A survey was created to help gain some insight on the opinions of homeowners across the <br/>Phoenix Metro Area. This survey consisted of 7 questions relating to personal experiences and <br/>the homeowners’ opinions or concerns. The results of the survey showed that there are a few <br/>concerns surrounding solar energy with an emphasis on the cost of maintenance of panels and <br/>the payback period where the homeowners would see a return on their investment. Most of the <br/>homeowners answered that they do not use solar energy but have thought about using it for their <br/>main source of energy before. The homeowners in the survey also thought that solar energy was <br/>overall too expensive and that it would take a long time before they would see any payoff or <br/>savings from the solar panels. It was found that the payback period for panels is around 7 years <br/>and that depending on the size of the solar system installed or on the model used, solar panels <br/>cost much less than many people think. This was found by researching non-biased resources <br/>from government websites and from local energy companies’ websites. To combat the concerns <br/>found from the survey, an infographic was created to help inform the public about solar energy <br/>and allow the homeowners to make decisions that are well informed and not based on <br/>misinformation. The infographic included information related to the survey by explaining the <br/>survey and explaining topics that were of concern to the homeowners who took the survey. In <br/>addition, the infographic displayed information about solar energy and that the decision to use <br/>solar is ultimately up to the audience.

Human beings have long sought to conquer the unconquerable and to push the boundaries of human endurance. There are few such endeavors more challenging than venturing into the coldest and harshest environments on the planet. The challenges these adventurers face are nearly countless, but one that is often underestimated is the massive risk of dehydration in high mountains and the lack of sufficient technology to meet this important need. Astronauts and mountaineers of NASA's Johnson Space Center have created a technology that solves this problem: a freeze-resistant hydration system that helps stop water from freezing at sub-zero temperatures by using cutting-edge technology and materials science to insulate and heat enough water to prevent dehydration over the course of the day, so that adventurers no longer need to worry about their equipment stopping them. This patented technology is the basis of the founding of Aeropak, an advanced outdoor hydration brand developed by three ASU students (Kendall Robinson, Derek Stein, and Thomas Goers) in collaboration with W.P. Carey’s Founder’s Lab. The primary goal was to develop traction among winter sport enthusiasts to create a robust customer base and evaluate the potential for partnership with hydration solution companies as well as direct sales through online and brick-and-mortar retail avenues. To this end, the Aeropak team performed market research to determine the usefulness and need for the product through a survey sent out to a number of outdoor sporting clubs on Arizona State University’s campus. After determining an interest in a potential product, the team developed a marketing strategy and business model which was executed through Instagram as well as a standalone website, with the goal of garnering interest and traction for a future product. Future goals of the project will be to bring a product to market and expand Aeropak’s reach into a variety of winter sport subcommunities, as well as evaluate the potential for further expansion into large-scale retailers and collaboration with established companies.

