Summer Undergraduate Research Fellowship Project Descriptions

Faculty Mentor: Cecilia Diniz Behn | Applied Mathematics and Statistics

Project Abstract: 

Analysis of synchronization provides key insights into the emergence of complex collective behavior from the dynamics of independent agents coupled to their noisy environment. Building on foundational work in classical systems such as neuroscience, current advances focus on understanding synchronization in minimal models of quantum systems where entanglement represents a novel form of coupling. In this work, the question of how network topologies affect synchronization in many-body systems will be investigated. Previous work has established that quantum systems show pockets of reduced noise in complex network topologies. Results from this work will establish how synchronization plays a role in quantum systems. In addition, simulations of open quantum systems with multiple network topologies have yet to be implemented on current noisy intermediate-scale quantum (NISQ) hardware, a promising application of current quantum computers. Therefore, these findings may inform the future design of systems in order to maximize coherence useful for quantum communication.

Student’s role and learning objectives: 

The student will learn more about network science, quantum applications, and mathematical analysis. Learning objectives will include
1. establishing models of quantum systems with different network topologies
2. explore existing metrics for quantifying aspects of complex systems and determine how to apply and/or adapt these metrics to networks of quantum systems
3. explore the role of synchronization in networks of quantum systems

The student will be part of an active research group and will collaborate with other undergraduates, graduate students, and the faculty mentor during weekly group and individual meetings.

The student will take an active role in designing network topologies for consideration and developing metrics to assess the effects of network topologies on the synchronization in many-body systems.

Faculty Mentor: Scott Strong | Applied Mathematics and Statistics

Project Abstract: 

Analyzing the mechanics and dynamics of one-dimensional subregions of physical space is a theme threading various mathematical models, e.g., vortex filaments in quantum liquids or airfoil mechanics, flagellar motion, phytoplankton bloom dynamics, protein structures, nonlinear response in stiff polymers, current-vortex filaments in heliophysics, glitches in neutron stars, and cosmic strings. The study of vorticity in ideal fluids teaches us that simplified expressions describing the local interactions between the one-dimensional filaments and their ambient space can be transformed into partial differential equations prescribing the evolution of the curvature and torsion of these strings.

In other words, the relatively complicated problem of nonlinear interactions between the filament and its environment reduces to scalar nonlinear evolution equations. While not entirely uncomplicated, this reduction in dimension gives us more direct access to the types of nonlinear transport and wave phenomenon one might look for in the wild. This research project focuses on the Euclidean cases and seeks to resolve the following questions:

1. For various prescribed flows what are the corresponding scalar partial differential equations?

2. Do these relationships provide us with any new or novel information about the corresponding model system?

3. Can we categorize flows based on this relationship and does this inform our thinking about the model systems?

We will accomplish this by cataloging the relevant phenomenon while reviewing concepts from vector calculus and differential geometry before conducting the necessary transformations to relate the associated ambient flows to scalar evolutions of the filament geometry, i.e., the Hasimoto map and its inverse. [1] The results will be documented and presented. Depending on interest and time, additional results may be pursued through either numerical simulation of the associated phenomenon and/or symbolic analysis of the scalar evolutions.

[1] Hasimoto, H. (1972). A soliton on a vortex filament. Journal of Fluid Mechanics, 51(3), 477-485. doi:10.1017/S0022112072002307

Student’s role and learning objectives: 

At a minimum, the student will need to have completed coursework in differential equations, vector calculus, and introductory physics. Preference will be given to students with a strong background in linear algebra and exposure to partial differential equations. Student learning objectives:

1. Gain an understanding of how to efficiently read through academic literature.
2. Learn to apply reflective techniques and record-keeping to support academic research practice.
3. Synthesize technical concepts relevant to the mathematical description of physical filaments.
4. Interpret boundaries of mathematical models and analyze relevant subparts.
5. Add to the current literature by extending existing models to include these mathematical techniques.

These will be accomplished through guided and structured self-study and weekly meetings that focus on practicing technical and administrative communication and planning. Technical writing/presenting will also be practiced with feedback provided.

Faculty Mentor: Karin Leiderman | Applied Mathematics and Statistics

Project Abstract: 

The main goal of this project was to build, implement, and test computational models of blood coagulation and the effects of combination anticoagulant and antiplatelet therapy. The project involved learning the basics of blood coagulation, working with and extending current mathematical models of blood coagulation, and using the models to investigate possible mechanisms underlying observed synergism in combination anticoagulation strategies. During the academic school year, the model was updated to study the effects of low doses of aspirin and low doses of direct enzyme inhibitors, alone, and in combination, in a mathematical model of coagulation under flow. Additionally, the hypothesized synergistic behavior was observed in the model results. However, there wasn’t time for exploration of the mechanism and thus it has not yet been determined. For the summer, the student will continue with the updated model, determine the mechanism underlying the synergy that was observed, and write up the results to submit for publication.

Student’s role and learning objectives: 

The student working on this project will gain experience building, implementing, and testing computational models of blood coagulation. The student will be expected to present the results in oral and written form with the goal of a publication being submitted by the end of the summer. I will meet with the student one-on-one, multiple times a week, to discuss research progress. Additionally, the student will be integrated into my weekly research group meetings where they will meet other undergraduate and graduate students working in mathematical biology. I will also work closely with the student on professional development topics, including scientific presentations and writing.

Faculty Mentor: Joseph Samaniuk | Chemical and Biological Engineering

Project Abstract: 

The goal of this project is to understand how 2D particles at fluid-fluid interfaces interact with phospholipid monolayers found in biology such as those formed by the lung surfactant dipalmitoylphosphatidylcholine (DPPC). There is an increasing number of 2D particles of varied chemistries, graphene being a common example, but their nanometer-scale thickness can lead to unique and little-investigated interactions with molecular films such as those found in the alveoli of lungs. Inhalation of graphene may lead to medical consequences from the interaction of the particles with the lung surfactants, and though the consequences will likely stem from their contact with phospholipid monolayers, those interactions have not been investigated experimentally.

Student’s role and learning objectives: 

In this project the student will work with a graduate student to measure particle diffusion in phospholipid monolayers and observe how monolayer structure is influenced by the presence of the particles. The student on this project will work with a PhD student to assist in fabricating 2D particles of well-defined shape and size, and to study their behavior at model lung surfactant interfaces. The student will need to work collaboratively, help to prepare experiments and materials, and assist in analyzing the data obtained from microscopy.

Faculty Mentor: Jose Delgado | Chemical and Biological Engineering

Project Abstract: 

The storge of fuel gases such as methane and ethane involves the formation of gas hydrates (GH). Gas hydrates are inclusion compounds in which small molecules are trapped in a hydrogen-bonding water network. They generally occur at high pressure and low temperatures. In multiphase systems where water is emulsified in an oil matrix, GH nucleates at water-oil interface. Therefore, the interfacial properties of the oil are key in predicting gas hydrates formation and hydrate inter-particle interactions. This project is focused on studying the interfacial properties of simple and complex oils and their correlation with the formation of gas hydrates as a practical method to store fuel gases.

Student’s role and learning objectives: 

The student will be performing experimental tests oriented to better understand the behavior of simple and complex oils when water is present in the system. Parameters such as interfacial tension, emulsion stability and surfactant adsorption at water-oil interfaces will be evaluated. Experimental techniques/setups will involve: Pendant drop tensiometer, bottle test to evaluate emulsion stability, and optical microscopy to measure droplet size and solid dispersion.
I will train the student in all the experimental techniques, and result interpretation. This implies that I will be working closely with the student to collect valuable information on new systems. In addition, this project will allow the student to obtain a strong understanding of surface/interfacial science applicable to different multiphase systems.

Faculty Mentor: Kevin Cash | Chemical and Biological Engineering

Project Abstract: 

Invertebrates are in a middle ground between in vitro and vertebrate model systems, where there aren’t as many tools available for imaging and physiological monitoring.

Pharmaceutical companies (one of the key purchasers of imaging and physiological monitoring tools) work primarily with vertebrate research models, and as such there are fewer customers to support development of tools for the invertebrate monitoring market. Our long-term goal is to develop tools for invertebrate research similar to those available for vertebrate monitoring, opening up research areas for smaller schools and universities like Mines.

Our goal in this project is to develop an affordable imaging system capable of monitoring fluorescent signals in invertebrates. This system would be valuable to the research community as a replacement for commercial animal imagers which are both overpriced and overdesigned for many invertebrate imaging needs (e.g., Perkin Elmer IVIS). We will use this system in our lab to assay nanosensor lifetime and function in vivo. One of the goals of this project is to make this accessible to other researchers, especially those at smaller institutions where they may not have access to commercial in vivo imaging systems.

