Faculty Mentor: Evgueni Filipov (firstname.lastname@example.org)
Research Mode: In Lab & Hybrid
Project Description: Deployable structures that use the principles of origami could lead to applications in multiple scales and disciplines from biomedicine to space exploration. In architecture and civil engineering reconfigurable facades could adapt to the environment, and rapidly deployable shelters and bridges could be used for disaster relief efforts. The objective of this project will be to explore how to scale-up principles of origami for structural engineering applications. The student will first create an analytical model to study the motion and geometry of an origami-inspired deployable structure. Next, a laser cutter will be used to fabricate panels for a scaled prototype of the structure. These individual panels will then be interconnected with metallic or plastic hinges that allow for deployment and reconfiguration. The systems will be constructed to minimize the stowed volume while allowing for a reliable deployment that requires minimum force input. Time permitting, the student will conduct experimental testing to quantify the stiffness of different deployable systems.
Faculty Advisor: Alex Szczuka (email@example.com)
Research Mode: In lab, remote, hybrid
Project Description: Drinking water disinfection is one of the greatest public health achievements of the twentieth century. Disinfectants readily remove disease causing pathogens in our water, and help prevent millions of deaths from waterborne illnesses. However, when disinfectants are applied to water, disinfection byproducts, which are probable carcinogens, can form. While some classes of disinfection byproducts are regulated in drinking water, toxicologists have found that disinfection byproducts that are not regulated in drinking water can be orders of magnitude more toxic to cells than regulated disinfection byproducts. In this project, we will develop methods to detect both regulated and unregulated disinfection byproducts in water, and explore how alternative disinfectants affect disinfection byproduct formation.
Faculty Advisor: Krista Wigginton (firstname.lastname@example.org)
Postdoctoral Mentor: Alex Szczuka (email@example.com)
Research Mode: In lab, hybrid
Project Description: Disinfectants are used ubiquitously to inactivate viruses on surfaces, in water treatment, and in food processing. To do so, disinfectants target various components of viruses, including viral nucleic acids (e.g. DNA/RNA). The rate of reaction of disinfectants with nucleic acids, and hence the rate of virus inactivation, can vary based on the environment that viruses are in. In this project, we will use both analytical chemistry and microbiology techniques to determine how disinfectants affect nucleic acids in viruses in different environments.
Faculty Mentor: Roman D Hryciw (firstname.lastname@example.org)
Prerequisites: Junior Standing, CEE 200
Research Mode: Hybrid, including possibly field work if pandemic conditions permit.
Project Description: The geotechnical image analysis group in the Civil and Environmental Engineering Department will be developing image analysis tools for characterizing soils in the laboratory using a new “Sediment Imaging” system and in the field using a newly developed Vision Cone Penetrometer (VisCPT). The SURE student will assist the faculty and a PhD student with collection of data and its analysis. The research will also require literature review and one-on-one instruction. It is envisioned that the project will lead to at least one conference and one journal paper co-authored by the SURE student.
Faculty Mentor: Brian Steglitz (email@example.com)
Prerequisites: Course work in water treatment technology
Research Mode: (In Lab, Online, Remote, Hybrid) – This research will take place over the summer in residence at the City of Ann Arbor’s water treatment plant at 919 Sunset Road.
Project Description: Student with work side-by-side with City of Ann Arbor water treatment plant staff to design a testing program for the city’s granular activated carbon filters. Students will evaluate operational parameters such as empty bed contact time, washing protocols, bed expansion, particle counts and turbidity to balance filter efficiency and performance.
Faculty Mentor: Henry Liu (firstname.lastname@example.org)
Prerequisites: Programming Language (Python, C++(optional)), familiar with Linux system
Research Mode: Online, Remote, Hybrid
Project Description: Connected and automated vehicles (CAVs) have attracted significant attention in recent years because of the potential to improve traffic safety. However, although the problem of CAV testing has been investigated extensively by various CAV developers, government agencies, professional organizations, as well as academic institutions, the theory and methods to support such testing and evaluation are lacking.
This project is aiming to develop a simulation platform for CAV testing purposes based on two open-source simulation platforms: SUMO and Carla. SUMO will be utilized to generate the background traffic flow and Carla will be used to provide a 3D user interface. Therefore, the perception and decision-making abilities can be tested thoroughly by synchronizing between the two simulators. Additionally, this project will support the deployment of augmented reality-based CAV testing platform at the American Center for Mobility (ACM) in Michigan, which is arguably the world’s largest closed test track for CAVs.
During the SURE project, the undergraduate student will contribute to the development of the CAV testing simulation platform, which will require familiarity of computer programming. Additionally, the ability to deploy the written software (based on Python or C++) on selected operating systems (Linux) is also essential during this project.
Faculty Mentor: Jerome P. Lynch (email@example.com)
Graduate Student Mentor: Gabe Draughon (firstname.lastname@example.org)
Research Mode: In person if allowed; otherwise online
Project Description: As smart cities emerge, sensors and actuators are proliferating to monitor and control infrastructure systems. These cyber-enables systems can enhance infrastructure performance, introduce new modes of robustness, and make infrastructure management more cost effective. However, to be truly resilient, such systems must explicitly consider how people use infrastructure and infrastructure services. To “sense” the social dimension of infrastructure services, we are developing deep learning methods to automate the identification of people in cities and using a plethora of modeling tools to model their behavior. This project will work with a graduate student team in refining deep learning algorithms to model park patrons, adopt machine learning tools to classify their behavior, and develop visualization tools for park managers. The project will work closely with park conservancies in the City of Detroit.
