RPI Theses Online (Complete)

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This collection consists of Ph.D. dissertations and Master's theses completed at Rensselaer Polytechnic Institute, Troy campus. Ph.D. dissertations since December 2006 and Master's theses since December 2007 are available. In addition, some older theses and dissertations have been added. Theses in this collection have either a Standard Rensselaer License or a Creative Commons License. Original paper copies of all dissertations and theses (including those not available online) are available in the Rensselaer Libraries.

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Now showing 1 - 5 of 3764
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    MoveOn and E-motion : the paradox of cyberactivism in consumer society
    (Rensselaer Polytechnic Institute, Troy, NY, 2006-08) Dincki, Sandrine; Nadel, Alan, 1947-
    The relationship between media and democracy has always been characterized by a tension between public and private interests with a tendency for the latter to prevail. Mediated politics and consumption in a democratic society paradoxically enable “lifestyle politics” and degrade the “public sphere.” The political and the commercial realms are not necessarily exclusive as MoveOn illustrates. MoveOn is an online activist group with a conventional mass media approach to politics. The production, representation, and consumption dimensions of the “circuit of culture” are used to investigate MoveOn. It has strong ties with the conventional media industry and the Silicon Valley culture and heavily relies on marketing techniques, standardization and pseudo-individualization. MoveOn members are a crucial site of production that shows the tension between empowerment and control. MoveOn produces the conditions for action while members materialize action and become both producer and consumer. MoveOn also exemplifies the confluence of political and symbolic representations in that its members are both subjects of the former and objects of the latter. Its representation practices include pseudo-events and pseudo-heroes. These practices are mediated by the rules of the “public screen.” “Astroturfing” practices problematize the dichotomy artificial vs. real/grassroots. MoveOn follows two axes of representation: “voice” and “numbers.” MoveOn communicates via the “discourse of images” and e-motion, a process through which emotions are filtered, repackaged, and electronically mediated in order to set people in vicarious motion. E-motion encapsulates the convergence of mass media and the internet, and the confluence of activism and consumerism. MoveOn also complies with the “info-tainment” conventions via silent sound bites and the participation of celebrities in its campaigns. Consumption is viewed from the perspective of “consumer culture” that views consumption as mediation between the individual and the social. Consumer activism is one aspect of the convergence of activism and consumerism. “Activist consumerism,” in the form of repetitive participation in campaigns and as exemplified by MoveOn, is another aspect. MoveOn membership consists in “window shoppers,” “immobile activists,” and grassroots activists. Consumption practices can also be a form of dissent or resistance.
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    Exploring cavity effects on protein dynamic disorder with pressure perturbation
    (Rensselaer Polytechnic Institute, Troy, NY, 2022-08) Zhang, Siwen; Royer, Catherine Ann
    Given the central role of conformational dynamics in protein function, it is essential to characterize the timescales and structures associated with these transitions. High-pressure perturbation favors transitions to excited states because they typically occupy a smaller molar volume, thus high pressure facilitates the characterization of conformational dynamics. In this dissertation, we describe the use of a combination of NMR chemical exchange spectroscopy, small-angle X-ray scattering, and high hydrostatic pressure to better investigate conformational exchange during protein folding process. Repeat proteins, with their straightforward architecture, provide good models for probing the sequence dependence of protein conformational dynamics. We choose the leucine rich repeat (LRR) domain of the tumor suppressor pp32 as a model. Pp32 is composed of five LRRs with a capping motif on each of its termini. We show here that the introduction of a cavity in the N-terminal capping motif of pp32 leads to pressure-dependent conformational exchange detected on the 500 µs - 2 ms timescale by 15N CPMG relaxation dispersion analysis. Exchange amplitude and minimum chemical shifts decrease from the N- to the C-terminus, revealing a gradient of structural disruption across the protein. In contrast, introduction of a cavity in the central core of pp32 leads to pressure-induced exchange on a slower (> 2 ms) timescale detected by 15N-CEST analysis. Excited state 15N chemical shifts indicate that in the major excited state, the N-terminal region is mostly unfolded, while the core retains native-like structure. These high-pressure chemical state exchange measurements reveal that cavity position dictates distinct structural dynamics, highlighting the subtle, yet central role of sequence in determining protein conformational dynamics.
