With an estimated 19.3 million cases and nearly 10 million deaths from cancer in a year worldwide, immunotherapies, which stimulate the immune system so that it can attack and kill cancer cells, are of interest. Tumors are produced from the uncontrolled and rapid proliferation of cells in the body. Cancer cells rely heavily on glutamine for proliferation due to its contribution of nitrogen for nucleotides and amino acids. Glutamine enters the tricarboxylic acid (TCA) cycle as α-ketoglutarate via glutaminolysis, in which glutamine is converted into glutamate by the enzyme glutaminase (GLS). Cancer cell proliferation may be limited by using glutaminase inhibitor CB-839. However, immune cells also rely on these metabolic pathways. Thus, a method for restarting the metabolic pathways in the presence of inhibitors is attractive. Succinate, a key metabolite in the TCA cycle, has been shown to stimulate the immune system despite the presence of metabolic inhibitors, such as CB-839. A delivery method of succinate is through microparticles (MPs) or nanoparticles (NPs) which may be coated in polyethylene glycol (PEG) for improved hydrophilicity. Polyethylene glycol succinate (PEGS) MPs were generated and tested in vivo and were shown to reduce tumor growth and prolong mouse survival. With the success in stimulating the immune system with MPs, NPs were investigated for an improved immune response due to their smaller size. These PES NPs were generated in this study. For clinical settings, it is necessary to scale-up the production of particles. Two methods of scale-up were proposed: (1) a combination of multiple small batches into a mixed batch, and (2) a singular, big batch. Size and release properties were compared to a small batch of PES NPs, and it was concluded that the big batch more closely resembled the small batch compared to the mixed batch. Thus, it was concluded that batch-to-batch variability plays a larger role than volume changes when scaling-up. In clinical settings, it is recommended to produce the particles in a big batch rather than a mixed batch.

DNA is useful for electronic applications due to its self-assembly and electronic properties. It can be improved for this purpose through the addition of metal ions. In this experiment, DNA was modified with silver ions and carbon nanotubes were attached to both ends. The DNA-CNTs were connected over a 300 nm gap between gold electrodes using cysteamine. The conductance was found to be 1.28*10-4 G0, which is similar to literature values for unmodified DNA. Therefore, modifying DNA with silver ions was not found to significantly improve the conductance. It was also found that smaller applied voltages need to be used because of electrochemistry happening above 1 V.

The photodissociation of 1-bromobutane is explored using pump-probe spectroscopy and time-of-flight mass spectrometry. Fragments of bromobutane are constructed computationally and theoretical energies are calculated using Gaussian 16 software. It is determined that the dissociation of bromine from the parent molecule is the most observed fragmentation pathway arising from the excitation of the ground state parent molecule to a dissociative A state using two 400 nm, 3.1 eV pump photons. The dissociation energy of this pathway is 2.91 eV, leaving 3.3 eV of energy that is redistributed into the product fragments as vibrational energy. C4H9 has the highest relative intensity in the mass spectrum with a relative intensity of 1.00. It is followed by C2H5 and C2H4 at relative intensities of 0.73 and 0.29 respectively. Because of the negative correlation between C4H9 and these two fragments at positive time delays, it is concluded that most of these smaller molecules are formed from the further dissociation of the fragment C4H9 rather than any alternative pathways from the parent molecule. Thermodynamic analysis of these pathways has displayed the power of thermodynamic prediction as well as its limitations as it fails to consider kinetic limitations in dissociation reactions.

Rotary drums are used to manufacture pharmaceuticals, cement, food, and other particulate products because of their high heat and mass transfer rates. These processes are governed by particle parameters, such as particle size, particle distribution, and shape, and operating parameters, such as rotation rate and fill level. Enormous energy savings are possible with further research in rotary drums due to potential increases in operating efficiency. This study investigates the drum rotation rate on particle bed temperature at temperatures above 500 °C to see the role that radiation heat transfer plays in this process. 2 mm silica beads and a stainless steel rotary drum were used at a fill level of 25% with rotation rates from 2-10 rpm. A new setup and procedure were developed using heating coils and an IR camera to reach high temperatures. The inner drum wall temperature exceeded the outer drum wall temperature because the steel transmitted more heat into the drum at higher temperatures. Although it was unclear whether the heat transfer rate was affected by the increasing rotation rate, the highest final average particle temperature was obtained at 5 rpm. The particle bed temperature distribution narrowed as the rotation rate increased because, at higher rotation rates, more particles are in contact with the drum wall than at lower rotation rates.