Faculty Mentor: Jerome P. Lynch (email@example.com)
Graduate Student Mentor: Kidus Admassu (firstname.lastname@example.org)
Research Mode: In person if allowed; otherwise online
Project Description: This project is developing machine learning tools to scalably process data derived from public transit systems to model their performance. The student recruited for this project will work with data collected from the Twin City Area Transit Authority (TCATA) to visualize performance metrics including on-time arrival, ridership demand, and origin-destination mapping. Student researcher will work with stakeholders from Benton Harbor on data acquisition, analysis and visualization.
Faculty Mentors: Nancy Love (email@example.com), Krista Wigginton (firstname.lastname@example.org)
Prerequisites: Must be at least an experienced sophomore or rising junior with a focus in environmental engineering or chemical engineering
Research Mode: In person; this project will only occur if students can attend in person
Project Description: Nutrients in wastewater can lead to harmful algal blooms that affect 70% of U.S. waters. Current approaches to removing these nutrients from wastewater are energy-intensive, costly, and environmentally harmful. Synthetic fertilizer production and phosphorus mining processes also emit greenhouse gases and require scarce inputs. The urine fraction of human wastes contain the vast majority of nutrients that enter our communities (over 80% of nitrogen and 65% of phosphorus); therefore, it is best to capture this waste stream and process it for repurposing. Instead of diluting urine with flush water to send to a centralized treatment plant, we separate it at the source and process it using a variety of methods to create concentrated fertilizers. In GG Brown, we have the first-of-its-kind urine separation and processing to fertilizer among U.S. university campuses. Drs. Love and Wigginton have led a team of researchers to evaluate urine treatment processes, evaluate the fate of biological and chemical contaminants, evaluate perceptions to advance communication about urine-derived fertilizers, and understand risks associated with urine separation versus maintaining mixed waste management practices. This year, we want to add a SURE student who will work with us and our partners, the RICH EARTH INSTITUTE, to get our urine separation and processing system back in operation after a long shutdown from the pandemic. The SURE student will also participate in a visit to RICH EARTH INSTITUTE in Brattleboro, Vermont as part of their training. Our ability to fill this slot is subject to the university opening lab access for undergraduate students during Summer 2021.
Project advisors: Steve Skerlos (email@example.com), Lutgarde Raskin (firstname.lastname@example.org), and Kuang Zhu (postdoc)
Graduate student mentor: Tim Fairley (email@example.com)
Project Description: Anaerobic digestion (AD) fits well in the framework of sustainability since it treats organic waste while generating energy in the form of methane and producing a solid digestate with fertilizing properties. Nevertheless, hydrolysis is usually slow due to the presence of lignocellulosic materials in solid wastes. In previous work, the use of rumen content, which contains microbes able to efficiently digest plant material, led to an improvement in the hydrolysis rate and a high production of volatile fatty acids (VFA), which can be used later for the generation of methane, hydrogen or platform chemicals. The objective of this project is to enhance the rate of hydrolysis and the fermentation yield when solid organic wastes like sewage sludge, food waste or agricultural wastes are used in an anaerobic system. To accomplish this, a novel anaerobic dynamic membrane bioreactor has been designed based on the rumen as a model. The student working on this project will gain experience with operating laboratory-scale anaerobic digesters and bioreactor monitoring methods. If interested, the student can also study the microbial community in the reactor using advanced molecular biology tools.
Faculty Advisor: Lutgarde Raskin (firstname.lastname@example.org)
Graduate Student Mentor: Dianna Kitt
Project Description: Chain elongation is an emerging biotechnology that allows for the recovery of medium chain carboxylic acids (carboxylic acids with six to twelve carbon atoms) from various solid and liquid waste streams using an open culture microbial community 1–3 . The recovery of medium chain carboxylic acids (MCCAs) is of great interest because the recovered products can provide sustainably sourced platform chemicals for the production of valuable products such as animal feed additives, antimicrobials, lubricants, fragrances, and biofuel precursors 1–3 . In previous work in our laboratory, a novel anaerobic dynamic membrane bioreactor was developed to recover MCCAs from brewery waste and pretreated food waste. The scope of this project will include further optimization of MCCAs recovery by exploring alternative waste streams that could be utilized for recovery, optimizing the bioreactor design to develop a more robust and resilient system, integrating an MCCAs extraction unit to reduce product toxicity in the bioreactor, and applying next generation molecular biology and community analysis tools to better understand the microbial community responsible for MCCAs production. The student working on this project will have the opportunity to gain experience operating a laboratory scale bioreactor and performing chemical and microbial analysis techniques.
Faculty advisor: Lutgarde Raskin (email@example.com)
Mentors: Pedro Puente (PhD student)
Project Description: Organic food waste represents an underutilized energy and nutrient resource that is largely disposed of in landfills, where it notably contributes to anthropogenic greenhouse gas emissions. It is estimated that about 38 million tons of food waste are discarded every year in the US (EPA, accessed 2018), of which, in 2014, ~95% was sent to landfills and incinerators. Instead of landfilling or incinerating food waste, this organic-rich stream can be anaerobically digested to produce and collect biogas, a renewable resource. Over the past decade, recovering source separated organics (SSO) at water resource recovery facilities (WRRFs) has gained a strong foothold around the world. Such WRRFs typically already anaerobically digest primary and secondary solids. By adding SSO food waste as a co-substrate, these WRRFs increase their biogas production and energy recovery. A key challenge is that the composition and characteristics of SSO food waste differ geographically and can change over time. This causes challenges and uncertainties for WRRFs accepting this material, operators, and engineers. Students involved in this project will perform a wide array of methods to sample and characterize different types of SSO food waste streams. The ultimate aim of this project is to provide WRRFs with analytical guidelines to sample and monitor their incoming SSO food waste and ensure optimal digester operation. A unique aspect of this opportunity entails the close collaboration with Carollo Engineers, Inc., an environmental engineering consulting firm, and several WRRFs.