Chemistry
Presenter(s): Bryn Merrill
Advisor(s): Dr. Jerry LaRue
Catalysis provides pathways for efficient and selective chemical reactions through the lowering of energy barriers for desired products. Gold nanoparticles (AuNP) show excellent promise as plasmonic catalysts. Plasmon resonances are oscillations of the nanoparticle electrons that generate energetically intense electric fields and rapidly decay into energetically excited electrons. The excited electrons have the potential to destabilize strongly bound oxygen atoms through occupation of accessible anti-bonding orbitals. Tuning the anti-bonding orbitals to make them accessible for occupancy will be achieved by coating the AuNP in a thin layer of another transition metal, such as ruthenium, silver, or platinum, creating a bimetallic nanoparticle. We will initially study the carbon monoxide (CO) oxidation reaction, where the oxygen species is strongly bound and limits reactivity, in the presence of ruthenium-gold bimetallic nanoparticles (Ru-AuNPs). The bond between oxygen and ruthenium is typically strong, which inhibits reaction rates. Excited electrons from the AuNPs can transfer to the oxygen-ruthenium anti-bonding orbital. Electrons occupy the anti-bonding orbital, weakening the bond between the atomic oxygen and the Ru-AuNPs and making the atomic oxygen much more reactive. We will be studying the physical and chemical characteristics of the synthesized Ru-AuNP catalysts with spectroscopic and microscopic techniques including: UV Vis spectroscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM).
Designing and Building a Surface Science Ultra-High Vacuum (UHV) Chamber
Presenter(s): Tiffany G. Vallejo, Kevin Alvarado, Devon Ball, Barbara Carpenter, Jason Yoon
Advisor(s): Dr. Jerry LaRue
Catalysts increase the efficiency and selectivity of a wide range of chemical reactions by lowering the activation energy barriers for desired reaction pathways. This results in decreased energy usage and wasteful byproducts, helping to reduce pollution. Reactions on metal catalysts can involve various reactions at the same time, making them difficult to study. To study the fundamental processes of individual reactions, we need to isolate them using an ultra-high vacuum (UHV) chamber. Ultra-high vacuum chambers are capable of generating atomically clean environments, limiting potential surface contaminations. Standard UHV components include an ion gun, quadrupole mass spectrometer, ion gauge, and turbomolecular pumps. Overall, the UHV chamber configuration provides an ideal high vacuum environment to generate the proper conditions to study the reaction steps in the molecular interactions on the metal surface. Therefore, we will design and build a surface science UHV chamber to provide the ideal conditions needed to investigate the surface reactions at the gas-surface interface in heterogeneous catalysis. When completed, this UHV chamber will offer an opportunity to study methanol decomposition on platinum, an industrially important heterogeneous reaction.
Large-Area Thermoregulatory Material Inspired by Cephalopods
Presenter(s): Christopher Moore
Advisor(s): Dr. Matthew Gartner
Inexpensive, large-area thermal management is desirable for the operation of many modern
technologies including smart clothing, electronic circuits, building cladding, and outdoor equipment to control heat flow. Inspired by the space blanket and the dynamic skin of cephalopods, we have demonstrated a large-area, highly uniform, low-cost nanostructured material with tunable thermoregulatory and infrared properties. We have implemented scalable nanofabrication processes to achieve a material with an area greater than 500 square cm, modulating a 40% change in infrared transmittance and reflectance, and a dynamic environmental setpoint temperature window of approximately 8 degrees Celsius. Due to characteristics of scalability and associated figures of merit, our material affords new scientific and technological opportunities not only for adaptive optics and thermoregulation but also for any platform that would benefit from dynamic tunability of infrared radiation and thermal energy.
Incorporating Docking Scores and Selectivity Measures of Tyrosine Kinases to Develop Novel Tyrosine Kinase Inhibitors via Machine Learning
Presenter(s): Robby Jones
Advisor(s): Dr. O. Maduka Ogba
Understanding TKs (tyrosine kinases) are of paramount importance in the oncogenic world of chemistry and biology. Specific mutations of TKs results in their primary function, catalyzing ATP transfers from one molecule to another, to become unregulatable and cancerous. These kinases account for a wide variety of cancers throughout the body, and they have been the targets of many cancer treatments via TKIs (tyrosine kinase inhibitors). While many tyrosine kinases have effective inhibitors, there are over 90 TKs in the human body, and there are many that have no marketable TKIs. Furthermore, TKI generation has notoriously taken decades in physical labs. With the ever-accelerating development of computational power, machine learning can generate new molecules, including TKIs. Chemical discoveries via machine learning has invented new drugs already, including a novel TKI for DDR1 kinase in 46 days. Even more promising is that this process can find other TKIs without many radical changes. Due to the scope of the project, the goal of this research is to incorporate docking scores and selectivity measures to aid the machine learning process. The software Glide will be used to obtain this information. Preliminary analyses have demonstrated Glide’s ability to generate a pharmacophore hypothesis for TKIs. Using real-world TKIs, the machine can determine their bioactive aspects from the pharmacophore hypothesis, docking scores, and selectivity measures. This information will be used to generate novel molecules, all of which have the bioactive elements of the real world TKIs. The resulting computationally determined TKIs may yield a real-world applicant that could enter the market as an effective TKI.