Student’s role and learning objectives: 

In this project, the student will work to design, build, and test key components for this imaging system, including mounts for cameras, optical filters, circuit boards, LEDs, and other necessary components. It may also include design and 3D printing of other laboratory devices. Student will be mentored by the PI and a graduate student through group meetings and individual meetings as necessary.

Faculty Mentor:  Ramya Kumar | Chemical and Biological Engineering

Project Abstract: 

Continuous flow polymerization in tubular reactors have rapidly emerged as a powerful platform for generating large combinatorial polymer libraries efficiently and with
unprecedented precision and speed. In contrast to batch modes of operation, flow chemistry offers superior heat and mass transfer characteristics, which improves
reproducibility, safety, and control. By modulating multiple experimental parameters using automated tools, hundreds of chemically and architecturally complex polymers such
as multi-block copolymers can be synthesized within minutes. In this project, the student will lead the development and validation of a continuous flow polymerization platform with the goal of producing polycationic vehicles for intracellular delivery. Student will be involved in purchasing, assembling, and validating the set up by performing proof-of-concept studies. In this project, the student will precisely control the degree of polymerization and copolymer composition in a way that is not possible with batch modes of synthesis and proceed to study how subtle changes in polymer composition and length affect polymer properties such as pKa. The student will also help the PI design and implement outreach activities targeted at high school students.

Student’s role and learning objectives: 

Student will
Set up tubular flow reactors
Optimize process parameters to obtain desired molecular weight distributions and copolymer compositions
Compare reaction outcomes from batch and continuous flow approaches
Learn to execute RAFT reactions in tubular flow systems, purify, and characterize polymers
Correlate polymer pKa with molecular weight and composition
Assist mentor with outreach activities.

Faculty Mentor: Alex Pak | Chemical and Biological Engineering

Project Abstract: 

This research project aims to use molecular dynamics (MD) simulations to study the self-assembly of or metabolite permeation through bacterial shell proteins. The focus of this study is on bacterial microcompartments (BMCs), which are membrane-less organelles formed through self-assembly that consist of enclosed enzymes surrounded by a protein shell. Prokaryotic cells utilize BMCs for a range of metabolic processes, including carbon fixation, propanediol utilization, and ethanolamine utilization. As naturally occurring analogues, BMCs are intriguing platforms to study physical principles that can be adapted for bottom-up design of catalytic bionanoreactors, which have applications in bioremediation, water treatment, chemical fuel production, and medicine. The goal of this study is to understand how BMC shell proteins differentiate shell morphology or substrate permeation and to explore how modification of these proteins may manipulate BMC properties.

Student’s role and learning objectives: 

The student will read through the literature, assess possible protein-protein interactions, prepare and run simulations, prepare and run analysis code, and visualize simulations and analyzed data.

The learning objectives include:
• How to prepare, run, and analyze molecular dynamics simulations
• How to use high-performance computing resources and the Linux operating system
• How to manage computational workflows (Git, lab notebooks, scripts, etc.)
• How to code in Python and Bash
• How to critically read the literature
• How to prepare and deliver oral presentations

Faculty Mentor: Carolyn Koh | Chemical and Biological Engineering

Project Abstract: 

Gas clathrate hydrates are crystalline solids formed from water and small organic molecules (e.g., methane, carbon dioxide). The formation of clathrate hydrates is important for a range of applications, including energy storage as well as carbon sequestration. This project is focused on measuring and analyzing the mechanical properties and adhesion of clathrate hydrates, which are critical to ensuring these clathrate hydrate solids when formed in oil/gas pipelines do not act as projectiles that can rupture the line, which can lead to serious environmental and safety consequences. Conversely, understanding the mechanical properties of clathrate hydrates is also important for carbon capture when methane and carbon dioxide are captured and stored in the clathrate hydrate structure.

Student’s role and learning objectives: 

The undergraduate student will perform mechanical and adhesion interaction of clathrate hydrates for different surfaces and physical conditions, i.e., surfaces with varying roughness, chemistry, and physical conditions of temperature and solid aging (the solid grains will anneal and sinter during aging). The principles and hands-on procedures of clathrate hydrate synthesis and mechanical measurement techniques, and surface characterization (i.e., profilometry) will be acquired during the project via detailed training and mentoring. Daily and weekly meetings will be held with the mentor (Professor) and co-mentor (PhD grad student) to discuss the experimental design, hands-on measurements, and analysis of the results, as well as the application of the results to the different energy and carbon capture applications of clathrate hydrates.

Faculty Mentor: Sumit Agarwal | Chemical and Biological Engineering

Project Abstract: 

Atomic layer deposition ALD) is a technique used in semiconductor device fabrication for the growth of ultrathin conformal films in a layer-by-layer surface reaction controlled process. The rapid shrinking of semiconductor device dimensions has created a need for the low-temperature (≤400 °C) ALD of silicon nitride (SiNx) films. However, to date, the ALD of these films remains challenging. In this project, we will determine the surface reaction mechanisms during the ALD of SiNx using novel custom-designed precursor molecules obtained through our industry collaborators. Initially, our research focused on understanding a baseline SiNx ALD process that used alternating exposures Si2Cl6 and NH3 plasma. The film composition, reactive surface sites, and adsorbed surface species will be monitored using in situ attenuated total reflection Fourier transform infrared spectroscopy, which allows us to elucidate the reaction mechanisms that lead to film growth. In addition, in situ four-wavelength ellipsometry will be used to obtain the film thickness and refractive index. This fundamental science approach will allow us to screen the custom precursors from our industry collaborators and provide insight on how to design new ones.

Student’s role and learning objectives: 

The UG students would be expected to work in the lab for a period of ~12 weeks. The UG student will meet with the research advisor every week to review the experiments and plan for the week ahead. The student will be trained and advised by a graduate student. The student must also undergo all safety training protocols prior to starting any independent experiments.

Faculty Mentor: Ahmad Atif Abdul Majid | Chemical and Biological Engineering

Project Abstract: 

Gas hydrates are crystalline solids that trap small gas molecules (e.g., carbon dioxide and methane) inside hydrogen bonded water cages. The composition of the gas trapped in the hydrate phase can be different from that in the composition of the waste gas (the gas at which the gas hydrates are formed). This is the basis of gas separation using gas hydrates. The project will study the feasibility of utilizing hydrate technology for gas separation. There are still underlying challenges such as improving the kinetics of hydrate formation and reducing the stochasticity of hydrate nucleation. This work will study the proof of concept of hydrate separation using gas hydrates and address the challenges of using this technology.

Student’s role and learning objectives: 

Student will conduct the following tasks while conducting the project
1. Perform thermodynamic simulations to determine optimum experimental condition
2. Perform lab work (generation of lab data)
3. Perform detailed analysis of lab experimental data
4. Generate conclusions based upon lab results

Faculty Mentor: Andrew Herring | Chemical and Biological Engineering

Project Abstract: 

The production of green hydrogen from renewable electrons from wind or PV resources is a growing area of world wide interest. Anion exchange membranes (AEMs) allow water electrolysis to occur under basic conditions using flowing carbonate electrolyte. This facilitates the use of non-precious metals on the anode and low platinum landings on the cathode. Th electrodes for the AEM electrolyzer still need considerable optimization and scientific understanding so that we can maximize both performance and durability. The interaction of the ion conducting polymer, the ionomer, with the catalyst and electrically conducting support is crucial to the electrodes development. In our laboratory we have developed a library of ion conducting polymers that have varied chemistry and morphology that we can use to answer the question concerning electrode operation and with the use of this knowledge achieve practical current densities and durability for low cost AEM electroayzers. Such research will accelerate the use of green hydrogen in the world wide economy.

Student’s role and learning objectives: 

The student will be responsible for constructing electrodes and testing their performance by linear sweep voltametry or cyclic voltametry in a separated electrode apparatus. The student will be responsible for fabricating membrane electrode assembles from the best performing electrodes and testing their performance and durability in a 5 cm2 electrolyzer.
The learning objectives are that the trident will understand the material synthesis challenges of electrode fabrication and testing. The student will learn how to make reproducible electrochemical measurements and the student will learn how to make small electrochemical devices and evaluate their performance and durability.

