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Understanding the relationships between chemistry students' motivation, performance, and gender can help identify and inform ways in which chemistry education might be improved. Students from four CHM 101 classes with two different instructors were surveyed using an adapted Science Motivation Questionnaire II, and motivation data was analyzed with respect to final course performance. Gender data was available for two of these classes, and motivation results analyzed by gender for these classes. Exam scores and gender data was obtained from one of the instructors for CHM 101 courses taught over the past five years and were also analyzed. The motivational study involved small sample sizes, especially in the motivation by gender study. Career motivation, grade motivation, self-efficacy, and total motivation declined over the course of the semester in the four classes combined. Self-efficacy and career motivation were found to predict final course performance only at the end of the semester. Self-efficacy strongly predicted performance, and career motivation was negatively correlated with performance. Female students had higher grade motivation at the end of the semester and lost more self-efficacy over the course of the semester than male students. Gender-performance analysis showed that male students scored slightly higher on exams on average, but that female students received a higher percentage of "A"s and a lower percentage of "D"s, "E"s, and "W"s in the majority of the semesters.
ContributorsJohnson, Walter Gregory (Author) / Gould, Ian (Thesis director) / Wolf, George (Committee member) / Austin, Ara (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor)
Created2015-05
Description
In materials science, developing GeSn alloys is major current research interest concerning the production of efficient Group-IV photonics. These alloys are particularly interesting because the development of next-generation semiconductors for ultrafast (terahertz) optoelectronic communication devices could be accomplished through integrating these novel alloys with industry-standard silicon technology. Unfortunately, incorporating a maximal amount of Sn into a Ge lattice has been difficult to achieve experimentally. At ambient conditions, pure Ge and Sn adopt cubic (α) and tetragonal (β) structures, respectively, however, to date the relative stability and structure of α and β phase GeSn alloys versus percent composition Sn has not been thoroughly studied. In this research project, computational tools were used to perform state-of-the-art predictive quantum simulations to study the structural, bonding and energetic trends in GeSn alloys in detail over a range of experimentally accessible compositions. Since recent X-Ray and vibrational studies have raised some controversy about the nanostructure of GeSn alloys, the investigation was conducted with ordered, random and clustered alloy models.
By means of optimized geometry analysis, pure Ge and Sn were found to adopt the alpha and beta structures, respectively, as observed experimentally. For all theoretical alloys, the corresponding αphase structure was found to have the lowest energy, for Sn percent compositions up to 90%. However at 50% Sn, the correspondingβ alloy energies are predicted to be only ~70 meV higher. The formation energy of α-phase alloys was found to be positive for all compositions, whereas only two beta formation energies were negative. Bond length distributions were analyzed and dependence on Sn incorporation was found, perhaps surprisingly, not to be directly correlated with cell volume. It is anticipated that the data collected in this project may help to elucidate observed complex vibrational properties in these systems.
By means of optimized geometry analysis, pure Ge and Sn were found to adopt the alpha and beta structures, respectively, as observed experimentally. For all theoretical alloys, the corresponding αphase structure was found to have the lowest energy, for Sn percent compositions up to 90%. However at 50% Sn, the correspondingβ alloy energies are predicted to be only ~70 meV higher. The formation energy of α-phase alloys was found to be positive for all compositions, whereas only two beta formation energies were negative. Bond length distributions were analyzed and dependence on Sn incorporation was found, perhaps surprisingly, not to be directly correlated with cell volume. It is anticipated that the data collected in this project may help to elucidate observed complex vibrational properties in these systems.
ContributorsLiberman-Martin, Zoe Elise (Author) / Chizmeshya, Andrew (Thesis director) / Sayres, Scott (Committee member) / Wolf, George (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
One very critical aspect of cell biology is the cytoskeleton. The cytoskeleton not only provides a strong foundation for the cell (Pegoraro et al., 2017), but it also allows for protein transport on its tracks that span long distances in cells (Löwe & Amos, 2009), specifically in neurons (Dent, 2017). Microtubules have a particular structure as polymers that are part of the cytoskeleton (Dent, 2017). Their components include alpha- and beta-tubulin dimers, and they have dynamic properties, such as polymerization and depolymerization (Dent, 2017). Concerning these dynamic properties and as will be discussed here, specific associated proteins can be useful in electrical signaling, neurodegeneration, and neurogenesis. In this review, I will review relevant findings on microtubule-associated proteins (MAPs), compare these to a prominent drug called taxol, and describe the significance of having a combination of MAPs in the brain. I will suggest that microtubules and their proteins form a critical geometric infrastructure that provides the framework for neuronal structure and function that contributes to more advanced cognitive processes, including consciousness.
ContributorsWilliamson, Elizabeth Paula (Author) / Coleman, Paul (Thesis director) / Mastroeni, Diego (Committee member) / Wolf, George (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
The purpose of this project was to compare the different physical models behind four algorithms in computational chemistry: Molecular dynamics with a thermostat (specifically simple velocity rescaling, Berendsen, and Nosé-Hoover), Langevin dynamics, Brownian dynamics, and Monte Carlo. These algorithms were programmed in C and the impact of specific parameters, such as the coupling parameter and time step, were studied. Their results were compared based on their radial distribution functions and, when the thermostats were in use, fluctuations in temperature.
ContributorsHemesath, Holly (Author) / Heyden, Matthias (Thesis director) / Sulc, Petr (Committee member) / Matyushov, Dmitry (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor)
Created2022-12
Description
Circular Dichroism (CD) and electron paramagnetic resonance (EPR) were used to investigate the metal-binding sites of five different four-helix bundles, which have slight differences in the population of their side chains. Of the four-helix bundles, three have central dinuclear metal binding sites; two of these three also have outer dinuclear metal binding sites. The other two peptides have two identical, non-central, dinuclear metal binding sites. The CD spectra showed changes in the secondary structure of the peptides, and X-band EPR spectra of these peptides revealed the unique four peak signal of Cu(II). These findings improve our understanding of the metal binding environments of these peptides.
ContributorsCanarie, Elizabeth Rose (Author) / Allen, James (Thesis director) / Wolf, George (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The relation between water and protein physics is a topic of much interest. Molecular dynamics (MD) simulations of biomolecules are a common computational technique to obtain atomistic insight into the physical behavior of biomolecules, including the nature of the interaction between water and the protein. In order to model biomolecules at the highest level of accuracy, an explicit, atomistic representation of the water is typically necessary. The number of water molecules that need to be simulated is normally on the order of thousands. The high dimensional MD dataset is then expanded with considerably more dimensions. We describe here a set of tools which can be used to extract general features of the water behavior, which can then be utilized to build simplified models of the water kinetics which make quantitative predictions, such as the flux rate through a pore.
ContributorsWelland, Ian (Author) / Beckstein, Oliver (Committee member) / Matyushov, Dmitry (Committee member) / Barrett, The Honors College (Contributor)
Created2015-12