This creative project outlines the steps taken to successfully plan and host a fundraising event at Arizona State University. In my case, this more specifically dealt with organizing a dodgeball tournament between two friendly rivals: police officers and firefighters in the city of Phoenix. All proceeds raised from this fundraising dodgeball tournament were donated back to first responders working in the city of Phoenix.

Graph neural networks (GNN) offer a potential method of bypassing the Kohn-Sham equations in density functional theory (DFT) calculations by learning both the Hohenberg-Kohn (HK) mapping of electron density to energy, allowing for calculations of much larger atomic systems and time scales and enabling large-scale MD simulations with DFT-level accuracy. In this work, we investigate the feasibility of GNNs to learn the HK map from the external potential approximated as Gaussians to the electron density 𝑛(𝑟), and the mapping from 𝑛(𝑟) to the energy density 𝑒(𝑟) using Pytorch Geometric. We develop a graph representation for densities on radial grid points and determine that a k-nearest neighbor algorithm for determining node connections is an effective approach compared to a distance cutoff model, having an average graph size of 6.31 MB and 32.0 MB for datasets with 𝑘 = 10 and 𝑘 = 50 respectively. Furthermore, we develop two GNNs in Pytorch Geometric, and demonstrate a decrease in training losses for a 𝑛(𝑟) to 𝑒(𝑟) of 8.52 · 10^14 and 3.10 · 10^14 for 𝑘 = 10 and 𝑘 = 20 datasets respectively, suggesting the model could be further trained and optimized to learn the electron density to energy functional.

In this project, we will argue that the Kardashian women influence beauty standards by portraying themselves as beauty moguls to control the interests of their audience and promoting themselves as the modern day standard of beauty. This affects their audience by creating unattainable and unrealistic standards of beauty that negatively impacts their sense of self and mental health. We will explore the influence they have created and why their influence remains preferred by their audience over time, explaining how they maintain control of the industry.

In this project, we will argue that the Kardashian women influence beauty standards by portraying themselves as beauty moguls to control the interests of their audience and promoting themselves as the modern day standard of beauty. This affects their audience by creating unattainable and unrealistic standards of beauty that negatively impacts their sense of self and mental health. We will explore the influence they have created and why their influence remains preferred by their audience over time, explaining how they maintain control of the industry.

Mixed Matrix Membranes (MMMs) combine a continuous organic polymer phase with a distributed porous additive, i.e. filler, and benefit from the ease processability of polymers as well as the improved gas separation performance of diverse porous filler materials. MMMs may have separation qualities that outperform the selectivity/permeability trade-off reported in pure polymer membranes. All MMMs require a polymer phase and a filler, and in this research a Pebax-1657 is used as a matrix and for filler a Covalent organic framework (COF) as it is less understood. Covalent organic frameworks (COFs) represent a category of porous organic polymers that have garnered significant interest across various fields, including gas adsorption and storage, catalysis, sensing, and photovoltaics. These frameworks offer outstanding characteristics such as permanent porosity, high surface areas, and easily adjustable frameworks [3]. Additionally, their entirely organic composition can lead to enhanced interactions between fillers and polymers, mitigating the formation of nonselective defects during mixed-matrix membrane (MMM) preparation that are often seen with using other sorts of fillers such as silica and metal- organic frameworks (MOFs). Once synthesized the MMMs which are based on COF will be tested in an in house built gas permeance setup to test for single gas permeance, giving us deep insight into the performance of the COF bas MMMs.

Polyketides are a wide ranging class of natural microbial products highly relevant to the pharmacological industry. As chemical synthesis of polyketides is quite challenging, significant effort has been made to understand the polyketide synthases (PKSs) responsible for their natural production. Native to Streptomyces, the aln biosynthetic gene cluster was recently characterized and encodes for an iterative type I polyketide synthase (iT1PKS). This iT1PKS produces both , and ,-double bond polyketides named allenomycins; however, the basis in which one bond is chosen over the other is not yet clear. The dehydratase domain, AlnB_DH, is thought to be solely responsible for catalyzing double bond formation. Elucidation of enzyme programming is the first step towards reprogramming AlnB_DH to produce novel industrially relevant products. The Nannenga lab has worked as collaborators to the Zhao lab at the University of Illinois at Urbana-Champaign to unravel AlnB_DH’s structure and mechanism. Here, mutant constructs of AlnB_DH are developed to elucidate enzyme structure and provide insight into active site machinery. The primary focus of this work is on the development of the mutant constructs themselves rather than the methods used for structural or mechanistic determination. Truncated constructs were successfully developed for crystallization and upon x-ray diffraction, a 2.45 Å resolution structure was determined. Point-mutated constructs were then developed based on structural insights, which identified H49, P58, and H62 as critical residues in active site machinery.