Matching Items (71)
Filtering by
- Status: Published
Description
In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.
ContributorsRai, Samita (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Borges, Chad (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2012
Description
The development of stab-resistant Kevlar armor has been an ongoing field of research
since the late 1990s, with the ultimate goal of improving the multi-threat capabilities of
traditional soft-body armor while significantly improving its protective efficiency - the amount
of layers of armor material required to defeat threats. To create a novel, superior materials
system to reinforce Kevlar armor for the Norica Capstone project, this thesis set out to
synthesize, recover, and characterize zinc oxide nanowire colloids.
The materials synthesized were successfully utilized in the wider Capstone effort to
dramatically enhance the protective abilities of Kevlar, while the data obtained on the 14
hydrothermal synthesis attempts and numerous challenges at recovery provided critical
information on the synthesis parameters involved in the reliable, scalable mass production of the
nanomaterial additive. Additionally, recovery was unconventionally facilitated in the absence of
a vacuum filtration apparatus with nanoscale filters by intentionally inducing electrostatic
agglomeration of the nanowires during standard gravity filtration. The subsequent application of
these nanowires constituted a pioneering use in the production of nanowire-reinforced
STF-based Kevlar coatings, and support the future development and, ultimately, the
commercialization of lighter and more-protective soft armor systems.
since the late 1990s, with the ultimate goal of improving the multi-threat capabilities of
traditional soft-body armor while significantly improving its protective efficiency - the amount
of layers of armor material required to defeat threats. To create a novel, superior materials
system to reinforce Kevlar armor for the Norica Capstone project, this thesis set out to
synthesize, recover, and characterize zinc oxide nanowire colloids.
The materials synthesized were successfully utilized in the wider Capstone effort to
dramatically enhance the protective abilities of Kevlar, while the data obtained on the 14
hydrothermal synthesis attempts and numerous challenges at recovery provided critical
information on the synthesis parameters involved in the reliable, scalable mass production of the
nanomaterial additive. Additionally, recovery was unconventionally facilitated in the absence of
a vacuum filtration apparatus with nanoscale filters by intentionally inducing electrostatic
agglomeration of the nanowires during standard gravity filtration. The subsequent application of
these nanowires constituted a pioneering use in the production of nanowire-reinforced
STF-based Kevlar coatings, and support the future development and, ultimately, the
commercialization of lighter and more-protective soft armor systems.
ContributorsDurso, Michael Nathan (Author) / Tongay, Sefaattin (Thesis director) / Zhuang, Houlong (Committee member) / Materials Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
With renewable energy on the rise, researchers have turned their funding and their focus towards new solar cell technologies, and perovskites are a major source of interest. This class of materials is particularly interesting due to their quick, simple synthesis as well as their physical and electrical superiority when compared to current silicon-based solar cells. Through this thesis, we will explore the synthesis of various types of perovskites and their subsequent characterization, which includes optical microscopy, photoluminescence spectroscopy, Raman microscopy, and X-ray diffraction. Analyzing two different perovskites both before and after a two-week period of storage revealed that while synthesis is indeed experiment-friendly, these materials have a concerning lack of stability even in ideal conditions.
ContributorsBuzas, Benjamin Joseph (Author) / Tongay, Sefaattin (Thesis director) / Muhich, Christopher (Committee member) / Materials Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Graphene is a very strong two-dimensional material with a lot of potential applications in microelectromechanical systems (MEMS). In this research, graphene is being optimized for use in a 5 m x 5 m graphene resonator. To work properly, this graphene resonator must have a uniform strain across all manufactured devices. To reduce strain induced in graphene sheets grown for use in these resonators, evaporated platinum has been used in this investigation due to its relatively lower surface roughness compared to copper films. The final goal is to have the layer of ultrathin platinum (<=200 nm) deposited on the MEMS graphene resonator and used to grow graphene directly onto the devices to remove the manual transfer step due to its inscalability. After growth, graphene is coated with polymer and the platinum is then etched. This investigation concentrated on the transfer process of graphene onto Si/SiO2 substrate from the platinum films. It was determined that the ideal platinum etchant was aqua regia at a volumetric ratio of 6:3:1 (H2O:HCl:HNO3). This concentration was dilute enough to preserve the polymer and graphene layer, but strong enough to etch within a day. Type and thickness of polymer support layers were also investigated. PMMA at a thickness of 200 nm was ideal because it was easy to remove with acetone and strong enough to support the graphene during the etch process. A reference growth recipe was used in this investigation, but now that the transfer has been demonstrated, growth can be optimized for even thinner films.
ContributorsCayll, David Richard (Author) / Tongay, Sefaattin (Thesis director) / Lee, Hyunglae (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Solid-state lithium-ion batteries are a major area of research due to their increased safety characteristics over conventional liquid electrolyte batteries. Lithium lanthanum zirconate (LLZO) is a promising garnet-type ceramic for use as a solid-state electrolyte due to its high ionic conductivity. The material exists in two dierent phases, one that is cubic in structure and one that is tetragonal. One potential synthesis method that results in LLZO in the more useful, cubic phase, is electrospinning, where a mat of nanowires is spun and then calcined into LLZO. A phase containing lanthanum zirconate (LZO) and amorphous lithium occursas an intermediate during the calcination process. LZO has been shown to be a sintering aid for LLZO, allowing for lower sintering temperatures. Here it is shown the eects of internal LZO on the sintered pellets. This is done by varying the 700C calcination time to transform diering amounts of LZO and LLZO in electrospun nanowires, and then using the same sintering parameters for each sample. X-ray diraction was used to get structural and compositional analysis of both the calcined powders and sintered pellets. Pellets formed from wires calcined at 1 hour or longer contained only LLZO even if the calcined powder had only undergone the rst phase transformation. The relative density of the pellet with no initial LLZO of 61.0% was higher than that of the pellet with no LZO, which had a relative density of 57.7%. This allows for the same, or slightly higher, quality material to be synthesized with a shorter amount of processing time.
