2024 Projects

Jad Abou Ali ’26

Chemical Engineering, Concentration in Energy

Advisor: Martin Bazant, Professor of Chemical Engineering and Mathematics, Chemical Engineering
Direct Supervisor: Yash Samantaray, Graduate Student, Chemical Engineering
Sponsor: Friends of MITEI UROP

Lithium-ion battery recycling
Lithium-ion batteries (LIBs) have lately seen a high demand, especially for their optimal use in electronics and electric vehicles. However, when batteries reach end-of-life (EoL)—when the battery’s capacity goes below 80%—a majority will be disposed of in landfills. To reduce waste generation and optimize the performance of LIBs, we are investigating different methods to rejuvenate LIBs that have reached EoL. Specifically, we are exploring the performance recovery of graphite/lithium iron phosphate (LFP) batteries through in-situ electrolyte modifications. Such research would improve the environmental and economic sustainability of LIBs, reducing harmful waste and improving the economic viability of using LIBs worldwide.

Advisor: Lawrence Susskind, Ford Professor of Urban and Environmental Planning, Urban Studies and Planning
Direct Supervisor: Jungwoo Chun, Graduate Student, Urban Studies and Planning
Sponsor: Friends of MITEI UROP

MIT Renewable Energy Clinic
Massachusetts has introduced its mission to shift to full renewable energy by 2050. Our research with the Renewable Energy Clinic involves looking at all aspects of a wind energy or battery storage project in New England, analyzing the risks, understanding stakeholders’ concerns and areas of support, and, eventually, investigating the reasons for opposing/supporting the project. With conversations with relevant stakeholders and further research, we would be able to generate class materials for the Renewable Energy Clinic to provide students of Fall 2024 with case studies that could help in resolving conflicts and continuing the projects. This research will help facilitate the progress of these renewable projects and help MA reach its climate goals by 2050. Broadly, this research on paused projects will take place in other states and countries to help more governments reach their climate goals.

Andrew Acevedo ’27

Chemical Engineering

Advisor and Direct Supervisor: Ariel Furst, Paul M. Cook Career Development Professor, Chemical Engineering
Sponsor: Chevron (Summer) and Friends of MITEI UROP (Fall)

Multiscale control of copper nanoparticle DNA conjugates for improved electrochemical CO2 reduction
Methanogens are microbes that are found in abundance across many industries such as agriculture and waste management. As their name may imply, these organisms have the unique ability to produce methane, a useful fuel and potent greenhouse gas. More interestingly, their primary metabolic pathway involves utilizing carbon dioxide (CO2) as the sole carbon source. My research this summer with the Furst Lab involves analyzing how environmental variables such as electrochemical conditions affect methane production in Methanogens. Characterizing the conditions that optimize the CO2 consuming metabolic pathway could provide us insight into CO2 revalorization. Methanogens could improve sustainability in industry by decreasing CO2 emissions all the while creating a useful, valuable product in the form of methane.

Kristel Acuña García ’27

Mechanical Engineering

Advisor: Alexander Slocum, Professor, Mechanical Engineering
Direct Supervisor: Aditya Mehrotra, Graduate Student, Mechanical Engineering
Sponsor: Friends of MITEI UROP

Economic and battery research for Ghanaian ambulance energy transition

Rose Alsalman ’25

Computer Science and Engineering, Business Analytics

Advisor: Thomas Magnanti, Institute Professor, Sloan School of Management
Direct Supervisor: Yifu Ding, Postdoctoral Associate, MIT Energy Initiative
Sponsor: Shell

Planning green hydrogen transportation for carbon-neutral grid operations
Green hydrogen, produced using renewable energy, is a key component in decarbonizing the energy sector. However, its integration into the energy grid is challenged by the variability of its production and the complexities of transporting it efficiently. Our research focuses on developing an integrated optimization model using Julia and Python that specifically targets the challenges of green hydrogen transportation and grid dispatch. The model identifies highly efficient transportation routes that minimize both costs and carbon emissions. This tool can be utilized by policymakers, energy companies, and grid operators to make informed decisions, supporting the transition to a low-carbon economy with an emphasis on sustainable and resilient grid operations.

