Now showing items 21-40 of 19204

    • Wakeley Pros., seismic sketch map, Sheridan area, Wyoming

      Colorado School of Mines. Arthur Lakes Library, 1900?-1985
    • Numbers 4 & 5 represent the "Antelope Lake Prospect" Carbon County, Wyoming

      Hintze, F. F. (Colorado School of Mines. Arthur Lakes Library, 1900?-1985)
    • Atlantic mining district, Golden Dome group of mines, Miners Deligt, Wyoming

      Colorado School of Mines. Arthur Lakes Library, 1910?-1919
    • Kerr-McGee Gillette area properties: overburden map

      Colorado School of Mines. Arthur Lakes Library, 1900?-1985
    • Laramie County, Wyoming

      Colorado School of Mines. Arthur Lakes Library, 1900?-1985
    • Ayrshire South

      Colorado School of Mines. Arthur Lakes Library, 1900?-1985
    • Marche du Colonel Bouquet atravers le Pays des Indiens en 1764

      Hutchins, Thomas, 1730-1789; Bouquet, Henry, 1719-1765 (Colorado School of Mines. Arthur Lakes Library, 1764)
    • Map showing claims on hematite deposits near Stanford, Montana

      Colorado School of Mines. Arthur Lakes Library, 1900?-1985
    • Sun River Project, Montana: general map

      United States Reclamation Service (Colorado School of Mines. Arthur Lakes LibraryUnited States Reclamation Service, 1905)
    • Alkali anticline: Big Horn County, Wyoming

      Colorado School of Mines. Arthur Lakes Library, 1900?-1985
    • Fluorescent point tracking for optical encoding in near-field ptychography

      Masztalerz, Veronica; Mubeen, Nabeeha; Selman, Beau; Adams, Daniel
      Ptychography is a process that utilizes scattered light patterns to image an object. To perform ptychography, an optical stage is employed to shift an object relative to a laser, often in hundreds of discrete positions. Keeping track of this motion involves a system of encoding the stage's position at each shift. This research focuses on optical encoding by tracking the motion of a fluorescent point adjacent to the stage. By imaging the fluorescent point at each position along with computing a cross-correlation with prior positions, information about the fluorescent point's relative position is calculated. Thus, the difference between highest-intensity positions at each stage motion represents the length traveled by the stage, effectively encoding its movement.
    • Optimizing shallow geothermal energy

      Neuder, Camille; Florida, Mark
      This project focuses on using shallow geothermal energy to provide clean and optimal energy to a 2000 square foot house test site in Dodge City, Kansas. The described test site aims to maintain a consistent temperature of 70°F year-round, 24 hours per day, using the most efficient methods of geothermal energy for the local environment. The identified solution involves a vertical loop with an open-source pumping system from the local aquifer connected to a heat pump. The results demonstrate that cooling can be effectively managed with circulating water from a shallow aquifer, while heating to 70°F requires supplemental energy due to the aquifer's temperature of 60°F. The mechanical and heat transfer process design can be adapted to provide efficient heating and cooling to multiple houses, facilitating an expansion from individual residential use to neighborhood access by connecting houses with horizontal loops. The analysis techniques used in this study can be extrapolated to any location, considering that each location will have different temperature profiles and aquifer depths to extract heat and cooling mediums.
    • Reconstructing ultra-high-energy cosmic ray cascades with deep learning

      Woo, Nathan; Wang, Zhuoyi; Mayotte, Eric; Mayotte, Sonja
      Ultra-high-energy cosmic rays are single atomic nuclei from other galaxies and the most energetic phenomena known to humankind. In observing these and using them to increase our astrophysical understanding, two properties are of key importance: the particle's energy and its mass. When one of these particles strikes the top of the atmosphere, they are destroyed and create a high-energy particle cascade which can reach 10 billion particles in size. Here, we present a new machine learning method to maximize the amount of information that can be extracted on the mass of the ultra-high-energy cosmic ray. We find that a neural networks such as a convolutional neural network can improve the mass prediction of ultra-high-energy cosmic rays, with a proton-iron merit factor of 9.81, three times better than traditional solutions. The extra precision on mass reconstruction for these cosmic rays can lead to a revolution on the quality of astrophysics performed with current ground-based observatories, and displays the usefulness of machine learning techniques in astrophysics.
    • Fabricate mesoporous silica nanoparticles with iron core for transporting organometallic cataysts and exogenous enzymes

      Raizada, Rewa; Kovach, Nolan C.; Smith, Jaclyn; Trewyn, Brian G.
      The objective of this research is to design and fabricate multifunctional mesoporous materials with an iron core to hold and transport paramagnetic molecular catalysts and biocatalysts that are capable of synthesizing advanced molecules in intracellular environments. Mesoporous Silica Nanoparticles (MSN) have a large surface area and can be used as drug carriers. Amine functionalization of MSN helps with dispersion, while paramagnetic component can assist in directed drug delivery under external magnetic field. Two distinct methods were employed for the manufacture of the nanocarriers: a co-precipitation and a solvothermal method. Both methods resulted in high surface area material with ordered, uniform pores. We will discuss the findings and material properties of both types of materials along with discussing applications for magnet-responsive mesoporous materials.
    • Machine learning as a tool to understand and predict ankle sprain