Faculty Mentor: Anuj Chauhan | Chemical and Biological Engineering

Project Abstract: 

Hydrogels are networks of crosslinked polymers that have high water absorption capacity. They are used in the field of medicine for wound dressings, as contact lenses for vision correction, in the food industry for moisture absorption and antimicrobial activity to extend shelf life, in agriculture to control the soil’s water hydration and delivery of fertilizers, and in cosmetics for hygiene and skin hydration. In research, hydrogels are investigated in the fields of tissue engineering and drug delivery.
The high water absorption of hydrogels gives them great utility in loading therapeutics which may be released in a controlled manner. Commercial contact lenses are made from biocompatible, synthetic polymers of silicone or methacrylate. The properties of these hydrogels are designed to provide comfort for the wearers while maintaining their durability and necessary properties for vision correction. The polymer matrix of lenses contains void spaces between the connected chains on the size order of a few nanometers, which is the typical size of small molecule drugs. Research studies show the applicability of many different commercial contact lenses capable of loading small molecule drugs in the porous matrix which then elute over time, providing a sustained drug release. By controlling the crosslinking and ratios of components in the synthesis of contact lenses, the polymer matrix may be tuned to increase pore size, which ultimately modulates water absorption and elastic properties of the gel.
In the field of pharmaceutics, biological therapeutics have been greatly advanced and have come to rival small molecule drugs in clinic to treat diseases and conditions. Biological therapeutics differ greatly from small molecule drugs in their properties and how they are produced. Namely, biologics are much larger molecules, are typically far more sensitive with shorter half-lives, and require expensive, highly controlled purification systems. In biopharmaceutical engineering, the manufacturing processes have come to be fairly well established. Numerous biologics have demonstrated great efficacy in clinical trials in treating different cancers, diseases, and eye conditions and have received FDA approval. Medicine has improved but there is still room for improvement in the treatment methods by tailoring the drug delivery and pharmacokinetics. Specifically, in the field of ophthalmic drug delivery, administration and delivery techniques for biologics is critical for the improvement in treatment and is undergoing ongoing investigation. The current standard for the administration of ophthalmic drugs is through eye drops, which give an imprecise dosage and overflow from the eye causing much of the drug to go to waste. Commercial contact lenses are microporous materials, making them suitable for small molecule drug loading, but incapable of loading large molecules of biologics. To utilize hydrogels for delivery of large biomolecules, hydrogels need to be made macroporous with pore sizes from one hundred nanometers up to microns. For the range of materials that are used to make hydrogels, the mechanisms by which they are formed, and the range in processing methods used to make them, further investigation must be conducted to understand more about processing conditions and the medical application potential of porous hydrogels.

Student’s role and learning objectives: 

In this project, students will learn about hydrogel synthesis materials and synthesis methods and processing. Specifically, they will become familiar with photoinduced and thermal-induced bulk polymerization of hydroxyethyl-methacrylate (HEMA). They will learn about hydrogel characterization techniques such as rheology, light transmittance, and permeability studies to measure hydrogel properties of modulus, optical clarity, and oxygen permeability. They will become familiar with and learn how to use the equipment for hydrogel synthesis and characterization.
Students will learn how to test hydrogel transport properties with model protein biologics as well as the analytical tools used to measure drug transport.
From this research experience, students will gain an understanding for synthetic hydrogel formulation and testing and experience in synthesizing hydrogels and designing hydrogel formulations. Students will determine if the experiments conducted can provide control in tuning pore size of HEMA hydrogels. Students will gain an understanding for synthetic hydrogel application in ophthalmics and drug delivery to the eye.

Faculty Mentor: Ramya Kumar | Chemical and Biological Engineering

Project Abstract: 

Problem and its significance:
Despite the ubiquity of flow cytometry in the non-viral gene delivery literature, we observe that manual analysis of flow cytometry datasets is inefficient, susceptible to operator bias, and consumes significant time. To ensure that manual gating on bivariate dot plots is reliable, it is typical to incorporate a slew of negative and positive controls, which guide the placement of gates and the identification of the target population (for instance, cells that have undergone editing and now express a fluorescence reporter). Typically, the cut-off for fluorescence intensities is manually selected using the characteristics of the density distribution in key controls and bivariate gating is sequentially implemented to identify cellular populations of interest (transfected, edited, internalized etc.). Although automated analysis approaches that ensure robust, reproducible, and reliable gating are already available, they remain either unknown or under-utilized by the biomaterials and gene delivery communities. In polymeric gene delivery, it is traditional to compare the transgene expression of a novel vehicle with a well-established commercial reagent through flow cytometry. These experiments are typically performed in triplicate and statistical tests of significance are performed to judge whether the novel vehicle exceeds performance benchmarks set by the commercial transfection reagent. In these contexts, errors introduced in gating and analysis can result in erroneous claims, especially if the novel vehicle outperforms the commercial vehicle by a hair’s breadth with statistically questionable p-value thresholds. These misleading conclusions that throw future researchers off-track and lure them into pursuing unproductive lines of investigation. Unlike in academic settings, automated tools and supervised learning methods are widely used in clinical settings where a high volume of flow cytometry is performed for diagnostic screening, patient immune profiling, and human clinical trials. This is undoubtedly motivated by the human costs of operator error in data interpretation.
Project goals:
The overall objective of this project is to develop a python pipeline for polymeric gene delivery experiments that renders the analysis of flow cytometry data transparent, robust, and reproducible.
1. We will complete a meta-analysis of papers published in polymeric gene delivery between 2020-21. How many publications provide detailed gating schemes and furnish the scatter plots?
2. We will assess analyst-to-analyst variability in processing flow data from a typical gene delivery experiment
3. We will directly compare the performance of supervising learning algorithms to manual gating or “gating by eye” approaches and discuss the main differences observed in a typical gene delivery study.
4. We will survey the computational pipelines best suited for datasets arising from gene delivery research questions and outline the advantages of the different computational methods tested.

Student’s role and learning objectives: 

Project steps:
1. Familiarize with Jupyter notebooks and Python environment (1-2 weeks)
2. Learn “manual” “gating by eye” using Flowcytometrytools (1 week)
3. Read literature on automating flow cytometry analysis (1-2 weeks)
4. Learn how to use flowDensity module for setting thresholds and identifying “positive populations” based on a negative control. (2-3 weeks)
5. Evaluate flowDensity on two or three datasets, compare results from “gating by eye” and automated gating. (3-4 weeks)
6. Quantify analyst-analyst variability in “gating by eye” and compare with automated gating
7. Meta-analysis of literature (2 weeks)

Student will apply Python scripting skills to develop an analysis pipeline that will help my group in interpreting flow data in a robust and reproducible manner.

Faculty Mentor: Alex Pak | Chemical and Biological Engineering

Project Abstract: 

This research project aims to use molecular dynamics simulations to study the assembly and binding of small peptides that selectively target viral structural proteins, thereby acting as therapeutic inhibitors. In particular, the focus of this study is on lung receptor-mimetic peptides that target SARS-CoV-2 spike proteins. Spike proteins are the key agents that mediate SARS-CoV-2 virion (i.e. viral particle) binding and entry into new cells. Therefore, inhibition of spike activity would reduce viral infectivity. By combining targeting peptides with self-assembling peptides, a multi-agent therapeutic may be designed with enhanced binding and efficacy. We will use computer simulations to understand how varying the self-assembling scaffold and/or the targeting peptide sequence mediate spike protein binding and inhibition.

Student’s role and learning objectives: 

The student will read through the literature, assess possible targeting peptides, prepare and run simulations, prepare and run analysis code, and visualize simulations and analyzed data.

The learning objectives include:
• How to prepare, run, and analyze molecular dynamics simulations
• How to use high-performance computing resources and the Linux operating system
• How to manage computational workflows (Git, lab notebooks, scripts, etc.)
• How to code in Python and Bash
• How to critically read the literature
• How to prepare and deliver oral presentations

The student will give brief updates over Slack once a week and have an extended meeting (joint with other group members in the same research area) with the PI every two weeks. The student will also participate in regular biweekly group meetings. The group members and PI will be accessible both in-person and via Slack.

Faculty Mentor: Amadeu Sum | Chemical and Biological Engineering

Project Abstract: 

This fundamental study aims to understand the molecular thermodynamics of aqueous electrolytes solutions by investigating how a new formulation based on the effective mole fraction can commonly capture the solution behavior of electrolyte systems. This research will involve the development of a complete model for both solvent and solute, testing/validation of the model, and simulations to understand the origin of this newly proposed fundamental quantity.

The effective mole fraction is a very simple yet very elegant approach to better describe the solution behavior of strong electrolytes in aqueous solutions. It suggests that the size and type of ions are not important, and only the charge fraction of ions in solution matters. This finding is counter to all prior knowledge on electrolyte solutions, but it appears that in reality there is a much simpler approach in describing the thermodynamics properties of single and mixed strong electrolytes.

Student’s role and learning objectives: 

Student will perform molecular simulation and analyze the results to understand the molecular properties and derive insight for correlating thermodynamics properties.

Student will learn about searching the literature, gathering data, analyzing data, developing thermodynamics models, and coding in Python.

