Physical Sciences
Microplastics in Soil: Testing Detection Limits with FTIR and DSC
Author(s): Jenna Olivier
Identification of Environmental Microplastics Using Raman Spectroscopy
Author(s): Courtney J. Ebert, Korryn Narvaez, Eliza Ballantyne, Stone Smith, Reece Anderson
Geochemical Analysis of Amphiboles at Mt Hillers, UT
Author(s): Zoe Fischer, Parker Tenney, Chloe FitzGerald Taylor, Logan Chappell, Spencer Hahnem
Strain-Driven Metal-to-Insulator Transition and Ferroelectricity in WTe₂
Author(s): Ross Richins
Encapsulation of Chemotherapeutic Temozolomide in Bovine-Milk Derived Extracellular Vesicles
Author(s): Charlee Cannon, Torrance Johnson, Caleb Weaver
Modifying the Light-Harvesting Capabilities of Phycocyanin using Rhodamine-B
Author(s): Brecken Shakespeare, Colton Koch
Synthesis of Chiral ɑ-Amino Boronates via a 3-Component Coupling Reaction
Author(s): Anna Page
Quantitation of Trace Oxygen in Anoxic Water Implementing a Microfluidic Device
Author(s): Corban Kerr, Kason Stinson
Tectonic History of the Rio Grande Rift Through a Study of Sediment Provenance
Author(s): Brian Angell
A Pilot Study Investigating Virtual Reality for Chemical Education
Author(s): Kaden Jensen
Creating a New Distance Ladder with Surface Brightness Fluctuations
Author(s): Mikaela Cowles
Seven potential double star systems in the stellar association WSD 05205+3654
Author(s): Stryder Wiese
Optical Scattering for Rapid UTI Detection
Author(s): Feng Guo, Elise Bauer, Kimball Henstrom, Caroline Torgersen, Hannah Thrupp, Isaac Zabriskie, Alex Martinez, Keaton Fuller, Clint Flinders
The study of urea on protein biomarker detection by quantum dots
Author(s): Daniel Jensen, Caitlin West
Differences between sexes in spatial visualization and memorization in organic chemistry
Author(s): Dagney Goodfellow, Lauren Jensen, Derek Baker, Seunghwan Shin
Brain-eating Amoeba: A Multi-drug Study to Combat Naegleria fowleri and Improve Survival Rates
Author(s): Victoria Green, Ethan Jensen, Ethan Stryker
Lensless High-Resolution Imaging with Laser Interference
Author(s): Ulises Thornock, Brian Weaver, Jackson Phippen
Insights into hydrogen storage utilizing cyclic amino-borane complexes
Author(s): Jordan Colmenero
Optimization of Cannabinoid Permeation Agents for Transdermal Delivery Applications
Author(s): Madison Bulloch, Alexia Casillas
Testing of an Acousto-Optic Modulator for Implementation as a Laser Q-Switch
Author(s): Micah Laing, Benjamin Holt
Increased Efficiency in Nonlinear Wireless Power Transfer
Author(s): Zachary Butler, Kendall Rosenkrantz, Yoonji Yo
Student's perceptions on the use of Virtual Reality in organic and inorganic chemistry
Author(s): Isabelle Smith, Alexandra Routsis, Laryssa Larson, Josie Wright, Kaden Jensen
Optimizing Continuous-wave Nd:YVO4 Laser Performance for Longwave Infrared Generation
Author(s): Benjamin Holt, Alex Gibb, MicahLaing
Fluoride Speciation Analysis of the Great Salt Lake Utah
Author(s): Danielle Kemmer, Dillon Reynolds, Alyssa Brown, Benjamin Judd, Dean V. Smith, Tyler Jenkins, Asha Ahmed, Amber Thornton Miller, Dylan Jenkins, Nashly Cruz-Guzman
Synthesis and Characterization of Novel Pterostilbene Derivatives for Potential use as Therapeutics
Author(s): Austin Lamoreaux
Exploring Urban Wildlife Management Plans: A Case Study in Cedar City
Author(s): Jhen Allison Seguiwan
Shocked Electrons: Determination of the Heating Mechanism in Abell 665
Wik, Daniel (University of Utah)
Faculty Advisor: Wik, Daniel (Science, Physics and Astronomy)
Mergers between galaxy clusters are some of the most energetic events in the universe, driving shock fronts in the intracluster medium (ICM), an X-ray hot plasma permeating the cluster. Shock fronts heat thermal electrons, causing an increase in their temperature. The mechanism by which this occurs is undetermined, with two models being proposed to explain the phenomenon. The first proposes direct shock-heating and the second suggests indirect adiabatic compression, with the electrons subsequently equilibrating with ions heated by the shock. We utilize NuSTAR observations, advantaging its effective area at higher energies, of a shock in the merging cluster Abell 665 in order to discriminate between the models. To do so, a temperature profile was constructed across the shock, utilizing spectral fitting, and compared against the models' predictions. In addition, temperature maps across the cluster were generated to better understand the merger event as a whole. We find that the temperature profile is suggestive of the shock model but is not yet statistically significant, due to NuSTAR's comparatively worse spatial resolution. As a result, we apply a novel joint fitting technique to NuSTAR data and Chandra observations in order to statistically distinguish between the models for the first time, accounting for the scattering of photons due to the PSF. Understanding these processes increases our understanding of the magnetic field of the ICM, allowing for mass determination, permitting galaxy clusters to be used to constrain cosmological studies.