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    The impact of surface modification on magneto-functional iron oxide – polyethylene oxide nanocomposites
    (Rensselaer Polytechnic Institute, Troy, NY, 2022-08) Weiblen, Donovan George, III; Ozisik, Rahmi
    Remote triggering of smart materials such as shape memory polymers and nanocomposites for drug delivery are research areas of continued interest for magneto-functional nanocomposites. Magnetically susceptible nanoparticles (NPs) generate heat when exposed to an alternating magnetic field (AMF), making these NPs an ideal candidate for use in smart nanocomposites. While a wide range of nanoparticle chemistries have been studied as ferrofluids in various liquid carrier media, the behavior of these nanoparticles in solid state polymers is not widely understood. This research studies the dependence of heat generation mechanisms and interfacial interactions on nanocomposite structure and morphology, nanoparticle surface coating and nanoparticle concentration in iron oxide (Fe3O4) - poly(ethylene oxide), PEO, nanocomposites. Nanoparticles were coated with surfactants and polymers to improve dispersion and magnetic properties. In this work we first focused on the impact of surface coating of iron oxide (Fe3O4) NPs on magnetic volume reduction, structure, magnetic heating efficiency and mechanical properties of poly(ethylene oxide), PEO, nanocomposites. Uncoated, poly(ethylene glycol), PEG, coated and amine coated 10–nm–diameter Fe3O4 NPs were dispersed at concentrations less than 1% by weight in PEO. Although loaded at low concentration these nanocomposites displayed excellent values for intrinsic power loss especially at low concentrations. We found that dispersion of nanoparticles was strongly related to the character of the surface coating. Uncoated nanoparticles formed large aggregates which led to a significant decrease in the heat generation capabilities. The surface coatings also strongly impacted the magnetic phase reduction. Amine coated nanoparticles had the least magnetic phase reduction. All nanoparticles showed unexpectedly higher heating efficiencies in PEO than when dispersed in water due to decreased magnetic volume loss. Aggregation was determined to be the dominant factor for decreased heating efficiency. Calorimetry experiments explored the impact of the nanoparticles on crystallinity and nucleation rates. Nanoindentation was used to evaluate the mechanical properties via stress relaxation and creep experiments. Amine coated nanoparticles were found to improve the moduli of the nanocomposites. Low concentrations of nanoparticles led to increased relaxation and decreased creep compliance whereas high concentrations had no effect on relaxation and increased creep compliance. The relevance of five rheological models was evaluated. Stress relaxation was best modeled by a power law or logarithmic based model whereas the creep was best modeled by a Generalized Maxwell model. In the second part of this work, single core aminosilane coated 10–nm–diameter Fe3O4 NPs were dispersed at concentrations less than 2% by weight in PEO matrices with varying molecular weights. Altering the matrix molecular weight of the matrix polymer allows for consistent intermolecular interactions between the NP surface groups and the PEO in order to determine relative importance of Brownian and Neel relaxation processes. Increased matrix molecular weight above the polymer matrix entanglement molecular weight led to decreased heat generation efficiency that was consistent with decreases in the nanoparticle magnetic volume determined via vibrating sample magnetometry. Brownian and Neelian relaxation mechanisms were proven to be present despite the high viscosity of the matrix media. Dynamic polymer relaxation modes such as the Reptation or Rouse models were found to be inactive.
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    Cell free production of isobutanol
    (Rensselaer Polytechnic Institute, Troy, NY, 2022-08) Wong, Matthew; Belfort, Georges; Koffas, Mattheos A. G.
    With a need for greener fuels, research into production of biofuels is essential. Isobutanol out preforms ethanol in key metrics such as engine compatibility, energy density, and gasoline blending. Current biofuel strategies of fermentation are constrained by the inherent toxicity of alcohol on microbial cells. While work has been performed on engineering these strains for higher tolerance, cell-free production with enzymes offers a novel approach to bypass the toxicity limitations altogether. These enzymes can also be immobilized to retain enzyme activity and facilitate separations. Based on previous work in the Belfort laboratory, the ketoisovaleric acid pathway was chosen for production of the biofuel, isobutanol. High preforming and stable enzymes were selected from the literature, cloned, expressed, and purified and tested for activity, kinetics, and stability. They were utilized in a novel in vivo to in vitro system, resulting isobutanol titer of 1.78 g/L and yield of 93%. An epoxy immobilized reaction scheme resulted in a titer of 2 g/L and 43% yield. The pathway enzymes were then fused to dockerins, which bound to a cohesin scaffold on cellulose. The reaction utilizing this immobilization scheme resulted in a titer of 5.92 g/L and 78.4% yield. Further work can be done to optimize this reaction, as well as to expand the pathway or scaffold, and incorporate separation of the isobutanol for eventual scaleup.
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    Data-driven modeling for smart manufacturing systems
    (Rensselaer Polytechnic Institute, Troy, NY, 2022-08) Yang, Shu; Bequette, B. Wayne
    Smart manufacturing (SM) is a new paradigm of manufacturing that leverages rapid advance in information technologies, industrial Internet-of-Things to improve the flexibility and adaptability of manufacturing, which is expected to fuel the next industrial revolution. The increasingly available data is one of the major driving forces of SM, where data-driven modeling plays a central role in harnessing the power of the advanced infrastructure. However, developing data-driven models for SM applications poses unique challenges for SM practitioners. This dissertation discussed three data-driven modeling projects.The first project develops a fault prediction model for a steel manufacturing process using casting and steelmaking data. Due to the limitations in instrumentation and the complexity of the underlying mechanisms, the data generating process is highly uncertain and time-varying. To address this challenge, this project incorporated mechanistic knowledge including process flowsheet, chemistry, and transport phenomena into the data-driven modeling process. Specifically, the upstream steelmaking data is used to develop the predictive model, while the casting data is used to develop an auto-labeler for the steelmaking data. The resulting model can predict faults 10 minutes in advance. The second project develops a methodology to identify critical process parameters using observational process data. Because the majority of data collected from manufacturing processes are observational, they are intrinsically insufficient to provide causal information, which is necessary for active tasks such as process control and optimization. To address this challenge, this project explored using causal inference to bridge the gap between data-driven methodologies and causal objectives. Specifically, this project discussed a seemingly paradoxical result where the fault prediction model developed in the steelmaking project is used to guide process improvement. Then this project proposed a metric to identify critical process parameters using causal inference, which is illustrated using a simulation case study. The third project explores using nonlinear data-driven models for model-based control. Due to the nonconvexity of many nonlinear data-driven models, using them for optimal control and optimization can lead to suboptimal solutions and increased computation expense. To address this challenge, this project explores using input convex neural networks (ICNNs) for model-based control, which guarantees the control problems to be convex optimization problems. Specifically, this dissertation presented different formulations of ICNNs including convolutional ICNNs, Bayesian last layer ICNNs, and partial ICNNs. Additionally, this project discussed the integration of ICNNs in a model predictive control framework. Through two simulation case studies, the unique benefits of the proposed method are shown.
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