Faculty Mentor: Stephanie Kwon | Chemical and Biological Engineering

Project Abstract: 

This project aims to design a catalytic system to efficiently store and release H2 molecules in form of chemical bonds in organic molecules. Specifically, formic acid (HCOOH) decomposition routes have emerged as a promising method to release H2 on demand for fuel cell applications. More recently, previous works have demonstrated that Au nanoparticles of subnanometer sizes (< 1nm) are very active and selective for HCOOH dehydrogenation catalysis, even at atmospheric temperature and pressure. Yet, the mechanistic details are not well-understood with conflicting mechanisms proposed in the literature. This work will combine kinetic, spectroscopic, isotopic, and theoretical methods to understand HCOOH dehydrogenation mechanisms involved in subnanometer-sized Au particles. We expect that the results of this study will provide a fundamental understanding of catalytic reactivity and selectivity of atomically dispersed Au catalysts, which will ultimately allow us to design reactive and selective catalytic systems for HCOOH dehydrogenation processes.

Student’s role and learning objectives: 

The student will synthesize nanomaterials for catalysts, characterize them, and test for HCOOH dehydrogenation catalysis. The project will involve hands-on experience in diverse research techniques in surface science, including infrared spectroscopy, mass spectroscopy, and gas chromatography. The student will gain experience in data analysis, kinetic analysis, and numerical methods.

Faculty Mentor: Christine Morrison | Chemistry

Project Abstract: 

One way to mitigate the ever-increasing concentration of carbon dioxide in the earth’s atmosphere is to develop carbon recycling catalysts. Such catalysts would capture and convert carbon dioxide waste from fossil fuels into value-added products. The Morrison group is exploring the viability of iron-sulfur clusters as potential carbon recycling catalysts. These catalysts offer several advantages, including being composed of earth-abundant elements and operating under mild conditions. In this SURF project, the student will synthesize and characterize a series of Fe-S cluster catalysts with unique ligand environments to understand how the molecular environment around the catalyst impacts its activity. This knowledge will contribute to our fundamental understanding of these catalysts and be used to design first-generation Fe-S carbon recycling catalysts.

Student’s role and learning objectives: 

The student’s role in this project is to establish a structure-function relationship that explains what structural and chemical features around Fe-S clusters enhance their CO2 reduction activity. This will be accomplished by synthesizing and characterizing a series of Fe-S cluster catalysts with different ligands and then assessing and comparing their CO2 reduction activity.

The student learning objectives are:
– Learn and become independent in synthesizing and characterizing Fe-S clusters
– Learn and become independent in assessing CO2 reduction activity of Fe-S clusters
– Use CO2 reduction assays to assess how the ligand environment around the Fe-S clusters impacts their activity

Faculty Mentor: Christine Morrison | Chemistry

Project Abstract: 

Iron-sulfur (FeS) clusters are ubiquitous in biology and are critical for many biological processes, such as electron transfer. FeS clusters form in a variety of sizes and shapes, including 2Fe-2S and 4Fe-4S clusters as well as larger clusters. These clusters are formed in several FeS cluster machineries, such as the ISC, CIA, NIF, and SUF pathways. The SUF pathway is unique to some bacteria and critical for their survival. In the Morrison lab, the FeS cluster biosynthetic pathway in bacteria is being investigated as a potential target for new drug development. Drugs against this pathway could be used to treat infections caused by Staphylococcus aureus, for example. In this SURF project, an undergraduate student will build off recent results obtained in the Morrison lab to explore how specific inhibitors interact with two proteins in the SUF pathway.

Student’s role and learning objectives: 

The student’s role in this project is to build off current work in the Morrison lab. We recently identified >200 compounds that interact with one protein in the SUF pathway. Due to the nature of these compounds and the SUF proteins, it is possible that these compounds also interact with a second protein in the pathway. The undergraduate student will explore how these compounds interact with both proteins.

The student learning objectives are:
– Learn and become independent in protein expression and purification
– Learn and become independent using in vitro activity assays to assess the activity of our proteins
– Use activity assays to assess how specific inhibitors interact with two proteins in the SUF pathway

Faculty Mentor: Alexandra Wayllace | Civil and Environmental Engineering

Project Abstract: 

Ingram Gulch, located in Boulder county, has been identified as an area highly susceptible to shallow landslides. A system to monitor and forecast instability mechanisms in the area is currently being deployed. The scope of this project is to characterize the mechanical (shear strength) and hydrological properties of the subsurface from minimally disturbed soil samples using specialized tests. The data obtained from this testing will then be used in a numerical model that will serve as the basis for the landslide early warning system.

Student’s role and learning objectives: 

The undergraduate student who works with us will:

1. Go to the field with a team of scientist from CSM and CGS to obtain soil samples

2. Perform direct shear tests under low normal stress conditions

3. Perform TRIM tests to obtain hydrological soil properties

4. Synthesize the data obtained.

Learning objectives:
The student will learn to:
1. Obtain “undisturbed” samples from the field
2. Performed specialized laboratory testing on saturated and unsaturated soils
3. Learn basic concepts of unsaturated soil mechanics.

Faculty Mentor: John McCray | Civil and Environmental Engineering

Project Abstract: 

The undergraduate researcher will work with Prof. McCray’s research group to collect and evaluate water quality data from the Front range in Colorado, mountain regions of Colorado that are susceptible to wildfire, and the arid high desert of the Arequipa region Peru. The team is collecting water samples from newly designed and installed green infrastructure near Cherry Creek in Denver to evaluate it’s ability to remove pollutants from Stormwater (project with Mines, City of Denver, and Geosyntech consulting firm). We are also installing a novel, portable, modular, green infrastructure in urban water channels in Golden Colorado and Sonoma County California (project with the Sonoma County Water Agency) to evaluate its ability to remove pollutants in Urban Drool (i.e., year-round dry weather flows). We are sampling streams in mountain watersheds to determine the effectiveness of “treatments” applied by the U.S. Forest Service (USFS) after a fire to improve water resources (project with USFS, funding pending from Colorado Water Center). Finally, we have collected and are mining water quality data in 5 watersheds in the high desert of southern Peru to understand current water quality problems in the region and determine their likely sources, as well as running models to predict how future changes in climate, land use, and population growth may impact water quality.

Student’s role and learning objectives: 

The student will conduct research with Professors John McCray and Pablo Garcia, and assist two PhD students and one Post-Doc in the McCray research group on several of these projects as follows:

1. Collecting and analyzing water quality samples in the field for the projects listed above.
2. Assisting in experiments to design innovative green infrastructure to improve water quality.
3. Conduct quantitative analysis on one of these projects, either to evaluate watershed water quality, and/or the effectiveness of innovative green infrastructure to improve water quality.

Student Learning Objectives;
1. Learn field methods and collect water quality samples for wet and dry weather for numerous pollutants
2. Learn to analyze samples for pollutant concentrations for one or more of the following: nutrients, metals, bacteria, dissolved solids, organics.
3. Become familiar with the operation and effectiveness of innovative green infrastructure designed to remove pollutants.
4. Analyze water quality data to determine pollutant source OR to evaluate effectiveness of green infrastructure
5. Present research results orally and visually (i.e., with a power point presentation to a group and / or a research poster)

Faculty Mentor: Amy Landis | Civil and Environmental Engineering

Project Abstract: 

Plastics have been playing a vital role in the present day industrialized economy, resulting in the growing amount of plastic waste in the environment. This project helps us identify sustainable solutions for the prevalent industry of plastics. We will not only look at end of life of plastics, but we will use sustainability tools like life cycle assessment, material flow analysis, and techno-economic analysis to look at manufacturing and see where we are using these plastics. If we know where we are using certain plastics, how much quantities, and then where they go, we can make design changes to the types of plastics that are used for certain applications and perhaps impact the total environmental impacts and sustainability of the system. We know some plastics get recycled, but most of them go through other waste management methods. One of the knowledge gaps is plastics lost to the environment, plastics lost directly to the ocean. This work can improve our understanding of where we might make design changes, material changes, or insert new types of plastics to improve plastics waste management and loss. Using these tools during the design phase while designing these new solutions ensures that we avoid unintended consequences and make truly sustainable decisions. All of this helps us to understand where circular economy opportunities lie, which is a focus in various system solutions research that tackle global challenges.

Student’s role and learning objectives: 

The objectives are to identify sustainable solutions for the prevalent industry of plastics by: 1) Conduct a literature review of recent material flow analysis (MFA) & life cycle analysis (LCA) plastic studies; 2) Develop an updated MFA of US plastics; and 3) Highlight areas for improvement and potential sustainable solutions based on literature review and updated MFA.