Faculty Advisor: Wik, Daniel (Science, Physics and Astronomy)
Mergers between galaxy clusters are some of the most energetic events in the universe, driving shock fronts in the intracluster medium (ICM), an X-ray hot plasma permeating the cluster. Shock fronts heat thermal electrons, causing an increase in their temperature. The mechanism by which this occurs is undetermined, with two models being proposed to explain the phenomenon. The first proposes direct shock-heating and the second suggests indirect adiabatic compression, with the electrons subsequently equilibrating with ions heated by the shock. We utilize NuSTAR observations, advantaging its effective area at higher energies, of a shock in the merging cluster Abell 665 in order to discriminate between the models. To do so, a temperature profile was constructed across the shock, utilizing spectral fitting, and compared against the models' predictions. In addition, temperature maps across the cluster were generated to better understand the merger event as a whole. We find that the temperature profile is suggestive of the shock model but is not yet statistically significant, due to NuSTAR's comparatively worse spatial resolution. As a result, we apply a novel joint fitting technique to NuSTAR data and Chandra observations in order to statistically distinguish between the models for the first time, accounting for the scattering of photons due to the PSF. Understanding these processes increases our understanding of the magnetic field of the ICM, allowing for mass determination, permitting galaxy clusters to be used to constrain cosmological studies.
Use of a Portable Handheld X-Ray Fluorescence Unit (pXRF) to Measure Alteration in Exhumed Fault Zones: Implications for Hydrologic Rock Properties and Injection Induced Seismicity
Paulding, Anna (Utah State University)
Faculty Advisor: Bradbury, Kelly (College of Science, Geosciences Department)
A dramatic increase in seismicity has occurred in the midcontinent region since 2009 (Rubinstein and Mahani, 2015), causing public concern for the stability of infrastructure and buildings. Several studies have directly linked this seismicity to the reactivation of buried fault systems near the Paleozoic sedimentary bedrock-Precambrian crystalline basement contact as a result of high volumes of injection of wastewater produced by the oil and gas industry (Ellsworth, 2013; Keranen et al., 2013).
The reactivation of fault zones due to fluid injection is not only influenced by injection rates but also by the ability of fluids to migrate along or across the contact, which is controlled by the rock properties and geologic setting. To better understand the rock property variations that may occur along the nonconformity interface, we use an outcrop analog site of an exhumed fault near Gunnison, Colorado. My undergraduate research focuses on using a portable handheld X-Ray Fluorescence Unit (pXRF) as a tool to measure compositional variations in outcrop. To directly compare data, a calibration using 16 USGS Concentration Standards as well as 12 analog samples will be used to create a calibration optimized for this specific suite of rocks which informs the accuracy of in-situ field data measurements against laboratory measurements of powdered samples, influencing how future pXRF measurements can be analyzed. Micro-scale variations of major and trace element concentrations reflect alteration and related fluid-rock interactions and may serve as a proxy for fluid migration along or across faulted sections of a nonconformity interface. I propose that calibrated pXRF data and whole rock XRF data is a useful tool for understanding the nature and degree of rock alteration in fault zones and across analog sites nonconformity interface. These data can aid in a more broad understanding of how pXRF data can be used in the field to characterize the nonconformity interface and fault zones.