Maya Ayoub ’26

Mechanical Engineering

Advisor: Alexander Slocum, Professor, Mechanical Engineering
Direct Supervisor: Aditya Mehrotra, Graduate Student, Mechanical Engineering
Sponsor: Friends of MITEI UROP

Economic and battery research for Ghanaian ambulance energy transition
Many developing countries do not produce their own non-renewable energy, which makes necessary electric vehicle operations, such as ambulances, hard during energy market swings. In addition, the lack of possible battery option models and documentation contributes to the difficulty in electrifying vehicles. Our project focuses on battery cells available in Ghana and we are testing key characteristics such as internal resistance, state of charge over time, life cycle analysis, etc. We aim to find a correlation between relatively easier-to-measure values (e.g., internal resistance) and harder-to-measure values (e.g., viable lifecycle), so people in developing countries can more easily and accurately build electric vehicles with the tools they have on hand. In addition, we hope the documentation we are conducting on batteries available in Ghana and other developing countries will assist in electric vehicle deployment.

Tsolmon Bazarragchaa ’25

Mechanical Engineering

Advisor and Direct Supervisor: John E. Parsons, Senior Lecturer, Sloan School of Management; Deputy Director for Research, Center for Energy and Environmental Policy Research
Sponsor: Friends of MITEI UROP

Modeling hydroelectric power in Québec
Reaching a decarbonized electric grid requires optimization of energy resources allocation and dispatch. For instance, in Québec, 95% of the electricity comes from hydropower resources. It is estimated that two-way trade between Québec and New England can lower the cost of the zero-emission power system by 17-28%. We are improving the representation of the hydropower resources in capacity expansion planning by adding detailed reservoir and river flow and pumped storage hydro representation data based on literature review.

Advisor: Michael Short, Associate Professor, Nuclear Science and Engineering
Direct Supervisor: David Cohen-Tanugi, Venture Builder, Office of Innovation
Sponsor: Shell

Techno-economic analysis on molten salt thermal energy storage
The U.S aims to reduce building emissions by 90% by 2050, which requires electrifying residential homes. However, the transition to electric heating will increase peak electricity demand, overloading the grid. I’m analyzing a cost-effective thermal energy storage system integrated with thermal resistive or induction heating that shifts heating demand away from peak hours. This solution could save up to $500 per year per household compared to current electric resistive heating and ease the grid’s burden, supporting a faster energy transition.

Otto Beall ’27

Electrical Engineering

Advisor: Tonio Buonassisi, Professor of Mechanical Engineering, Mechanical Engineering
Direct Supervisor: Tianran Liu, Postdoctoral Associate, Research Laboratory of Electronics
Sponsor: Friends of MITEI UROP

Perovskite solar cell durability enhancement and material discovery project
The manufacturing of conventional silicon-based solar cells is energy intensive. Perovskite solar cells are a promising alternative that can be fabricated with low-energy wet-chemical methods, but they face problems of instability and lead toxicity. Our ongoing research seeks to bring perovskite technology to commercialization by using AI to identify potentially stable lead-free perovskite films and implement high-throughput testing of candidate materials. Additionally, by evaluating the impact of supplementary layers such as a strain-relief monolayer and 2-D perovskite capping layer on cell performance and stability, we seek to create durable and low-cost solar cells that can advance the accessibility of renewable energy.

Riddhi Bhagwat ’27

Computer Science and Engineering

Advisor: Ruben Juanes, Professor, Earth, Atmospheric and Planetary Sciences, Civil and Environmental Engineering
Direct Supervisor: Hannah Lu, Postdoctoral Associate, Civil and Environmental Engineering (Summer); Lluis Salgo-Saldago, PhD Student, Civil and Environmental Engineering (Fall)
Sponsor: Shell (Summer) and Friends of MITEI UROP (Fall)

Enhancing subsurface fluid flow and solute transport modeling through machine learning: A study of fractured rocks using FracNet
Developing better systems to improve our understanding of fractured rock networks is crucial for managing underground resources and advancing renewable energy. Existing models and simulation algorithms are very time-consuming, computationally expensive, and have inherent uncertainties that can lead to inaccuracies in predictions. In my research, I have generated representational datasets and am integrating various flow modeling strategies to develop a surrogate deep learning tool to map these networks and maintain high accuracy rates while reducing computational costs. Having this model allows us to better allocate our resources to manage underground substance storage, prevent groundwater contamination, and enhance renewable energy developments.