      Szigeti-Larenne, Jordan; Mazur, Mykola; Petrella, Anthony
      Ankle injury is a fundamental and recurring issue in high impact activity that affects athletes, military personnel, and everyday people. Current attempts at ankle injury mitigation look towards passive devices that simply support the ankle through bound structure built through the device. The goal of this study was to identify the risk factors and indicators that are actively associated with ankle sprain, implement them into a machine learning algorithm, and establish a program that could significantly predict ankle sprain. The currently understood risk factors were distinguished through previous research papers and were used as key features for the machine learning algorithm. Based on these features machine learning algorithms were used to predict ankle sprain. This preliminary research sets the stage for future research into wearable devices that could be implemented into a passive ankle brace and predict sprain while in motion. This prediction could then be implemented into active devices that will increase the stiffness of the brace only when sprain is likely.
    • Identification and analysis of two-pixel correlated events in BeEST Phase-III data

      BeEST Collaboration; Lamm, Amii O.; Leach, Kyle G.
      The Beryllium Electron Capture in Superconducting Tunnel Junctions (BeEST) experiment measures Lithium-7 atomic recoils from the electron capture decay of Beryllium-7 as a precision search for neutrino-coupled BSM physics. Following the single-pixel demonstration in Phase-II, Phase-III uses a 6x6 grid of superconducting tunnel junction (STJ) detectors, allowing us to analyze spatial correlations and simultaneity between devices. Due to the sensitivity of the detector, there are external events that must be identified and filtered out for the creation of the final Phase-III limit. In this talk, we present a systematic analysis of two-pixel correlated events for their location, time, energy and pulse shape to identify anomalous events and hypothesize their origins. By examining the shape and occurrence of these events, we can understand and reduce systematic uncertainty in our data and contribute to the design of the Phase-IV detector array.
    • Engineering surface properties through controlled radical polymerization for therapeutic application

      Kani, Wakana G.; Humpal, Adam; Kumar, Ramya
      Polymers play an integral role in therapeutic applications, especially in the development of drug delivery systems. Advancements in controlled polymerization have allowed for a greater control of polymer architecture. This work explores the atom transfer radical polymerization (ATRP) of hydrophobic polymers in alcohol-water media to find reaction conditions balancing an increased reaction rate while maintaining consistency and stability. The rate of polymerization was tuned by adjusting reactant ratios, altering the rate of radical formation and deactivation. Different solvent properties were studied as ATRP relies on water for a controlled reaction, necessitating a delicate balance with the alcohol required to solubilize the monomer. More protic solvents were shown to increase the reaction rate, but resulted in decreased control due to a higher probability of termination events. After successfully developing optimized reactions, the work is translated to polymerization on nanoparticles for drug delivery applications. The effects of pH and ion concentration on polymer swelling are studied through film characterization using dynamic light scattering (DLS) and zeta potential, measuring the changes in film thickness and surface charge.
    • Remote S-parameter communication from NanoVNA v2

      Hora, Kenny Y.; Elsherbeni, Atef Z.; Aaen, Peter H.
      Measurement of biological metabolites, such as glucose and lactate are important in both healthcare and athletic settings. Research is currently being conducted on remote sensing approaches to these metabolites, many of which use on-body antenna or resonator circuits that measure changes in resonant frequencies to determine the concentrations of the metabolite. However, these approaches use conventional laboratory VNAs, which are impractical for on-body measurement of moving subjects due to their size. A software program was developed for a small handheld vector network analyzer (NanoVNA) that allows it to be attached to a single-board computer and attached to the subject being measured. This permits the subject to move freely while measurements are wirelessly transmitted to a base station where they can be further analyzed.
    • One-pot synthesis of mesoporous cobalt phosphate used as a catalase-like nanozyme

      Hughes, Davin V.; Berstler, Calvin; Trewyn, Brian G.
      Being able to conduct multi-step chemoenzymatic catalysis in a one-pot system is a challenging task. A chemoenzymatic reaction represents a reaction where two or more consecutive transformations take place in a single reaction vessel. This process uses both enzymatic and chemical catalysis to more efficiently generate desired products from commercially available starting materials while removing the need to isolate intermediates. With new techniques to support employing enzymes and organic species within single-pot systems, the use of catalytically active mesoporous nanomaterials as enzyme supports has the potential benefits to enhance the performance of chemoenzymatic tandem systems. There are numerous advantages to using enzymes though they are costly and their functionality is limited to a small range of temperatures and pH levels. Because of this, enzyme mimics (Nanozymes) are very sought-after inorganic alternatives that are low-cost, high-stability alternatives. Here I explore a low-cost, high-efficiency method to synthesize a mesoporous cobalt phosphate (mCoPi) with the ability to transform hydrogen peroxide into water and oxygen and to be able to act as a catalase-like nanozyme in a variety of harsh local environments.