Faculty Advisor: Bradbury, Kelly (College of Science, Geosciences Department)
A dramatic increase in seismicity has occurred in the midcontinent region since 2009 (Rubinstein and Mahani, 2015), causing public concern for the stability of infrastructure and buildings. Several studies have directly linked this seismicity to the reactivation of buried fault systems near the Paleozoic sedimentary bedrock-Precambrian crystalline basement contact as a result of high volumes of injection of wastewater produced by the oil and gas industry (Ellsworth, 2013; Keranen et al., 2013).
The reactivation of fault zones due to fluid injection is not only influenced by injection rates but also by the ability of fluids to migrate along or across the contact, which is controlled by the rock properties and geologic setting. To better understand the rock property variations that may occur along the nonconformity interface, we use an outcrop analog site of an exhumed fault near Gunnison, Colorado. My undergraduate research focuses on using a portable handheld X-Ray Fluorescence Unit (pXRF) as a tool to measure compositional variations in outcrop. To directly compare data, a calibration using 16 USGS Concentration Standards as well as 12 analog samples will be used to create a calibration optimized for this specific suite of rocks which informs the accuracy of in-situ field data measurements against laboratory measurements of powdered samples, influencing how future pXRF measurements can be analyzed. Micro-scale variations of major and trace element concentrations reflect alteration and related fluid-rock interactions and may serve as a proxy for fluid migration along or across faulted sections of a nonconformity interface. I propose that calibrated pXRF data and whole rock XRF data is a useful tool for understanding the nature and degree of rock alteration in fault zones and across analog sites nonconformity interface. These data can aid in a more broad understanding of how pXRF data can be used in the field to characterize the nonconformity interface and fault zones.
Isotope Paleothermometry of Belemnites from the Jurassic Sundance Sea of Western North America
Perdue, Perdue; Burke, Joshua; Bylund, Kevin; Stephen, Daniel (Utah Valley University)
Faculty Advisor: Stephen, Daniel (Utah Valley University, Earth Science)
The Sundance Sea covered much of western North America during the Middle to Late Jurassic Period. Deposits from this vast epeiric sea are now widely exposed across the region, including the Stump Formation in northeastern Utah, which consists of sandstones and shales reflecting shallow marine deposition. Well-preserved belemnites (Pachyteuthis densus, Oxfordian Stage, ~156 Ma) collected from this unit preserve stable isotope data (_18O and _13C ) that can be used to better understand the paleoceanography and paleoclimatology of the area, as well as possibly some paleobiologic characteristics such as migration patterns through the life cycle and age at sexual maturity and death. Incremental growth of belemnites created growth bands that record isotopic values through various life stages, thus potentially providing information about the life history traits of these organisms, in addition to seasonal temperature variations. Preliminary results suggest our material is consistent with previous reports from other locations in the region, with paleotemperatures in the 17 to 20° C range. In addition, there is some indication of seasonal variations. However, analyses of more samples and further evaluation of potential diagenetic alteration is necessary before more robust conclusions can be drawn.
Faculty Advisor: Stephen, Daniel (Utah Valley University, Earth Science)
The Sundance Sea covered much of western North America during the Middle to Late Jurassic Period. Deposits from this vast epeiric sea are now widely exposed across the region, including the Stump Formation in northeastern Utah, which consists of sandstones and shales reflecting shallow marine deposition. Well-preserved belemnites (Pachyteuthis densus, Oxfordian Stage, ~156 Ma) collected from this unit preserve stable isotope data (_18O and _13C ) that can be used to better understand the paleoceanography and paleoclimatology of the area, as well as possibly some paleobiologic characteristics such as migration patterns through the life cycle and age at sexual maturity and death. Incremental growth of belemnites created growth bands that record isotopic values through various life stages, thus potentially providing information about the life history traits of these organisms, in addition to seasonal temperature variations. Preliminary results suggest our material is consistent with previous reports from other locations in the region, with paleotemperatures in the 17 to 20° C range. In addition, there is some indication of seasonal variations. However, analyses of more samples and further evaluation of potential diagenetic alteration is necessary before more robust conclusions can be drawn.