Students will learn or hone scientific research and writing skills, gain increased understanding of plastics material flows, develop skills in sustainability tools like material flow analysis (MFA) & Life cycle analysis (LCA), and gain increased understanding of circular economy sustainable solutions

The student will meet one-on-one, weekly or bi-weekly with LSRG graduate students and post doc (and Dr. Amy Landis as needed) for project planning and progress reports. The graduate student mentors will also be available to the student throughout the fellowship to answer questions and assist with technical skills development. When available, the student will also join weekly LSRG meetings to participate in professional development activities

Faculty Mentor: Reza Hedayat | Civil and Environmental Engineering

Project Abstract: 

Mine tailings are leftover materials from the mining operations that, in addition to heavy metals and contaminants, can contain valuable components. Our current research has shown great promise in converting tailings into valuable cementitious materials through the Geopolymerization process. The geopolymerization involves alkali-activation of the existing aluminosilicates in tailings to produce high strength and durable concrete. The main goal of this summer undergraduate research project is to evaluate the mechanical and durability characteristics of geopolymerized tailings for construction applications.

Student’s role and learning objectives: 

Student will be working with graduate student mentors and faculty in exploring the effect of different activators in production of geopolymer concrete from different sources of mine tailings. Student will learn a suite of laboratory characterization techniques and will perform strength and durability tests for evaluation of the properties of the produced materials.
The research activities for the undergraduate researcher will be as follows: (a) review and finalize the specific research objectives in collaboration with the faculty mentor; (b) create geopolymerized concrete in the laboratory; (c) conduct strength and durability tests on the produced specimens; (d) evaluate the test results in collaboration with the faculty mentor; and (e) prepare research manuscript for dissemination of the research findings in a conference.

Faculty Mentor: Shiling Pei | Civil and Environmental Engineering

Project Abstract: 

As an NSF/USDA/industry-funded research effort to develop and validate seismic design method for resilient tall mass timber buildings, the NHERI TallWood Project is planning on a landmark shake table test of a full-scale 10-story mass timber building in San Diego CA this coming Fall. This test will be conducted at UCSD’s newly updated 3-Dimensional LHPOST outdoor shake table facility. Specifically, the 10-story test building was designed for to withstand Design Basis Earthquake without structural damage and have minimal down-time in Maximum Considered Earthquakes. The test will also include non-structural walls and façade in order to truly evaluate building down-time related to the damage and repair of these systems. Based on current schedule, the construction of the building will start in May 2022 and the test will begin in September 2022. More information about the TallWood Project can be found at https://nheritallwood.mines.edu.
The project is looking for undergraduate students to help on the construction and instrumentation of the test building during summer 2022. This project involves traveling and staying at the UC San Diego shake table site and work with the PI and a team of graduate students and other undergraduate students. Because the building has 10 stories without elevator, the student need to feel comfortable working on a construction site and climb stairs.

Student’s role and learning objectives: 

The student will assist on preparation of a full-scale 10-story mass timber building for earthquake testing at the world’s largest out-door shake table in San Diego CA. The student will work with a group of researchers, graduate students, and undergrad research students from other universities to study and document the construction process of the 10-story building and produce multi-media representation of the process. Depending on the interest of the student and project needs, the PI will mentor the student on specific research topics related to fundamentals of mass timber design and construction, benefit of off-site fabrication, and carbon benefit of mass timber structures.

Faculty Mentor: Gabe Fierro | Computer Science

Project Abstract: 

Modern buildings contain networked monitoring and control systems that possess thousands of data streams providing insight into the building’s operation. There are many potential uses of these data, including fault detection, energy efficiency analysis, and intelligent control algorithms. However, this data is difficult to use because it is often poorly labeled and lacks “metadata” which allows us to understand what the data means, where it comes from, or even what its engineering units are.

The goal of this project is to investigate, develop, and evaluate algorithms for classifying data streams from “smart buildings” so that they can be more easily used in data analytics programs. Data streams will be classified according to the Brick Ontology (https://brickschema.org/), an open-source metadata standard being developed at Mines and NREL. The project will involve learning and implementing time series classification techniques from the literature, resulting in the publication of an open-source project that can be used by a larger research community.

Student’s role and learning objectives: 

The student will work closely with Dr. Fierro — meeting at least once a week — to discuss progress, research ideas, and co-develop the algorithm and eventual software package. By the end of the project, the student will be able to describe a family of timeseries classification techniques, how they work, and when they are appropriate to use. The student will also develop a familiarity with the Brick ontology and other graph-based metadata schemes for IoT and smart building data. Finally, the student will develop their own algorithm for timeseries classification and produce an evaluation against leading techniques from the literature.

Faculty Mentor: Hua Wang | Computer Science

Project Abstract: 

This project seeks to harness the opportunities of creating large-scale, principled computational strategies and effective software tools to reveal sophisticated relationships among heterogeneous brain data including genetic variations, multi-dimensional and longitudinal quantitative phenotypes, neural circuits, and outcomes, and addressing critical big data mining issues of scalability, efficiency, dimensionality, heterogeneity, complexity, and interactive visual exploration in order to realize the full potential of the data. Given the massive genomic, imaging, and other phenotypic data sets available to us and our rich expertise in integrating neuroimaging and genomics, neuroinformatics is an ideal innovative application domain for the development, application, and validation of the proposed big data mining framework. Massive continuous phenotypic measures from neuroimaging data, fluid biomarkers, and cognitive scores have the potential to serve as useful traits intermediate on the chain of causality from genes to phenotypic outcomes. In this project, we will study principled and large-scale data mining models, coupled with a rigorous theoretical foundation, data-intensive computing, and interactive visual exploration, to conduct the first comprehensive and integrative study of imaging genomics and connectomics. The success of this project on big data research will greatly support the BRAIN Initiative which has become a national goal and has been unveiled by the U.S. Government on research effort to revolutionize our understanding of the human brain.

Student’s role and learning objectives: 

Together with the graduate students in the research team, the undergraduate students will perform the following research tasks:
1. implement novel large-scale non-convex sparse learning algorithms for identifying genetic risk factors from multiscale imaging genomics data;
2. to better capture the underlying gene-to-QT mechanism, identify the genetic markers with biological structures from massive genome-wide SNP data;
3. investigate the large-scale non-convex sparse learning models via providing linear convergence optimization algorithms for big data feature selection.

Faculty Mentor: Hua Wang | Computer Science

Project Abstract: 

Breast cancer is a type of cancer that develops in breast tissues, and, after skin cancer, it is the most commonly diagnosed cancer in women in the United States of America. Given that an early diagnosis is imperative to prevent breast cancer progressions, many machine learning models have been developed to automate the histopathological classification of the different types of carcinomas in recent years. In this study, we how to classify the histopathological breast cancer images and determine which tissue segments in an image exhibit an indication of using large-scale histopathological images, where we mainly address the computational efficiency of designed machine learning models.

Student’s role and learning objectives: 

1. The students will learn the skills to perform data processing and management.
2. The students will be involved in my research team to perform research on machine learning and data mining.
3. The students will be involved in scientific paper writing for the results of this project.
4. The students will have a chance to work together with my collaborators in medical schools.
5. The students will gain fundamental knowledge on medical image computing, as well as how to use machine learning, as well as computational algorithms, to deal with problems in medical image computing.

In a word, after the training in this project by successfully completing the assigned research tasks, the student is expected to be ready for pursuing a graduate degree in the area of machine learning, data mining, artificial intelligence, or a broader area of computer science.

Faculty Mentor: Hua Wang | Computer Science

Project Abstract: 

This project will develop innovative machine learning-based approaches for predicting prospective areas of exploration on regional scales and for exploration targeting district scales. The project aims to (1) implement and develop new machine learning approaches for integrating large-scale exploration data including geology, geochemistry, and geophysics to identify highly prospective areas for both greenfield and brownfield exploration; (2) devise computational and statistical algorithms to predict and identify high-potential target areas (along with estimated uncertainty associated with this prediction) at the prospecting stage to allow designing an optimal drilling program; and (3) investigate the use of machine learning and spatial statistical techniques in automated interpolation and interpretation of drilling data for generating 3D ore body shapes in resource quantification.

Student’s role and learning objectives: 

1. The students will learn the skills to perform data processing and management.
2. The students will be involved in my research team to perform research on machine learning and data mining.
3. The students will be involved in scientific paper writing for the results of this project.
4. The students will have a chance to work together with my collaborators in medical schools.
5. The students will gain fundamental knowledge on medical image computing, as well as how to use machine learning, as well as computational algorithms, to deal with problems in medical image computing.

In a word, after the training in this project by successfully completing the assigned research tasks, the student is expected to be ready for pursuing a graduate degree in the area of machine learning, data mining, artificial intelligence, or a broader area of computer science.

