Mentors: Prof. Jean Chung and Prof. Nancy Levinger

Project Description: 

Biological membranes comprise a range of molecules, including phospholipids, glycolipids, sterols, as
well as proteins, and sugars, and how these molecules are arranged relates to the membrane physiology.
Many biologically active molecules, such as antimicrobial agents and anesthetics are known to work by
altering the spatial distribution of membrane components. Likewise, cryoprotecting molecules, like
DMSO and glycerol, which help cells survive freezing, may also affect the physical properties the cell
membrane. This project will explore whether cryoprotectants, like antimicrobial agents and anesthetics,
can shift the arrangement of membrane lipid by measuring the effect of DMSO and glycerol on the phase
separation behavior of a model membrane.

Description of REU Student Activities: 

• Prepare giant unilamellar vesicles (GUVs) as model membranes;
• Visualize GUVs using confocal fluorescence microscopy;
• Measure the miscibility transition temperature (Tm) of phase-separating GUVs;
• Determine the changes in miscibility transition temp Tm of GUVs exposed to increasing DMSO
  and glycerol concentrations.

Mentors: Prof. Garett Miyake and Prof. Megan Hill

Project Description: 

Photoredox catalysis is a powerful approach to efficiently synthesize molecules and materials through harnessing the energy from light to drive a chemical transformation. Furthermore, photoredox catalysis enables unique reaction pathways that allow for the synthesis of new chemicals in more direct routes. This project will focus on the development of organic photoredox catalysts and use of these catalysts in synthetic organic chemistry methodologies. 

Description of REU Student Activities: 

• Synthesis of organic photoredox catalysts
• Characterization of organic photoredox catalysts using spectroscopy and electrochemistry
• Use of photoredox catalysts to develop synthetic organic methodologies 
• Participate in Hill and Miyake group meetings

Mentors: Prof. Seonah Kim and Prof. Garett Miyake 

Project Description: 

Plastic waste is an urgent issue globally, but the diverse applications of modern plastics make addressing this waste challenging. Copolymers, in particular, present a challenging recycling problem due to their complex chemical and morphological properties, with few tools to predict copolymer properties in silico. In this project, we will explore copolymer properties through computations and experiments to improve recyclable copolymers. The REU student will gain valuable experience in experimental polymer chemistry, using standard air-free methods and polymerization methods such as ROMP (Ring-Opening Metathesis Polymerization) to synthesize and characterize a wide variety of recyclable copolymers. The REU student will also use cutting-edge computational methods such as density functional theory (DFT) and machine learning (ML) to model real-world copolymers, focusing on property prediction via ML.

Description of REU Student Activities: 

• Building polymer property database and developing machine learning models 
• Recyclable polymer design and synthesis 
• Python programming and data science 
• Experimental polymer property measurements and analysis 

Mentors: Prof. Romana Jarosova and Prof. Debbie C. Crans

Project Description: 

Lithium is used to treat bipolar disorder, manic episodes, and prevent severe affective episodes and suicide, but its use has declined due to severe side effects and drug interactions. We aim to develop systems for slow-release Li-ion treatments and propose using the Zebrafish model to investigate their effects. To monitor Li levels, we will track ROS levels in Zebrafish as an indicator of Li-administration, alongside direct measurements. Our prior work shows carnosine affects ROS levels, and chronic lithium treatment reduces oxidative stress in cells. If time allows, we will explore two potential slow-release Li systems: one using graphite oxide quantum dots and another with a caged Li compound.

Description of REU Student Activities: 

• Developing zebrafish animal model system – Jarosova lab
• Developing ROS assay to monitor Li-levels in zebrafish – Jarosova lab
• Synthesis of Li-GQDs constructs using a known protocols – Crans lab
• Preparation of Li+- graphite oxide quantum dots – Crans lab
• Loading Li+ ions and derivatives on GQDs and titrating the binding and leaching of the drug – Crans/ Jarasova labs.

References 

Caruso, G.; Scalisi, E. M.; Pecoraro, R.; Cardaci, V.; Privitera, A.; Truglio, E.; Capparucci, F.; Jarosova, R.; Salvaggio, A.; Caraci, F.; Brundo, M. V. Effects of Carnosine on the Embryonic Development and TiO2 Nanoparticles-Induced Oxidative Stress on Zebrafish. Front. Vet. Sci. 2023, 10, 1148766.  

