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Description
Nitrate is the most prevalent water pollutant limiting the use of groundwater as a potable water source. The overarching goal of this dissertation was to leverage advances in nanotechnology to improve nitrate photocatalysis and transition treatment to the full-scale. The research objectives were to (1) examine commercial and synthesized photocatalysts, (2) determine the effect of water quality parameters (e.g., pH), (3) conduct responsible engineering by ensuring detection methods were in place for novel materials, and (4) develop a conceptual framework for designing nitrate-specific photocatalysts. The key issues for implementing photocatalysis for nitrate drinking water treatment were efficient nitrate removal at neutral pH and by-product selectivity toward nitrogen gases, rather than by-products that pose a human health concern (e.g., nitrite). Photocatalytic nitrate reduction was found to follow a series of proton-coupled electron transfers. The nitrate reduction rate was limited by the electron-hole recombination rate, and the addition of an electron donor (e.g., formate) was necessary to reduce the recombination rate and achieve efficient nitrate removal. Nano-sized photocatalysts with high surface areas mitigated the negative effects of competing aqueous anions. The key water quality parameter impacting by-product selectivity was pH. For pH < 4, the by-product selectivity was mostly N-gas with some NH4+, but this shifted to NO2- above pH = 4, which suggests the need for proton localization to move beyond NO2-. Co-catalysts that form a Schottky barrier, allowing for localization of electrons, were best for nitrate reduction. Silver was optimal in heterogeneous systems because of its ability to improve nitrate reduction activity and N-gas by-product selectivity, and graphene was optimal in two-electrode systems because of its ability to shuttle electrons to the working electrode. "Environmentally responsible use of nanomaterials" is to ensure that detection methods are in place for the nanomaterials tested. While methods exist for the metals and metal oxides examined, there are currently none for carbon nanotubes (CNTs) and graphene. Acknowledging that risk assessment encompasses dose-response and exposure, new analytical methods were developed for extracting and detecting CNTs and graphene in complex organic environmental (e.g., urban air) and biological matrices (e.g. rat lungs).
ContributorsDoudrick, Kyle (Author) / Westerhoff, Paul (Thesis advisor) / Halden, Rolf (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2013
Description
The OMFIT (One Modeling Framework for Integrated Tasks) modeling environment and the BRAINFUSE module have been deployed on the PPPL (Princeton Plasma Physics Laboratory) computing cluster with modifications that have rendered the application of artificial neural networks (NNs) to the TRANSP databases for the JET (Joint European Torus), TFTR (Tokamak Fusion Test Reactor), and NSTX (National Spherical Torus Experiment) devices possible through their use. This development has facilitated the investigation of NNs for predicting heat transport profiles in JET, TFTR, and NSTX, and has promoted additional investigations to discover how else NNs may be of use to scientists at PPPL. In applying NNs to the aforementioned devices for predicting heat transport, the primary goal of this endeavor is to reproduce the success shown in Meneghini et al. in using NNs for heat transport prediction in DIII-D. Being able to reproduce the results from is important because this in turn would provide scientists at PPPL with a quick and efficient toolset for reliably predicting heat transport profiles much faster than any existing computational methods allow; the progress towards this goal is outlined in this report, and potential additional applications of the NN framework are presented.
ContributorsLuna, Christopher Joseph (Author) / Tang, Wenbo (Thesis director) / Treacy, Michael (Committee member) / Orso, Meneghini (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2015-05
Description
Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
A model has been developed to modify Euler-Bernoulli beam theory for wooden beams, using visible properties of wood knot-defects. Treating knots in a beam as a system of two ellipses that change the local bending stiffness has been shown to improve the fit of a theoretical beam displacement function to edge-line deflection data extracted from digital imagery of experimentally loaded beams. In addition, an Ellipse Logistic Model (ELM) has been proposed, using L1-regularized logistic regression, to predict the impact of a knot on the displacement of a beam. By classifying a knot as severely positive or negative, vs. mildly positive or negative, ELM can classify knots that lead to large changes to beam deflection, while not over-emphasizing knots that may not be a problem. Using ELM with a regression-fit Young's Modulus on three-point bending of Douglass Fir, it is possible estimate the effects a knot will have on the shape of the resulting displacement curve.
ContributorsSexton, Thurston Bryant (Author) / Takahashi, Timothy (Thesis director) / Jones, Donald (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-05
Description
In shotgun proteomics, liquid chromatography coupled to tandem mass spectrometry
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
(LC-MS/MS) is used to identify and quantify peptides and proteins. LC-MS/MS produces mass spectra, which must be searched by one or more engines, which employ
algorithms to match spectra to theoretical spectra derived from a reference database.