Faculty Mentor: Atef Elsherbeni | Electrical Engineering

Project Abstract: 

This project aims toward the design of a compact two-dimensional microstrip patch array for use as a feed source for a larger reflector antenna.
The project will start with the design of a single element patch antenna, followed by the analysis of an array characteristics formed of the one antenna element design. When the array configuration is decided on, the study of a feeding network will start, and a combined structure of the feeding network and the array will be simulated to check the overall performance. This will be followed by fabrication and testing the array performance. Fabrication will be conducted in the EE department EDC lab and the testing will be conducted in the ARC and chamber EE labs. The substrate material and connectors for this project are available and will be provided by the mentor of the project.

Student’s role and learning objectives: 

This project will involve several stages of learning and gaining experience leading to an end product that will be used in a bigger project. Her are the phases of this project:
– Learning the basics of printed antenna design, with emphasis on patch antennas
– learning how to design a feeding network for a planar array of antennas with predefined phase shift
– Learning how to conduct antenna simulations
– Learning how to use Matlab to generate DXF and Gerber files for direct milling on LPKF machine
– learning how to mill antennas and feeding network on the PLKF EE department milling machine
– learning how to perform reflection coefficient and radiation pattern measurements of antennas and array in the ARC and EE chamber labs.
– learning how to write a professional technical report.

The mentor will work with the student in every stage of this project. Helpful documents and codes will be provided. Interaction with graduate students in the ARC lab will be a great experience for the undergraduate student education.
The mentor will conduct at least one-to-one project status update meetings every week to review, discuss and provide guidance for future work.

Faculty Mentor: Michael Wakin | Electrical Engineering

Project Abstract: 

Probabilistic, or randomized, algorithms play a key role in many domains such as signal processing, machine learning, and numerical linear algebra. They are especially useful in big data applications when classical algorithms are intractable due to space and computational complexity constraints. A key feature of randomized algorithms is that instead of directly computing a desired result from the given data, they approximate a solution using only a portion of the very large amount of available data. This gives us much faster algorithms and cheaper solutions to large-scale problems. When analyzing randomized algorithms, we are concerned with what conditions reduce the probability of a bad approximation in addition to classical questions around computational complexity. In this SURF project, the student will choose a randomized algorithm and will aim to produce stronger probabilistic and/or computational guarantees for the chosen algorithm. The student can choose from, but is not limited to, the following applications: numerical linear algebra (e.g., matrix approximation), kernel-based approximation, compressive sensing, and low-dimensional models for high-dimensional data analysis. Finally, the student will devise numerical experiments to empirically validate their developed theoretical bounds.

Student’s role and learning objectives: 

The student is expected to commit full-time (40 hours/week) to the project. At the start of the project, they are expected to have narrowed down a list of potential algorithms/applications they would like to work on and be familiar with the current state of research in that niche. They will meet with Prof. Wakin twice a week to discuss the progress they made up to that point and to ask any questions they may have moving forward. Prof. Wakin will provide them with feedback and suggest supplementary material or other guiding research avenues should the student get stuck. As appropriate, the student will also interact with graduate students and/or other collaborators. The student’s main objective is to get a sense of the research process and to get familiar with state-of-the-art tools for the analysis of randomized algorithms. Student qualifications: Background in probability theory (EENG 311, MATH 334 or MATH 534), scientific computing (MATH 307), and vector spaces (EENG 515 or MATH 500).

Faculty Mentor: Katharina Pfaff | Geology and Geological Engineering

Project Abstract: 

Diamond drill core and drill cuttings represent the most important sources of subsurface geological information in the minerals and geothermal industries. Large amounts of drilling are typically conducted as projects in the minerals and geothermal industries advance from the early exploration to the production stage. Continuous XRF scanning on core is a newly developed method available at Mines which will allow the successful SURF student(s) to obtain images and topography data as well as co-registered geochemical information. These co-registered datasets will be augmented with mineralogical information acquired in the Mineral and Materials Characterization Facility in the Department of Geology and Geological Engineering to be used for subsurface modeling and visualization endeavors using Leapfrog. The aim of this project is to help develop a workflow that can be implemented in research and industry to characterize and visualize the subsurface.

Student’s role and learning objectives: 

The selected SURF student(s) will be closely working with PI Dr. Pfaff and collaborators Drs. Monecke and Tharalson and graduate student assistants. The student(s) will participate in research group meetings and meetings with university and industry stakeholders. The student will be trained in sample preparation, data acquisition, data interpretation, and subsurface modeling. The student(s) will participate in discussions and present their findings to their mentors.

The research project will be subdivided into three general milestones with discrete goals:

(I) Learning: Working in an analytical laboratory (including safety training), sample preparation, data acquisition, quality control, organizational skills, and literature review

(II) Learning/Analysis: Achievement of information literacy, data analysis, critical thinking, training of effective communication skills, data acquisition and interpretation

(III) Analysis/Synthesis/Presentation: Data interpretation, critical thinking during data analysis and quantitative reasoning skills, data presentation – quantitative and scientific reasoning, effective communication skills and presentation of research outcomes.

Faculty Mentor: Kamini Singha | Geology and Geological Engineering

Project Abstract: 

In 2015, the Animas River in Durango, Colorado turned bright orange. During an attempt to clean up an old mine in the river’s headwaters, the Environmental Protection Agency (EPA) accidentally released 3 million gallons of toxic water into the stream. Communities along the river scrambled to respond. Then, understandably, they started to debate whose fault the spill was. Was the EPA to blame? The headwaters communities that promoted mining? Contemporary mine owners? The miners of the past who made the mess in the first place?

Our study examines how responsibility for water quality disasters like the Gold King Mine spill is debated and determined in different river communities. You will help us analyze news articles from local newspapers published in Silverton, Durango, and the Navajo Nation. We will use the articles as a window into public discourse surrounding the spill. We will systematically track important variables within the articles, and then compare them. We are interested in looking at change over time in how communities define responsibility for the spill, as well as similarities and differences among community perspectives.

Student’s role and learning objectives: 

This is an exciting opportunity for a student to explore how the public decides who is responsible for environmental disasters and what to do about them. Please join us if you are interested in water and/or environmental issues and if you like working on an interdisciplinary team. Because the research will be reading-intensive, a love of reading is also a must!

The student will work with two professors (Kamini Singha in GE and Adrianne Kroepsch in HASS) as well as MS student Becca Holmes. We will meet with the student weekly. The students primary activities will be working with the team to explore how newspapers cover disasters, and there is flexibility for this project to turn into work in the semester as well if the student enjoys it.

Faculty Mentor: Eric Anderson | Geophysics

Project Abstract: 

In large mid-latitude lakes like the North American Great Lakes seasonal ice conditions can be dynamic, undergoing thermodynamic growth/melting, mechanical deformation, and horizontal transport. Due to the fast-changing nature of Great Lakes ice, having accurate information on daily conditions is crucial for commercial shipping, search and rescue operations by the US Coast Guard, and oil spill response. It’s also important to understand these conditions for Earth Systems modeling approaches like global and regional climate models. While daily products of ice concentration (fractional coverage) are available from the U.S. National Ice Center, and computer models have been developed that predict ice conditions, satellite-observed ice thickness has not been investigated for these large lakes. The goal of this project will be to investigate available ice thickness satellite products (e.g., ICESat-2, CryoSat, etc) in combination with other observations to evaluate ice conditions over large lakes and compare to existing computer ice models to assess forecast accuracy.

Student’s role and learning objectives: 

The student will apply data analysis and computer programming skills to investigate satellite-derived ice products for large bodies of water. This project will provide experience in environmental data analysis, ice physical processes, and some of the most pressing freshwater ice issues facing the scientific field. The student will work with the faculty member to explore ice products over the Great Lakes and achieve these goals/learning-objectives. The project will consist of weekly meetings to discuss progress, with greater frequency at the start of the project. Given the computational nature of this project, there is the possibility of working in-person or remotely (dependent upon internet/network access and other factors).

Faculty Mentor: Paulo Cesar Tabares Velasco| Mechanical Engineering

Project Abstract: 

This is one of the hottest topics in the energy world. As communities start moving away from fossil fuels, it is important to understand the impacts on the electric grid and find ways to make it affordable. This project looks at the impacts electrification has on homes in Colorado and analyze pathways to mitigates these effects

Student’s role and learning objectives: 

This is an ongoing project that utilizes BEopt to simulate homes and SAM for solar and batteries systems. Goal for this summer is to finish preliminary analysis and write a draft paper.