Bo Feng, Yaqiong Dong, Bing Shang, Bowen Zhang, Debbie C. Crans, and Xiaoda Yang “Convergent Protein Phosphatase Inhibitor Design for PTP1B and TCPTP: Exchangeable Vanadium Coordination Complexes on Graphene Quantum Dots” Adv. Funct. Mater. 2022, 32, 2108645 (1-14) DOI: 10.1002/adfm.202108645 

Mentors: Prof. A. Ronnie Banerjee and Prof. Debbie Crans

Project Description:

Vanadium compounds are potential pharmaceutical agents with anti-cancer and anti-diabetic activity; multiple approaches are currently underway to optimize the most promising candidates (such as [VO(HSHED)(DTB)] [1]) with optimal drug efficacy and potency.  We propose to enhance the delivery of the V-drugs through formation of V-graphene oxide quantum dots (GQDs) constructs, which have been found to be successful for other potential drug delivery systems [2]. The Crans group has already established that a more lipophilic environment around the metal centers stabilizes V-drugs from degradation upon administration [1]; forming a V-GQD construct is expected to improve bioavailability [2]. An added benefit of the GQDs is their tunable photoluminescence, which would harness an orthogonal imaging modality, making our V-GQDs multifunctional reporting entities [3]. The project would entail the synthesis of the V-coordination complex, preparation and characterization of the V-GQD construct, and evaluation of their enzyme inhibitory effect against phosphatases as a marker of their anti-neoplastic activity. 

Description of REU Student Activities: 

• Synthesis of [VO(HSHED)(DTB)] – Crans lab
• Synthesis of near-IR photoluminescent GQDs using a one-pot microwave protocol – Banerjee lab
• Titrating the [VO(HSHED)(DTB)] on the GQDs to make the V-GQDs constructs and testing their stability using fluorescence spectroscopy – Crans/Banerjee labs
• Characterization of the V-GQDs through one or more of the following techniques: Fluorescence and UV-Vis-NIR spectroscopy, X-ray photoelectron spectroscopy, and electron microscopy –Banerjee/Crans labs

Mentors: Prof. Megan Hill and Prof. Seonah Kim

Project Description:

This project introduces a machine learning method to predict the physical properties of dynamic polymer gels. Dynamic polymer gels are formed through crosslinking polymer chains with reversible, supramolecular, or dynamic–covalent bonds. The reversibility of the bond imparts desirable properties such as self-healing and viscoelasticity, which are valuable for applications like tissue mimics and drug delivery. Although the relationship between chemical crosslink properties (e.g., equilibrium and kinetic rate constants) and physical properties (e.g., stiffness and relaxation time) is known, predicting these properties across a wide variety of crosslink types remains a challenge. This project aims to overcome this by building a machine learning model using experimental data from various polymer networks. 

Description of REU Student Activities: 

• Experimentally measure the physical properties of a polymer network using rheometry (Hill) 
• Experimentally measure the chemical properties of a reversible crosslink using Isothermal Titration Calorimetry (Hill) 
• Database management and chemical descriptors development using Python packages like Pandas and cheminformatic packages like RDKit (Kim) 
• Develop a Graphical Neural Network (GNN) machine learning model to predict properties (Kim) 

Mentors: Prof. Megan Willis and Prof. Delphine Farmer

Project Description: 

Most of our lives are spent indoors exposed to a stew of potentially toxic compounds, ranging from nicotine adsorbed on smokers’ clothing to ozone or wildfire smoke transported inside from outdoor air to formaldehyde, which is both emitted from building materials and formed from ozone reacting with limonene, a key ingredient of personal care products and cleaners. While we know that ozone is harmful and limonene on its own is not, we don’t yet understand the fundamental chemical reactions that occur among these compounds and over time in the multiple interlinked phases of surfaces and air in the indoor environment. This REU opportunity will allow students to participate in data collection and analysis for collaborative indoor chemistry experiments using stable isotopic tracers to track the fate of oxidants and reactive organic carbon indoors.

Description of REU Student Activities: 

• Analysis of sorbent-tube samples by thermal desorption gas chromatography mass spectrometry, and analysis of resulting data
• Analysis of real-time trace gas data from indoor chemistry experiments
• Participation in collaborative indoor chemistry experiments  

Mentors: Prof. Jamie Neilson and Prof. Amy Prieto

Project Description:

To enable next-generation, beyond Li-ion batteries, we require new materials. For example, sodium is extremely abundant and inexpensive, and sodium ion batteries have the potential to replace lithium in myriad applications. However, we cannot simply replace Li with Na in existing materials used in litihium ion batteries. In this project, we are taking advantage of new synthetic solid-state chemistry to synthesize and test new materials for the development of sodium ion batteries.

