be reproducibly obtained directly on Si substrates and nickel seed layers. An
all-Al Si solar cell, with an electroplated Al front electrode and a screen-printed Al
back electrode, has been successfully demonstrated based on commercial p-type
monocrystalline-Si solar cells, and its efficiency is approaching 15%. Further
optimization of the cell fabrication process, in particular a suitable patterning
technique for the front silicon nitride layer, is expected to increase the efficiency of
the cell to ~18%. This shows the potential of Al electroplating in cell metallization is
promising and replacing Ag with Al as the front finger electrode is feasible.
ContributorsSun, Wen-Cheng (Author) / Tao, Meng (Thesis advisor) / Vasileska, Dragica (Committee member) / Yu, Hongbin (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2016