Faculty Mentor: Paulo Cesar Tabares Velasco| Mechanical Engineering

Project Abstract: 

This project analyzes the impact wildfires have on solar panels (PV) electricity generation in Colorado. Using air pollution data and solar generation data from NREL, we will analyze the impacts nearby (and faraway) pollution from wildifres have on PV generation.

Student’s role and learning objectives: 

Student will analyze energy data generated by solar photovoltaic systems in colorado as well as air quality data either from PurpleAir or from Colorado Dept. of Public Health & Environment.
We have preliminary scripts (matlab) and data that needs further work.

Faculty Mentor: Joy Gockel| Mechanical Engineering

Project Abstract: 

Additive manufacturing can be used to make complex parts with very little material waste. For metal additive manufacturing, a major concern is the formation of voids within the material, which can cause failure in a component. In additive manufacturing the manufacturing parameters that are used highly influence the quality of the material in the part because the material is being built at the same time as the part. Changing of parameters, like the laser power, will influence how much material is melted and how each layer is formed. This project will investigate different methods that are used for process parameter development to determine the applicability across different structural metals. A combination of existing data from the literature and experimentally collected results will be used to assess the different methods. An understanding of process design will provide the ability to confidently implement additive manufacturing in advanced applications.

Student’s role and learning objectives: 

The student will learn how to search existing literature to find experimental data as well as to collect new data through hands on material analysis in the laboratory.

Learning Objectives:
-Understand how manufacturing parameters in different materials impact the formation of voids
-Prepare and analyze samples to determine densification
-Search existing literature data for applicable results

Mentoring activities-
The student will meet weekly with the faculty advisor to discuss research progress and plans
The student will collaborate with other lab members through group meetings and lab work
The student will be invited to participate in broader ADAPT center activities to learn about other aspects of additive manufacturing in research and industry

Faculty Mentor: Neal Sullivan | Mechanical Engineering

Project Abstract: 

In this research project, the student will utilize Mines user facilities to quantify the consistency of our electrolyzer-fabrication processes. Electrolysis is witnessing great interest as a means to store the intermittent, renewable electricity in the form of chemical bonds. Specifically, electricity from wind and solar sources is used to drive electrochemical water-splitting reactions to convert H2O into H2 and O2. The Colorado Fuel Cell Center is a national leader in electrolyzer development, including materials discovery and device scale up. In this SURF program, the student will work with existing Ph.D. students and research faculty at the Colorado Fuel Cell Center to help us understand our level of process control in fabricating electrolyzer devices, and to then help us improve that control to advance performance.

Student’s role and learning objectives: 

Student will characterize electrolyzers built by graduate students and research faculty at the Colorado Fuel Cell Center using Mines electron microscope resources. Student will share results with researchers, and develop strategies to improve process control and device performance.

Faculty Mentor: Neal Sullivan | Mechanical Engineering

Project Abstract: 

In this project, the student will establish operational control of new tools used in the fabrication of electrolyzers for H2 production from H2O. Green hydrogen production is emerging as a means to store the intermittent renewable energy produced from solar and wind power. The renewable sources are used to split water into H2 and O2, effectively converting and storing renewable electricity in the form of chemical energy. The Colorado Fuel Cell Center (CFCC) is actively developing next-generation electrolyzers, and recently acquired new manufacturing tools for electrolyzer fabrication. While the effectiveness of these new tools has been established at the CFCC, precision control has not. In this project, the student will integrate new control hardware and software to enable repeatable, precise process control of high-performance electrolyzers for green H2 production.

Student’s role and learning objectives: 

1.) Learn about the components and materials used in fabrication of next-generation ceramic electrolyzers;

2.) Become a master of our new manufacturing tool, the ultrasonic atomistic sprayer built by SonoTek;

3.) Spec, purchase, and install hardware to enable precision control of the SonoTek sprayer;

4.) Develop software to interface with sprayer and its translation stage;

5.) Fabricate next-generation electrolyzers, and evaluate performance of our new spray process.

6.) Interface with four graduate students and four research faculty on electrolyzer development.

Faculty Mentor: Jonah Klemm-Toole | Metallurgical and Materials Engineering

Project Abstract: 

Twin wire arc thermal spray is a high deposition rate process that can be used to coat structural components to improve wear and environmental resistance. New wire feedstocks are need to tailor the properties for specific applications. In this project, the interested student will work with an external company to produce aluminum powder core tubular wires for twin wire arc thermal spray. In addition to producing wires, the student will characterize twin wire arc coatings using X-ray diffraction and scanning electron microscopy.

Student’s role and learning objectives: 

The student will be the primary researcher on the project. A graduate student will assist the interested student to run the wire mill.

At the end of the project, the student will be able to:
– Produce powder core tubular wires based on composition and final diameter requirements
– Perform X-ray diffraction for phase identification
– Utilize scanning electron microscopy to characterize microstructure

The student will meet with the faculty mentor on a weekly basis to provide support and help solve problems with with research

Faculty Mentor: Kip Findley | Metallurgical and Materials Engineering

Project Abstract: 

Hydrogen has gained attention as a renewable energy strategy through the recent White House announcement promoting hydrogen that has boosted an already-expanding hydrogen economy in the United States. Hydrogen compressors, storage containers, and transportation pipelines that make up the expanding hydrogen infrastructure often contain alloys that are susceptible to hydrogen embrittlement. Hydrogen embrittlement is a phenomenon by which a normally-ductile alloy fails in a brittle manner in the presence of hydrogen. Hydrogen embrittlement is especially prevalent in high-strength steels that are exposed to hydrogen during manufacturing or in service. High-strength steel bolts or fasteners can experience hydrogen ingress during zinc electroplating. During service, hydrogen can diffuse into the bolts through cathodic protection in offshore oil and gas environments. While the mechanism for hydrogen embrittlement is not well-established, the risk of hydrogen-induced failure can be reduced by modifying the microstructure and mechanical properties through alloying and thermal treatment. In this project, the student will examine how varying thermal history affects the hydrogen embrittlement susceptibility in steels.

Student’s role and learning objectives: 

During this project, the student will apply metallurgical tools to examine the effects of thermal history on hydrogen embrittlement susceptibility in steels. The student will have the opportunity to use a full suite of metallurgical tools spanning processing, characterization, and breaking stuff (mechanical testing). In particular, the project will involve heat treating, hydrogen charging, hydrogen concentration testing, hydrogen embrittlement susceptibility testing, traditional mechanical testing, and microscopy and microstructural characterization. The student will apply the scientific process and delve into literature to develop a hypothesis and consider possible explanations for the observed behavior. From this experience, the student will learn how to conduct a research project and interpret the outcome.

Faculty Mentor: Terry Lowe | Metallurgical and Materials Engineering

Project Abstract: 

The Transdisciplinary Nanostructured Materials Research Team has developed deep insights into the microstructures and properties of medical devices made from medical grades of stainless steel. Austenitic stainless steels commonly contain small amounts of magnetic ferrite and martensite phases. Martensite can nucleate during deformation processing, for example, during rolling, drawing, swaging, or forging to make medical devices. These phases can dramatically influence the processing and properties of medical devices. Therefore, it is important to measure the amount of these phases. For background information on magnetism in stainless steel see the following article from one of the leading stainless steel producers for medical applications:

https://www.carpentertechnology.com/blog/magnetic-properties-of-stainless-steels

There are two objectives for this project.

The first objective is to assess and compare alternative techniques to detect the amount of magnetic phases. This objective includes developing and applying one or more techniques. The sensitivity of the technique to measure volume fractions less than 1% by volume is the key metric of success. Results should be compared with measurements based upon x-ray diffraction and electron backscatter diffraction (as conducted graduate students and research staff in our research team).

The second objective is to determine whether the locations of the magnetic phases in the surface or near-surface region of stainless steel objects such as sheet, wire, or tubes can be detected. Since we expect martensite will likely be present at grain boundaries and grain sizes range between 1 micron and 25 microns, the technique will need to be combined with some form of microscopy. Optical microscopy is capable of resolutions below 1 micron, and should be sufficient for documenting the location of image contrast agents on a stainless steel surface.

Student’s role and learning objectives: 

There are three deliverables for this project: 1) demonstration of a method that can detect the amount of martensite and other magnetic phases, 2) demonstration of a method that can detect the distribution of martensite in the near-surface region of stainless steel sheet, tubes, or needles, and 3) a report that summarizes the results of the entire project.

The student will work with senior research staff and doctoral students throughout the project since it is synergistic with several current research projects.

The student will be fully responsible for planning and execution of experiments. At least two senior mentors will provide guidance and oversight of the research.

The Transdisciplinary Nanostructured Materials Research Team has an established mentoring program and processes to ensure successful professional development of undergraduate student researchers.