Evan Boothe ’25

Aerospace Engineering

Advisor: Brian Wardle, Apollo Program Professor, Aeronautics and Astronautics
Direct Supervisor: Jen-Hung Fang, Postdoctoral Associate, necstlab
Sponsor: Shell

Sulfonated polyether ether ketone empowered multifunctional supercapacitors
Many electrified vehicles, including electric aircraft and vertical takeoff and lift (eVTOL) systems, require both high specific energy and power. The goal of my project is to design a supercapacitor using carbon fiber and a solid polymer electrolyte which has high strength and stiffness to be used as both a structural element and an electrical energy storage device. This multifunctional approach combines the mechanical function of the composite with the energy storage function in the same mass or volume, leading to system efficiencies that can be realized as, e.g., enhanced range or payload. Such efficiencies may enable future electric vehicles.

Joshika Chakraverty ’25

Chemical Engineering, Concentration in Computation and Applied Mathematics

Advisor: Michael Strano, Carbon P. Dubbs Professor, Chemical Engineering
Direct Supervisor: Yu-Ming Tu, Postdoctoral Associate, Strano Research Group
Sponsor: Shell

Examining nanoconfined fluid phase transitions within single digit nanopores
Nanotechnology enables precise customization at the molecular level, driving innovations in fields like energy, healthcare, and agriculture. My lab focuses on fluid flow through nanopores, where fluids behave differently due to the increased impact of intermolecular forces. This unique behavior can be harnessed for applications such as desalination, chemical sensors, and energy storage. Experimentally measuring fluid properties in nanopores is costly and energy-intensive, so we are combining experimental data, simulations, and theory to develop equations that can accurately predict these behaviors. Our goal is to create a generalizable set of equations that accurately models fluid behavior at the nanoscale.

Anushree Chaudhuri ’24

Urban Studies and Planning, Economics

Advisor: Lawrence Susskind, Ford Professor of Urban and Environmental Planning, Urban Studies and Planning
Direct Supervisor: Jungwoo Chun, Graduate Student, Urban Studies and Planning
Sponsor: Friends of MITEI UROP

Characterizing the scope and nature of local community sentiment towards large-scale renewable energy development in the United States
A clean energy transition can be an opportunity to empower communities, but the current rapid buildout of large-scale solar and wind projects can sometimes overlook local concerns. There is no comprehensive national database documenting community perspectives about large-scale renewable energy facilities to understand trends and quantify the effects of local support on meeting climate goals. This research will use natural language processing and machine learning models to analyze online discourse and quantify positive and negative sentiment towards proposed and operational renewable energy projects in the United States in the past two decades. We aim to produce an open-access database summarizing community perspectives to inform more inclusive energy infrastructure planning and policy. The database can help diverse stakeholders—policy makers, project developers, non-profits, and communities—to understand local needs, learn from past conflicts, and proactively design better solutions to build support for renewable energy projects.

Disha Chauhan ’26

Computer Science, Economics, and Data Science

Advisor: Cathy Wu, Thomas D. and Virginia W. Cabot Career Development Associate Professor, Civil and Environmental Engineering
Direct Supervisor: Guangchun Ruan, Postdoctoral Associate, Institute for Data, Systems, and Society
Sponsor: Friends of MITEI UROP

Deep learning for operating renewable energy system
Managing large-scale power grid systems is increasingly complex and inefficient, particularly with the growing integration of renewable energy sources. We are developing a versatile machine learning model that determines when to turn on and off various generators by simulating numerous power grid scenarios to optimize decisionmaking. By using machine learning to skip unnecessary steps in the mathematical search process (such as certain “separators” normally used to simplify the problem), we can find optimal solutions more quickly and at lower cost. This clearer understanding of feasibility and costs will improve the overall efficiency of energy systems and support the broader integration of renewables, enabling decision-makers to identify effective and sustainable strategies for clean energy adoption.

Jessica Cohen ’24

Physics

Advisor: Michael Short, Associate Professor, Nuclear Science and Engineering
Direct Supervisor: Nathan Melenbrink, Lead Instructor, NEET Renewable Energy Machines, School of Engineering
Sponsor: Friends of MITEI UROP

Thermal salt batteries as a viable alternative to coal in Ulaanbaatar, Mongolia
In Ulaanbaatar, Mongolia, the coldest capital in the world, around 60% of the population heats their homes using coal, which releases greenhouse gases and contributes to air pollution levels up to 27 times the safe level as characterized by the World Health Organization. In order to promote cleaner air as well as address decarbonizing heating in this city and unique climate, this project focuses on using thermal salt batteries to replace coal to heat homes. By using thermal salt we can store a lot of thermal energy in the batteries without producing air pollution, unlike coal. We will run both physical experiments and simulations through COMSOL to model the thermodynamics of the system. This project has the potential to lower the carbon footprint of the capital of Mongolia and increase air quality for overall population health.