ContributorsLondon, Nathan Harry (Author) / Chan, Candace (Thesis director) / Tongay, Sefaattin (Committee member) / Department of Physics (Contributor) / Materials Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
In the Rare-earth-Tri-telluride family, (RTe3s) [R=La, Ce, Nd, Sm, Gd, Tb, Dy, Er, Ho, Tm] the emergence of Charge Density Waves, (CDW) has been under investigation for a long time due to broadly tunable properties by either chemical substitution or pressure application. These quasi 2D Layered materials RTe3s undergo Fermi Surface Nesting leading to CDW instability. CDWs are electronic instabilities found in low-dimensional materials with highly anisotropic electronic structures. Since the CDW is predominantly driven by Fermi-surface (FS) nesting, it is especially sensitive to pressure-induced changes in the electronic structure. The FS of RTe3s is a function of p-orbitals of Tellurium atoms, which are arranged in two adjacent planes in the crystal structure. Although the FS and electronic structure possess a nearly four-fold symmetry, RTe3s form an incommensurate CDW.This dissertation is structured as follows: Chapter 1 includes basic ideas of Quantum materials, followed by an introduction to CDW and RTe3s. In Chapter 2, there are fundamentals of crystal growth by Chemical Vapor Transport, including various precursors, transport agent, temperature gradient, and rate of the reaction. After the growth, the crystals were confirmed for lattice vibrations by Raman, for composition by Energy Dispersive Spectroscopy; crystal structure and orientation were confirmed by X-ray Diffraction; magnetic ordering was established by Vibrating sample measurement. Detailed CDW study was done on various RTe3s by Raman spectroscopy. The basic mechanism and instrumentations used in these characterizations are explained in Chapter 3. Chapter 4 includes experimental data for crystal growth and results of these characterizations for Parent RTe3s. Chapter 5 includes fundamental insights on Cationic alloying of RTe3s, along with one alloy system’s crystal growth and characterization. This work tries to explain the behavior of CDW by a Temperature-dependent Raman study of RTe3s established the CDW transition temperature accompanied by Phonon softening; Angle-resolved Raman data confirming the nearly four-fold symmetry; thickness-dependent Raman spectroscopy resulting in the conclusion that as thickness decreases CDW transition temperature increases. Also, CDW transition is analyzed as a function of alloying.
ContributorsAttarde, Yashika (Author) / Tongay, Sefaattin (Thesis advisor) / Botana, Antia (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2021
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the last few decades, extensive research efforts have been focused on scaling down silicon-based complementary metal-oxide semiconductor (CMOS) technology to enable the continuation of Moore’s law. State-of-art CMOS includes fully depleted silicon-on-insulator (FDSOI) field-effect-transistors (FETs) with ultra-thin silicon channels (6 nm), as well as other three-dimensional (3D) device architectures like Fin-FETs, nanosheet FETs, etc. Significant research efforts have characterized these technologies towards various applications, and at different conditions including a wide range of temperatures from room temperature (300 K) down to cryogenic temperatures. Theoretical efforts have studied ultrascaled devices using Landauer theory to further understand their transport properties and predict their performance in the quasi-ballistic regime.Further scaling of CMOS devices requires the introduction of new semiconducting channel materials, as now established by the research community. Here, two-dimensional (2D) semiconductors have emerged as a promising candidate to replace silicon for next-generation ultrascaled CMOS devices. These emerging 2D semiconductors also have applications beyond CMOS, for example in novel memory, neuromorphic, and spintronic devices. Graphene is a promising candidate for spintronic devices due to its outstanding spin transport properties as evidenced by numerous studies in non-local lateral spin valve (LSV) geometries. The essential components of graphene-based LSV, such as graphene FETs, metal-graphene contacts, and tunneling barriers, were individually investigated as part of this doctoral dissertation.
In this work, several contributions were made to these CMOS and beyond CMOS technologies. This includes comprehensive characterization and modeling of FDSOI nanoscale FETs from room temperature down to cryogenic temperatures. Using Landauer theory for nanoscale transistors, FDSOI devices were analyzed and modeled under quasi-ballistic operation. This was extended towards a virtual-source modeling approach that accounts for temperature-dependent quasi-ballistic transport and back-gate biasing effects. Additionally, graphene devices with ultrathin high-k gate dielectrics were investigated towards FETs, non-volatile memory, and spintronic devices. New contributions were made relating to charge trapping effects and their impact on graphene device electrostatics (Dirac voltage shifts) and transport properties (impact on mobility and conductivity). This work also studied contact resistance and tunneling effects using transfer length method (TLM) graphene FET structures and magnetic tunneling junction (MTJ) towards graphene-based LSV.
ContributorsZhou, Guantong (Author) / Sanchez Esqueda, Ivan (Thesis advisor) / Vasileska, Dragica (Committee member) / Tongay, Sefaattin (Committee member) / Thornton, Trevor (Committee member) / Arizona State University (Publisher)
Created2023