Plastic consumption has reached astronomical amounts. The issue is the single-use plastics that continue to harm the environment, degrading into microplastics that find their way into our environment. Finding sustainable, reliable, and safe methods to break down plastics is a complex but valuable endeavor. This research aims to assess the viability of using biochar as a catalyst to break down polyethylene terephthalate (PET) plastics under hydrothermal liquefaction conditions. PET is most commonly found in single-use plastic water bottles. Using glycolysis as the reaction, biochar is added and assessed based on yield and time duration of the reaction. This research suggests that temperatures of 300℃ and relatively short experimental times were enough to see the complete conversion of PET through glycolysis. Further research is necessary to determine the effectiveness of biochar as a catalyst and the potential of process industrialization to begin reducing plastic overflow.

Using DFT calculations and GAMESS computational software, porphine and its derivatives were analyzed for unique sites to accept the adsorbates As(III), As(V) and P(V) in order to compare resulting adsorption energies and determine if any of these molecules prefer arsenic oxyanions over phosphate. Pure porphine preferred As(III) over P(V) with a resulting adsorption energy of -0.7974 eV. Of the functionalized porphyrins tested, carboxyl porphyrin preferred As(V) over P(V) with a total adsorption energy of -0.7345 eV. Ethyl, methyl, chlorine and amino porphyrin all preferred As(III), with energies of -0.7934, -0.8239, -0.7602, and -0.8508 eV, respectively. Of the metalated porphyrins tested, copper and vanadium porphyrin preferred As(V) over P(V) with adsorption energies of -0.7645 and -2.0915 eV. Chromium, iron and magnesium porphyrin all preferred As(III) over P(V) with energies of -0.5993, -1.4539, and - 1.0790 eV, respectively.

Metal oxides are crucial materials that can be applied to sustainable processes for heat storage or oxygen pumping. In order to be able to apply metal oxides to industrial processes, an effective model of the metal oxide’s reduction thermodynamics is required. To do this, Wilson et al., (2023) developed a compound energy formulism (CEF) algorithm to form these models. The algorithm in its current form can effectively form model thermodynamics; however, the data set required for this model is extensive and large, leading to high costs of modeling a metal oxide. Furthermore, the algorithm faces further difficulties with uneven data densities within the set, leading to poorer fits for low density data. To assist in alleviating the cost associated with data collection, data-omitting strategies were performed to find unimportant points, or points that formed models that had good fits to the original model when removed. After conducting these tests, many points and trends were found to be crucial to keep within the data set, but due to uneven data density, no definitive conclusions could be made on how to reduce the algorithm’s data set. The tests gave evidence that points in high data density regions could be removed from the data set due to only the fact that there existed nearby points to provide essential information to closely interpolate/extrapolate the missing data. Although this project currently did not meet the goal of reducing the data set, preliminary findings of what points could be non-crucial to the data set were identified. Future testing with the proposed weighting methods will be conducted to determine what data can be safely removed from the set to form models that properly reflect the metal oxide’s properties.

In this thesis, six experiments which were computer simulations were conducted in order to replicate the negative association between sample size and accuracy that is repeatedly found in ML literature by accounting for data leakage and publication bias. The reason why it is critical to understand why this negative association is occurring is that in published studies, there have been multiple reports that the accuracies in ML models are overoptimistic leading to cases where the results are irreproducible despite conducting multiple trials and experiments. Additionally, after replicating the negative association between sample size and accuracy, parametric curves (learning curves with the parametric function) were fitted along the empirical learning curves in order to evaluate the performance. It was found that there is a significant variance in accuracies when the sample size is small, but little to no variation when the sample size is large. In other words, the empirical learning curves with data leakage and publication bias were able to achieve the same accuracy as the learning curve without data leakage at a large sample size.

Ketone levels give an insight into the bodies metabolism. People with epilepsy or people dieting may want to keep their levels high, whereas type one diabetics or those recovering from eating disorders may want to keep their levels low. Current ketone detection methods involve blood samples or urinalysis. A ketone (acetone) biosensor was fabricated to detect levels in human breath, providing a noninvasive way to quickly and accurately detect ketone levels in the body.