Katie Crowley ’25

Chemical Engineering

Advisor: Fikile Brushett, Associate Professor, Chemical Engineering
Direct Supervisor: Trent Weiss, Graduate Student, Chemical Engineering
Sponsor: Friends of MITEI UROP

Characterizing chain length effects on redox-active bottle brush polymers for redox flow batteries
Redox flow batteries have great potential for energy storage, but they contain expensive ion exchange membranes that make them cost prohibitive. Our project looks at the electrochemical characteristics of bottlebrush polymers that can be used in redox flow batteries which would enable the expensive membrane to be replaced with a cheap size exclusion separator. This would allow redox flow batteries to be a more affordable option, facilitating the decarbonization and electrification of many processes.

Cory Decker ’27

Advisor: John E. Parsons, Senior Lecturer, Sloan School of Management; Deputy Director for Research, Center for Energy and Environmental Policy Research
Direct Supervisor: Shen Wang, Postdoctoral Associate, MIT Energy Initiative
Sponsor: Friends of MITEI UROP

Modeling hydroelectric power resources in electricity system generation expansion planning models
The water dynamics of hydroelectric power generation are often misrepresented or ignored in terms of simulating power output and ramping capability in electricity generation expansion planning models. Lack of impact assessments of water dynamics may lead to miscalculations in power and transmission capacity needs and flexibility requirements using current models. Through a comprehensive literature review, we determined issues within the current model regarding hydropower modeling and proposed new formulations to make our electricity generation projections more accurate. Through this analysis, we can provide practical insights into addressing the challenges associated with integrating renewable energy sources into regional power grids, such as how hydroelectricity production in Quebec can be transmitted to cities like New York and Boston.

Matthew De Jesus ’25

Electrical Engineering and Computer Science

Advisor & Direct Supervisor: John E. Parsons, Senior Lecturer, Sloan School of Management; Deputy Director for Research, Center for Energy and Environmental Policy Research
Sponsor: Friends of MITEI UROP

Electricity market design for incentivizing investments in battery storage
Grid-scale batteries are essential for integrating renewable energy into the grid by storing energy for use when generation is low. However, investors are wary because there isn’t enough reputation and documentation on profitability. My work models the revenue of grid-scale batteries using real-life energy markets and grid conditions. Using the model, we can predict the return on investment, offering investors the confidence to support renewable energy projects. This could accelerate the deployment of renewable infrastructure, helping to meet climate goals.

Gozel Dovranova ’26

Chemical Engineering

Advisor: Heather J. Kulik, Lammot du Pont Professor of Chemical Engineering, Chemical Engineering
Direct Supervisor: Akash Ball, Graduate Student, Chemical Engineering
Sponsor: Friends of MITEI UROP

Understanding structure-property relationships in mechanical stability of ultrastable metal-organic frameworks via machine learning
Industrial separation processes are energy-intensive, and current membrane technologies face trade-offs between cost, efficiency, and durability, making it challenging to identify materials that achieve high selectivity, permeability, and mechanical stability among countless potential candidates. This project uses machine learning to accelerate the discovery of mechanically stable metal-organic frameworks (MOFs) by predicting properties such as bulk and shear moduli. By uncovering critical structure-property relationships, these predictions guide the selection and design of MOFs that are better suited to address the outlined challenges. Developing durable and efficient MOFs for industrial membranes can reduce energy consumption, lower greenhouse gas emissions, and provide sustainable solutions to global water and energy challenges.

Tyler Ea ’25

Mechanical Engineering

Advisor & Direct Supervisor: Robert Granetz, Principal Research Scientist, Plasma Science and Fusion Center
Sponsor: Friends of MITEI UROP

Mechanical design of 3D printed stainless steel plates for stellarator coils
Magnetic confinement nuclear fusion (MCF) may become the most important power-generating technology with its high energy density and zero operational emissions, and the stellarator concept in MCF allows for steady-state operation. Stellarators, however, have not been realized due to the complex physics and engineering considerations of their current-carrying coils. My 3D modeling and simulation work seeks to provide an iterable workflow to analyze potential mechanical structures needed to engineer stellarator coils in reactor-relevant conditions.