Faculty Mentor: Geoff Brennecka | Metallurgical and Materials Engineering

Project Abstract: 

Additive manufacturing (AM) of ceramics is currently limited by two major problems: 1) flaws introduced during the forming process and 2) after forming, AM ceramics parts are typically densified via slow (multiple days long) thermal processes. This project will adapt in situ process monitoring techniques currently being used for other AM processes to ceramic AM to start to remove these limitations. The primary goal is to setup optical monitoring for both forming and sintering stages and spatially-resolved thermal monitoring for sintering. The stretch goal will be to implement rapid feedback controls to utilize this in situ data to track and modify the processes during operation.

Student’s role and learning objectives: 

The SURF student will learn the basics of ceramic fabrication, rheology, and sintering as well as simple computer control of optical and thermal cameras. Much of the effort will be focused on data capture and–eventually–implementation of closed-loop process control. The SURF student will be mentored by the faculty member as well as one graduate student working on parallel aspects of the same project.

Faculty Mentor: Prashun Gorai | Metallurgical and Materials Engineering

Project Abstract: 

The discovery of new materials is essential to making progress in many of the technological challenges we face: harvesting solar energy to more powerful microelectronics and safer batteries. Computational modeling has greatly accelerated the search for new functional materials. However, such computational searches have largely focused on already known materials that are documented in crystallographic databases. There are a plethora of chemically plausible materials that are likely stable and may possess high-performing properties but have not yet been synthesized. The project will utilize the chemical replacements in structure prototype (CRISP) approach to perform targeted searches of unexplored or under-explored families of Zintl phases. The project will employ data science methods in conjunction with quantum mechanical calculations to implement and accelerate CRISP searches.

Student’s role and Learning Objectives: 

The student will work directly with the faculty mentor to: (1) create and train predictive machine learning models, (2) run first-principles quantum mechanical calculations, (3) perform data analysis and visualization, (4) present the results periodically to a collaborative research team of computer scientists and experimental chemists, and (5) organize and share the data through an open-access (public) GitHub repository.

The student will learn to: (1) write Python codes, (2) learn the basics of quantum mechanical calculations, (3) develop machine learning models, including deep learning neural networks, and (4) develop soft skills (collaborative teamwork, disseminating research through presentations and publications). The soft skills will be useful for the student whether they choose a career in academia or industry.

The student will have one-on-one meetings with the faculty mentor twice a week initially, and then, once a week as the student becomes more independent. The student will also have a chance to interact with the other researchers in the group during the weekly group meetings.

Faculty Mentor: Veronica Eliasson | Mining Engineering

Project Abstract: 

In this project we are going to explore height of burst effects of a blast wave initiated above different types of surfaces. The blast wave is generated using an exploding wire setup, in which a thin wire is subjected to a sudden high-voltage load, which means the wire will vaporize and create a cylindrical or spherical blast wave. The wire is located above a horizontal plane where the surface roughness is a parameter of interest. The main goal is to understand what parameters influence the regular to irregular reflection transition and subsequent growth of the Mach stem. A parameter study will be performed, initially in 2D, but may be extended to 3D. Interested students should have an interest in studying explosives, high-speed photography and be interested in using hands-on experimentation to obtain research results.

Student’s role and learning objectives: 

The student will 1) design the experimental setup, 2) perform experiments together with other group members, 3) analyze data from high-speed photographs and pressure sensors using Matlab programming, and 4) together with group members write up the results in a format acceptable for submission to a peer-reviewed journal.

Our group has a Slack channel for daily conversations and quick questions. We hold weekly group meetings where each student presents on work done in the last week and the outlook for next week followed by brainstorming when the whole group helps to solve problems anyone may have. I also work directly with the student to ensure they are on track with the summer project with the hope to gather enough data to be written up for publication in a peer-reviewed journal.

Faculty Mentor: Will Fleckenstein | Petroleum Engineering

Project Abstract: 

This project contributes to a cutting-edge geothermal technology development project to build a subsurface heat exchanger, which can contribute meaningfully to developing a zero-emission power source with worldwide application. Mines submitted a successful proposal to the DOE to build, test, and run equipment at the University of Utah’s Frontier Observatory for Research in Geothermal Energy (FORGE) located in Milford, Utah needed for a subsurface heat exchanger to solve the EGS riddle, and challenges abound. https://www.minesnewsroom.com/news/mines-awarded-63-million-build-new-geothermal-energy-system-inspired-shale-technology. Key to this project is the development of sleeve that allows the creation of fractures from one horizontal injection well to a series of horizontal production wells. This creates a subsurface heat exchanger that cold water can be injected into the injection wells and harvest heat in the rock while traveling through the fractures to the producer wells. These sleeves are spaced in casing that is cemented into the well, with the cement pumped through the casing, through inside of the sleeves, exiting the casing at the bottom of the well, and returning toward the surface in the casing annulus, where the cement sets and hardens. This project would contribute to that effort with multiple students looking at two critical areas: choice of material such as grease to prevent cement fouling of the tool mechanism, and choice of hydraulic oil or grease within the actuation mechanism. Students will learn design skills for choosing materials that make or break whether a tool or system works at geothermal temperatures of 225 C. Below is a link to an animation of the sleeve design – it will help to understand the particulars of the project, if the sleeve can’t be protected, the project fails. There is also a link to an overview of the project and what this could mean if we succeed.

https://www.dropbox.com/s/w6raqj6944eywrf/Animation%20Prototype%20Concept%203.mp4?dl=0
https://www.dropbox.com/s/lb8rzzaiyoysy9x/GeoThermOPTIMALApril62022.mp4?dl=0

Student’s role and learning objectives: 

The students working on this project will learn to identify, formulate, and then solve a complex materials engineering problem to produce solutions that meet specified needs of the material needs. The students would work with the professional sleeve designers to understand what material is needed with the tool being designed and built and learn to roughly scope out the project objectives, and what is a reasonable timeline. A key skill for an engineer is to learn to scope out the project objectives and define what is “success”. The student would have to work to understand what is needed, and then how to find what material fits that need and what type of tests may be needed to prove that it works for its intended purpose.

The students would research greases to understand what a grease is and are there other materials that may make more sense, such as a silicone putty or some other material. The student would work with Dr. Fleckenstein and the tool designers, who have weekly meetings to collaborate and will be able to mentor the students on their role, and learn how a complex, multi-year, multi-million-dollar technology development project is managed. This is a key part of the project, so the students will work with Dr. Fleckenstein to design a work schedule with plenty of “face time” that provides students the independence to work on the project, but also the guidance to help the students succeed. There are a variety of other parts of the project that are moving forward, and if the student can contribute to other parts of the project, it would be wonderful.

Faculty Mentor: Serena Eley | Physics

Project Abstract: 

Two-dimensional (2D) materials consist of a single layer of atoms and, when stacked, are called van der Waals (vdW) heterostructures due to strong in-plane covalent bonding and weak interlayer interactions. Graphene is perhaps the most well-known vdW material. Our group is interested in studying the motion of magnetic domains in magnetic vdW materials that are of interest for use in next-generation spintronic devices. The undergraduate researcher would fabricate Hall bars of the 2D vdW material Fe3GeTe2 (FGT) and study the low temperature, variable magnetic field electrical transport properties of these devices.

Student’s role and learning objectives: 

Activities
– Fabricate Hall Bars of FGT using a setup for mechanical exfoliation in a glove box and annealing
– Wirebonding
– Low temperature electrical transport measurements in an attocube cryostat

Learning objectives
– Standard techniques for microfabricating devices
– Mechanical exfoliation
– Electrical transport measurements
– Low temperature measurements

Faculty Mentor: Susanta Sarkar | Physics

Project Abstract: 

Matrix metalloproteases (MMPs) are calcium- and zinc-dependent broad-spectrum proteases secreted by host and bacterial cells that degrade many proteins in the human body. MMPs, a 23-member family of human enzymes, are implicated directly or indirectly in most human diseases. Despite such relevance in human health, only one FDA-approved drug (Periostat) targets MMPs because any drug used for inhibiting MMPs results in adverse side effects due to diverse functions of MMPs. We are pursuing the long-term hypothesis that we can control one MMP function without affecting others using allosteric ligands.
We can now define changes at the catalytic site as MMP1 binds different biomaterials. Also, we can identify allosteric residues or “allosteric fingerprints” in MMP1 for each biomaterial. Currently, we are screening drugs against these sites will lead to material-specific control of MMP1 function. Undergraduate students will test if machine learning based methods can screen ten million or more compounds per day.

Student’s role and learning objectives: 

Students will use publicly available database of commercially available compounds from the ZINC database.
We will provide material-specific amino acids in matrix metalloprotease-1. Students will use these information
to test a machine learning based algorithm for drug screening.