Much of Nepal lacks access to clean drinking water, and many water sources are contaminated with arsenic at concentrations above both World Health Organization and local Nepalese guidelines. While many water treatment technologies exist, it is necessary to identify those that are easily implementable in developing areas. One simple treatment that has gained popularity is biochar—a porous, carbon-based substance produced through pyrolysis of biomass in an oxygen-free environment. Arizona State University’s Engineering Projects in Community Service (EPICS) has partnered with communities in Nepal in an attempt to increase biochar production in the area, as it has several valuable applications including water treatment. Biochar’s arsenic adsorption capability will be investigated in this project with the goal of using the biochar that Nepalese communities produce to remove water contaminants. It has been found in scientific literature that biochar is effective in removing heavy metal contaminants from water with the addition of iron through surface activation. Thus, the specific goal of this research was to compare the arsenic adsorption disparity between raw biochar and iron-impregnated biochar. It was hypothesized that after numerous bed volumes pass through a water treatment column, iron from the source water will accumulate on the surface of raw biochar, mimicking the intentionally iron-impregnated biochar and further increasing contaminant uptake. It is thus an additional goal of this project to compare biochar loaded with iron through an iron-spiked water column and biochar impregnated with iron through surface oxidation. For this investigation, the biochar was crushed and sieved to a size between 90 and 100 micrometers. Two samples were prepared: raw biochar and oxidized biochar. The oxidized biochar was impregnated with iron through surface oxidation with potassium permanganate and iron loading. Then, X-ray fluorescence was used to compare the composition of the oxidized biochar with its raw counterpart, indicating approximately 0.5% iron in the raw and 1% iron in the oxidized biochar. The biochar samples were then added to batches of arsenic-spiked water at iron to arsenic concentration ratios of 20 mg/L:1 mg/L and 50 mg/L:1 mg/L to determine adsorption efficiency. Inductively coupled plasma mass spectrometry (ICP-MS) analysis indicated an 86% removal of arsenic using a 50:1 ratio of iron to arsenic (1.25 g biochar required in 40 mL solution), and 75% removal with a 20:1 ratio (0.5 g biochar required in 40 mL solution). Additional samples were then inserted into a column process apparatus for further adsorption analysis. Again, ICP-MS analysis was performed and the results showed that while both raw and treated biochars were capable of adsorbing arsenic, they were exhausted after less than 70 bed volumes (234 mL), with raw biochar lasting 60 bed volumes (201 mL) and oxidized about 70 bed volumes (234 mL). Further research should be conducted to investigate more affordable and less laboratory-intensive processes to prepare biochar for water treatment.

This project analyzes the diversity of the various Chinese languages present in the Phoenix metropolitan area. The diversity and presence of these languages can be used to make inferences about different aspects of the Chinese American community in the Phoenix area, and therefore the dialects and compared to other aspects of the Chinese American immigration experience, such as where immigrants are from, what areas of Phoenix they reside, and the Chinese language skills of both the participants and their children. The data is then presented with historical context of the Phoenix Chinese community as well as a brief discussion on the current Chinese community in Phoenix as well as the acculturation of Chinese American children.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

This thesis explores the investigation of the project “Designing for a Post-Diesel Engine World”, a collaborative experiment between organizations within Arizona State University and an undisclosed company. This investigation includes the analysis of various renewable energy technologies and their potential to replace industrial diesel engines as used in the company’s business. In order to be competitive with diesel engines, the technology should match or exceed diesel in power output, have reduced environmental impact, and meet other criteria standards as determined by the company. The team defined the final selection criteria as: low environmental impact, high efficiency, high power, and high technology readiness level. I served as the lead Hydrogen Fuel Cell Researcher and originally hypothesized that PEM fuel cells would be the most viable solution. Results of the analysis led to PEM fuel cells and Li-ion batteries being top contenders, and the team developed a hybrid solution incorporating both of these technologies in a technical and strategic solution. The resulting solution design from this project has the potential to be modified and implemented in various industries and reduce overall anthropogenic emissions from industrial processes.

The ongoing Global Coronavirus Pandemic has been upheving social norms for over a year at this point. For countless people, our lives look very different at this point in time than they did before the pandemic began. Quarantine, Shelter in Place, Work from Home, and Online classes have led global populations to become less active leading to an increase in sedentary lifestyles. The final impact of this consequence is unknown, but emerging studies have led to concrete evidence of decreased physical and mental wellbeing, particularly in children. VirusFreeSports was the brainchild of three ASU Honors students who sought to remedy these devastating consequences by creating environments where children can participate in sports and exercise safely, free of the threat COVID-19 or other transmissible illnesses. The ultimate goal for the project team was to build traction for their idea, which culminated in a video pitch sent to potential investors. Although largely created as an exercise and we did not create a full certification course, merely a prototype through a website with sample questions to gauge interest, the project was a success as a large target market for this product was identified that showed great promise. Our team believes that early entrance to the market, as well as the lack of any other competitors would give the team a tremendous advantage in creating an impactful and influential service.