ProSPr: Protein Structure Prediction via Interatomic Distances
Hedelius, Bryce; Millecam, Todd; Wingate, David; Della Corte, Dennis (Brigham Young University)
Faculty Advisor: Della Corte, Dennis (BYU College of Physical and Mathematical Sciences, Physics); Wingate, David (BYU College of Physical and Mathematical Sciences, Computer Science)
Substantial progress has been made in the past several years towards the accurate prediction of protein tertiary structures from primary sequence, aided greatly by the integration of machine learning. Current success is based on two-stage protocols: first, the training of a deep convolutional neural network (CNN) to predict macromolecular structure restraints, and second, the use of these restraints to construct a folded three-dimensional structure of the target protein. Such a two-stage folding protocol was used by DeepMind in the recent Critical Assessment of Structure Prediction (CASP13), which outperformed all established groups. However, DeepMind has not expressed a plan to publish the code of their AlphaFold protocol. Here we present ProSPr, a network representing the first part of the AlphaFold pipeline for predicting interatomic distances, and demonstrate its abilities in the contact prediction task relative to other state-of-the-art methods. We also investigate and report on the roles of certain input features in prediction quality. ProSPr is made freely available to the scientific community both as source code and a Docker container, which we anticipate will encourage the development of better techniques for assembling protein structures from restraints.
Faculty Advisor: Della Corte, Dennis (BYU College of Physical and Mathematical Sciences, Physics); Wingate, David (BYU College of Physical and Mathematical Sciences, Computer Science)
Substantial progress has been made in the past several years towards the accurate prediction of protein tertiary structures from primary sequence, aided greatly by the integration of machine learning. Current success is based on two-stage protocols: first, the training of a deep convolutional neural network (CNN) to predict macromolecular structure restraints, and second, the use of these restraints to construct a folded three-dimensional structure of the target protein. Such a two-stage folding protocol was used by DeepMind in the recent Critical Assessment of Structure Prediction (CASP13), which outperformed all established groups. However, DeepMind has not expressed a plan to publish the code of their AlphaFold protocol. Here we present ProSPr, a network representing the first part of the AlphaFold pipeline for predicting interatomic distances, and demonstrate its abilities in the contact prediction task relative to other state-of-the-art methods. We also investigate and report on the roles of certain input features in prediction quality. ProSPr is made freely available to the scientific community both as source code and a Docker container, which we anticipate will encourage the development of better techniques for assembling protein structures from restraints.
Machine learning-based auto-segmentation of polystyrene micro-bead phantoms for cellular confluence measurements
Johnston, Olivia; Preston, Kolten; Hoyt, Tyson; Owens May, April; Bentley, Kaden; Gunnerson, Shane; Johnson, Alex; Parr, McKenna; Reeves, Duncan; Parry, Whitney; Rawson, Clayton; Hart, Vern (Utah Valley University)
Faculty Advisor: Hart, Vern (Science, Physics)
Recent efforts in early cancer detection require identifying the disease at a cellular level, by distinguishing cancer cells from healthy cells at low concentrations (<0.1%). Cancerous cells typically have larger nuclei than healthy cells and can be distinguished using a variety of optical techniques, however, this process is complicated when the fraction of malignant cells is extremely low. As such, high-precision detection requires highly accurate measurements of cell confluence and the ratio of healthy to cancerous cells. Techniques such as machine learning and Fourier analysis have been used to auto-segment cells in microscopy images. However, these techniques often lack a ground truth standard to validate the segmentation results. We present a methodology for producing agarose tissue phantoms embedded with mixed polystyrene microbeads of varying diameters. These phantoms were imaged using a 2D translational stage and a microscope camera, collecting hundreds of images that were input to an artificially intelligent neural network for training and classification. The ability of this binary classifier to identify and quantify micro-beads in the images was assessed by comparing the automated results to manual counts, producing accuracies above 90% for bead sizes ranging from 50-200 microns. Auto-segmentation results will also be presented for mixtures of micro-beads and U-87 (glioblastoma) cancer cells, which differ in shape and morphology from the beads but whose boundaries are significantly less defined. The ability to accurately segment two different cell types in vitro would be highly beneficial for future cellular imaging studies.