Elise Echarte ’27

Mechanical Engineering

Advisor: Lawrence Susskind, Ford Professor of Urban and Environmental Planning, Urban Studies and Planning
Direct Supervisor: Jungwoo Chun, Graduate Student, Urban Studies and Planning
Sponsor: Friends of MITEI UROP

MIT Renewable Energy Clinic
At the MIT Renewable Energy Clinic, we aim to assist all relevant stakeholders in working to mitigate the socio-economic, cultural, environmental, and political impacts of large-scale renewable energy projects by prioritizing equitable development and ensuring the inclusion of diverse perspectives in the planning process, or as described by MIT’s Climate Project missions, empowering frontline communities. My work strives to identify early projects with concerned host communities that could benefit from a neutral and independent facilitation, helping the group reach agreement and joint problem-solve by assisting in the process, including conducting a full-fledged stakeholder assessment. By helping to improve and inform the siting and permitting processes, we accelerate renewable energy projects and help New England work towards a greener, more equitable future.

Matthew Garcia ’27

Artificial Intelligence and Decision Making

Advisor: Matteo Bucci, Esther and Harold E. Edgerton Associate Professor, Nuclear Science and Engineering
Direct Supervisor: Matthew Hughes, Postdoctoral Associate, Nuclear Science and Engineering
Sponsor: Friends of MITEI UROP

Development of a small-scale autonomous boiling experiment platform for decision-making and benchmarking
Currently, delays in experimental testing on the Suva Skid loop have hindered our ability to validate autonomous boiling heat transfer methodologies. To mitigate these delays, our team developed a small-scale, low-cost Suva flow boiling test facility. This setup will allow us to refine optical diagnostics and study boiling mechanisms of the fluid under various operating conditions. By leveraging available components and minimal additional resources, this project will accelerate progress toward our goal of fully automated boiling experimentation. The resulting advancements will not only enhance efficiency in high-temperature systems but also contribute to the broader development of autonomous heat transfer technologies. This will greatly improve the efficiency of boiling systems in nuclear reactors. This will make nuclear energy cheaper, helping the world reach its greater energy goals.

Sponsor: Shell

Development of an autonomous boiling heat transfer experiment software platform
Currently, high-speed videography allows us to examine boiling mechanisms to prevent risks and increase productivity in high-temperature reactors. However, there is extensive manual work needed for these processes. So, I will be creating an automation script using matlab and labview to make changes in reactor parameters such as flow rate and pressure to keep the reactor as efficient as possible without risking a meltdown. This project would help make energy cheaper and more efficient, helping the world reach its energy goals.

Javier Gil ’26

Chemical-Biological Engineering

Advisor & Direct Supervisor: Jean-Francois Hamel, Lecturer, Chemical Engineering
Sponsor: Shell (Summer) and Friends of MITEI UROP (Fall)

Biocatalytic enhancement of CO2 capture using recombinant carbonic anhydrase
Global warming, driven by the accumulation of greenhouse gases such as CO2 in the atmosphere, presents a critical challenge to our planet. This project addresses this issue by leveraging the catalytic power of carbonic anhydrase, an enzyme that catalyzes the conversion of CO2 to bicarbonate, which is mineralized into carbonates, effectively sequestering CO2. Furthermore, these carbonates have practical applications in construction materials, cement production, and soil amendment, offering additional economic value and offsetting the costs of CO2 capture. By enhancing the efficiency of CO2 conversion and providing useful by-products, this project holds significant potential for advancing carbon capture technologies and mitigating the impacts of climate change.

Presha Goel ’27, Wellesley

Math and Computer Science

Advisor & Direct Supervisor: John Parsons, Senior Lecturer, Sloan School of Management; Deputy Director for Research, Center for Energy and Environmental Policy Research
Sponsor: Friends of MITEI UROP

Electricity price modeling
The increased penetration of intermittent renewable generation increases the volatility of electricity prices and the value of flexibility in industrial processes such as hydrogen production. This project is focused on modeling the drivers of electricity price volatility. My contribution is the statistical analysis of the output of certain price models being used to value flexibility.