These engines identify and characterize proteins and their component peptides. By
training a convolutional neural network on a dataset of over 6 million MS/MS spectra
derived from human proteins, we aim to create a tool that can quickly and effectively
identify spectra as peptides prior to database searching. This can significantly reduce search space and thus run time for database searches, thereby accelerating LCMS/MS-based proteomics data acquisition. Additionally, by training neural networks
on labels derived from the search results of three different database search engines, we
aim to examine and compare which features are best identified by individual search
engines, a neural network, or a combination of these.
ContributorsWhyte, Cameron Stafford (Author) / Suren, Jayasuriya (Thesis director) / Gil, Speyer (Committee member) / Patrick, Pirrotte (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
A defense-by-randomization framework is proposed as an effective defense mechanism against different types of adversarial attacks on neural networks. Experiments were conducted by selecting a combination of differently constructed image classification neural networks to observe which combinations applied to this framework were most effective in maximizing classification accuracy. Furthermore, the reasons why particular combinations were more effective than others is explored.
ContributorsMazboudi, Yassine Ahmad (Author) / Yang, Yezhou (Thesis director) / Ren, Yi (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In materials science, developing GeSn alloys is major current research interest concerning the production of efficient Group-IV photonics. These alloys are particularly interesting because the development of next-generation semiconductors for ultrafast (terahertz) optoelectronic communication devices could be accomplished through integrating these novel alloys with industry-standard silicon technology. Unfortunately, incorporating a maximal amount of Sn into a Ge lattice has been difficult to achieve experimentally. At ambient conditions, pure Ge and Sn adopt cubic (α) and tetragonal (β) structures, respectively, however, to date the relative stability and structure of α and β phase GeSn alloys versus percent composition Sn has not been thoroughly studied. In this research project, computational tools were used to perform state-of-the-art predictive quantum simulations to study the structural, bonding and energetic trends in GeSn alloys in detail over a range of experimentally accessible compositions. Since recent X-Ray and vibrational studies have raised some controversy about the nanostructure of GeSn alloys, the investigation was conducted with ordered, random and clustered alloy models.
By means of optimized geometry analysis, pure Ge and Sn were found to adopt the alpha and beta structures, respectively, as observed experimentally. For all theoretical alloys, the corresponding αphase structure was found to have the lowest energy, for Sn percent compositions up to 90%. However at 50% Sn, the correspondingβ alloy energies are predicted to be only ~70 meV higher. The formation energy of α-phase alloys was found to be positive for all compositions, whereas only two beta formation energies were negative. Bond length distributions were analyzed and dependence on Sn incorporation was found, perhaps surprisingly, not to be directly correlated with cell volume. It is anticipated that the data collected in this project may help to elucidate observed complex vibrational properties in these systems.
By means of optimized geometry analysis, pure Ge and Sn were found to adopt the alpha and beta structures, respectively, as observed experimentally. For all theoretical alloys, the corresponding αphase structure was found to have the lowest energy, for Sn percent compositions up to 90%. However at 50% Sn, the correspondingβ alloy energies are predicted to be only ~70 meV higher. The formation energy of α-phase alloys was found to be positive for all compositions, whereas only two beta formation energies were negative. Bond length distributions were analyzed and dependence on Sn incorporation was found, perhaps surprisingly, not to be directly correlated with cell volume. It is anticipated that the data collected in this project may help to elucidate observed complex vibrational properties in these systems.
ContributorsLiberman-Martin, Zoe Elise (Author) / Chizmeshya, Andrew (Thesis director) / Sayres, Scott (Committee member) / Wolf, George (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.
ContributorsBrowning, Jacob Christian (Author) / Meuth, Ryan (Thesis director) / Jones, Donald (Committee member) / McCulloch, Robert (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In this paper, I will show that news headlines of global events can predict changes in stock price by using Machine Learning and eight years of data from r/WorldNews, a popular forum on Reddit.com. My data is confined to the top 25 daily posts on the forum, and due to the implicit filtering mechanism in the online community, these 25 posts are representative of the most popular news headlines and influential global events of the day. Hence, these posts shine a light on how large-scale social and political events affect the stock market. Using a Logistic Regression and a Naive Bayes classifier, I am able to predict with approximately 85% accuracy a binary change in stock price using term-feature vectors gathered from the news headlines. The accuracy, precision and recall results closely rival the best models in this field of research. In addition to the results, I will also describe the mathematical underpinnings of the two models; preceded by a general investigation of the intersection between the multiple academic disciplines related to this project. These range from social to computer science and from statistics to philosophy. The goal of this additional discussion is to further illustrate the interdisciplinary nature of the research and hopefully inspire a non-monolithic mindset when further investigations are pursued.
ContributorsPriniski, John Hunter (Author) / Haiyan, Wang (Thesis director) / Hazel, Kwon (Committee member) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12