Faculty Advisor: Hart, Vern (Science, Physics)
Recent efforts in early cancer detection require identifying the disease at a cellular level, by distinguishing cancer cells from healthy cells at low concentrations (<0.1%). Cancerous cells typically have larger nuclei than healthy cells and can be distinguished using a variety of optical techniques, however, this process is complicated when the fraction of malignant cells is extremely low. As such, high-precision detection requires highly accurate measurements of cell confluence and the ratio of healthy to cancerous cells. Techniques such as machine learning and Fourier analysis have been used to auto-segment cells in microscopy images. However, these techniques often lack a ground truth standard to validate the segmentation results. We present a methodology for producing agarose tissue phantoms embedded with mixed polystyrene microbeads of varying diameters. These phantoms were imaged using a 2D translational stage and a microscope camera, collecting hundreds of images that were input to an artificially intelligent neural network for training and classification. The ability of this binary classifier to identify and quantify micro-beads in the images was assessed by comparing the automated results to manual counts, producing accuracies above 90% for bead sizes ranging from 50-200 microns. Auto-segmentation results will also be presented for mixtures of micro-beads and U-87 (glioblastoma) cancer cells, which differ in shape and morphology from the beads but whose boundaries are significantly less defined. The ability to accurately segment two different cell types in vitro would be highly beneficial for future cellular imaging studies.
Raman Imaging of Single Cellular Metabolism
Ballantyne, Eliza; Buck, Lance; Cox, Zach; Adams, Brittney; Trappett, Matthew; Shipp, Dustin (Utah Valley University)
Faculty Advisor: Shipp, Dustin (Utah Valley University, Physics)
Understanding how cells metabolize the chemicals around them on a single cellular level is paramount to analyzing the effectiveness of pharmaceutical drugs. Discrepancies between pharmaceutical drug results during lab testing versus in actual patients are an expensive and time consuming obstacle. These differences could be alleviated using Raman spectroscopy by testing based on an overall chemical map instead of individual factors. Raman spectroscopy has great potential to aid this process because of its ability to present a chemical fingerprint of an entire cell without interfering with the cell's natural responses to chemical changes.
Using Raman spectroscopy to develop an additional method for observing cell metabolism will enhance understanding of cell function and advance studies focused on the results of chemical effects on cells in vivo. As a step toward this goal, this project is currently focused on obtaining time-lapsed Raman images of glucose uptake. Using glucose metabolism, we are able to model a system for more complicated pharmaceuticals. This study has explored methods for collecting Raman spectra in vivo, balancing time-dependent data collection with the time-constraint of working with living and changing cells. Raman spectra describing the chemical makeup of glioblastoma cancer cells as they metabolize glucose were analyzed and used to create time-lapsed images during uptake.
Our process presents a new lens for understanding cell metabolism and a potential tool for analyzing an additive's effect on a single-cellular level. We developed a platform and method for measuring chemical changes in cells over time. Next stages for this research include observing how metabolism varies depending on what additives are used for uptake and quantifying metabolic differences between types of cells.
Faculty Advisor: Shipp, Dustin (Utah Valley University, Physics)
Understanding how cells metabolize the chemicals around them on a single cellular level is paramount to analyzing the effectiveness of pharmaceutical drugs. Discrepancies between pharmaceutical drug results during lab testing versus in actual patients are an expensive and time consuming obstacle. These differences could be alleviated using Raman spectroscopy by testing based on an overall chemical map instead of individual factors. Raman spectroscopy has great potential to aid this process because of its ability to present a chemical fingerprint of an entire cell without interfering with the cell's natural responses to chemical changes.
Using Raman spectroscopy to develop an additional method for observing cell metabolism will enhance understanding of cell function and advance studies focused on the results of chemical effects on cells in vivo. As a step toward this goal, this project is currently focused on obtaining time-lapsed Raman images of glucose uptake. Using glucose metabolism, we are able to model a system for more complicated pharmaceuticals. This study has explored methods for collecting Raman spectra in vivo, balancing time-dependent data collection with the time-constraint of working with living and changing cells. Raman spectra describing the chemical makeup of glioblastoma cancer cells as they metabolize glucose were analyzed and used to create time-lapsed images during uptake.
Our process presents a new lens for understanding cell metabolism and a potential tool for analyzing an additive's effect on a single-cellular level. We developed a platform and method for measuring chemical changes in cells over time. Next stages for this research include observing how metabolism varies depending on what additives are used for uptake and quantifying metabolic differences between types of cells.