Description of REU Student Activities:

• Synthesis of new ceramic materials using high-temperature furnaces.
• Characterization of materials using diffraction and electron microscopy.
• Construction of electrochemical cells
• Electrochemical testing of materials in battery half-cells.

Mentors: Prof. Matthew Shores and Prof. Anthony Rappe

Project Description: 

Spin crossover is an energetically balanced phenomena capable of measuring small binding energy differences. Metal podand complexes with pendant groups will be used to study the impact of solvent on intramolecular binding events including hydrogen bonding, halogen bonding, and lipophobic fluorocarbon binding. Students on this project gain exposure to all aspects of making, measuring, and modeling coordination complexes. A recent example of our long-term collaboration is depicted below. 

Description of REU Student Activities: 

• Synthesize and structurally characterize new coordination complexes
• Study the temperature dependence of the magnetism of these complexes
• Compute the structures and magnetic properties of these complexes

Mentors: Prof. Justin Sambur and Prof. Jamie Neilson

Project Description:

Transition metal oxide compounds are exciting alternatives to graphite for high-rate energy storage applications (e.g., power drills). Of all the candidate materials, “Wadsley-Roth” compounds with multiple transition metals (e.g., Mo, W, Nb, V) offer extremely fast charge/discharge rates and the ability to store more than 1 electron per transition metal site. However, these compounds contain “Wadsley defects” that likely trap charge, which diminishes the battery’s ability to store charge over time. This project aims to control the concentration and type of Wadsley defects in a series of niobium tungsten oxide compounds. 

Description of REU Student Activities:  

• Synthesize transition metal oxide compounds using a high temperature furnace and/or microwave. 
• Characterize the structure using X-ray diffraction and electron microscopy. 
• Construct Li-ion battery cells. 
• Characterize the charge-discharge performance of the anode materials. 

Mentors: Prof. Jeff Bandar and Prof. Eugene Chen

Project Description:

Organic superbases are neutral compounds that possess very strong basicity and display unique properties compared to traditional anionic bases. The Bandar and Chen Groups at CSU employ these bases as catalysts for the synthesis of bioactive small molecules and recyclable polymers with closed-loop chemical circularity, respectively. These protocols rely on commercially available superbases, but to improve this chemistry, the invention of new superbases is critical. Students involved in this project will be mentored by senior graduate students in the Bandar and Chen labs to accomplish this task. The research will involve the synthesis of new superbases, analysis of their basicity and optimization of their catalytic performance for the sustainable synthesis of pharmaceuticals and circular polymers.

Description of REU Student Activities:  

• This student will work with their mentors to design and prepare new superbases, then test and optimize their performance for catalyzing the synthesis of small molecules and polymers.
• Students will conduct daily laboratory work, including learning proper and safe techniques for reaction setup, reaction analysis, compound purification and the use of all associated instruments and equipment.
• Individual mentorship from senior graduate students on planning, conducting and analyzing organic synthesis experiments.
• Participation in and presentations at Bandar and Chen group meetings.
• Practice activities and preparation for public scientific presentations and writing.

Mentors: Prof. Seonah Kim and Prof. Rob Paton

Project Description:

This project introduces a machine learning (ML) methodology to improve the prediction of experimental chemical properties from a single reference molecular geometry. Traditional ML methods employed for chemical property prediction utilize a single reference geometry: a dataset of computed properties for a single geometry (typically the geometry at the energetic minima) is compiled, and an ML model is trained against these property labels. However, this methodology needs to consider that molecular geometries vary across the Boltzmann energy distribution, and the experimentally measured property is a weighted average of these values. Thus, the prediction results of current models are accurate when compared to theoretical computed values but fail to reproduce real-world experimental results accurately. For instance, the singlet lifetime of photochemical species is easily measured experimentally, but an ML methodology to predict singlet lifetimes has yet to be developed[1]. We will develop an ML model that encodes the geometric variability of molecules, allowing for more accurate predictions that better reflect experimental results.

Description of REU student activities:

• Implement a high throughput workflow to compute DFT energies for a dataset of molecular conformers (Paton)
• Database management and develop chemical descriptors using Python packages like pandas and cheminformatic packages like RDKit (Kim)
• Develop an Equivariant Neural Network (ENN) ML model to predict molecular properties (Kim)

[1] Kollenz, P.; Herten, D.-P.; Buckup, T. Unravelling the Kinetic Model of Photochemical Reactions via Deep Learning. J. Phys. Chem. B 2020, 124 (29), 6358–6368. https://doi.org/10.1021/acs.jpcb.0c04299.