Vivian Guo ’27

Chemical Engineering

Advisor & Direct Supervisor: Jean-Francois Hamel, Lecturer, Chemical Engineering
Sponsor: ExxonMobil (Summer) and Friends of MITEI UROP (Fall)

Enhancement of CO2 capture using carbonic anhydrase
The lack of efficacy and cost efficiency of current capture technologies cannot sustain our planetary limit due to increasing rates of carbon emissions due to mass industrialization. Carbonic anhydrase is an enzyme that can speed up mineralization, however it must be immobilized to be re-used. But when immobilized, the carbon cannot reach the active binding sites due to steric hindrance. We have developed a new immobilization technique that extends the spacer arm allowing for easier binding between the enzyme and carbon substrate, thus decreasing the cost-efficient aspect of carbon capture.

Logan Hammond ’25

Chemical Engineering

Advisor: Aristide Gumyusenge, Henry L. Doherty Career Development Professor in Ocean Utilization, Materials Science and Engineering
Direct Supervisor: Eric Lee, Graduate Student, Materials Science and Engineering
Sponsor: Friends of MITEI UROP

Electrochemical development of semiconducting polymers for energy storage
Medicinal drug delivery research is crucial for improving patient treatment and expanding the range of treatable conditions. Once a drug is put into the body, it is difficult to control the release with any great complexity. To address this complexity, we can take advantage of polymers to create a drug delivery device that can be remotely controlled, safely powered and dissolvable. My research focuses on the battery of the delivery device, developing a novel polymer-based energy storage system. By characterizing metrics like capacity, discharge rate, and resistance of different polymers, this battery system would be optimized for peak performance, which would allow for more sophisticated ailments to be treated easier than ever.

Sarah Hernandez ’25

Chemical Engineering, Concentration in Energy

Advisor: Fikile Brushett, Associate Professor, Chemical Engineering
Direct Supervisor: Katelyn Ripley, Graduate Student, Chemical Engineering
Sponsor: Friends of MITEI UROP

Technoeconomic comparison of electrochemical and amine-based CO2 separation processes
There is a need for carbon dioxide (CO2) capture processes that are energy efficient and low cost to adequately mitigate the excessive release of CO2 into the atmosphere. Novel electrochemical CO2 capture systems may meet this need, but, to the best of our knowledge, there are limited techno-economic comparisons between commercially deployed thermochemical systems and potentially lower-cost electrochemical systems. Using a process modeling platform, I develop and optimize a state-of-the-art thermochemical CO2 capture system that allows for direct cost and performance evaluations against electrochemical alternatives. This modeling aids in quantifying key cost contributors for each system while also guiding future research directions and policy in the carbon capture field.

Rachel Jiang ’27

Electrical Engineering and Computer Science

Advisor: Bill Green, Director, MIT Energy Initiative
Direct Supervisor: Guiyan Zang, Research Lead, MIT Energy Initiative
Sponsor: Friends of MITEI UROP

Identification of the steel decarbonization options for different regions
Carbon dioxide emissions from the steel industry need to be reduced to meet the net zero CO2 emissions objective by 2025. The steel industry relies heavily on coal and energy-intensive processes, making decarbonization both challenging and necessary. Our research conducts techno-economic analysis (TEA) and life cycle analysis (LCA) on steel decarbonization by evaluating region-specific data on production capacity, energy use, and emissions in China, Japan, Germany, Turkey, South Korea, and Brazil. By identifying practical decarbonization strategies like fuel switching, technological innovations, and carbon capture and storage, our project develops an optimization framework to determine the most practical steel decarbonization scenarios according to cost, emissions, and demand in different regions. This project will provide actionable pathways that significantly reduce carbon emissions while balancing cost-effectiveness and meeting regional steel demand.

Advisor: Tomás Palacios, Director, 6-A MEng Thesis Program; Professor, Electrical Engineering
Direct Supervisor: John Niroula, Research Fellow, Electrical Engineering and Computer Science
Sponsor: Shell

Characterization of GaN devices for high-temperature applications
Geothermal energy is a promising clean and renewable resource that has not been fully utilized in part due to the limitations of current electrical devices and their inability to operate at the necessary extreme temperatures (~500°C). To unlock the full potential of geothermal energy, our research focuses on enhancing the internal components of geothermal drilling technology by investigating gallium nitride (GaN), a semiconductor material that enables high performance electronic devices at these extreme temperatures. Specifically, we investigate novel dielectric passivation layers and their effects on GaN devices after exposure to such high temperatures over long periods of time. By developing robust, high-temperature semiconductor devices, we can significantly improve the safety, efficiency, and sustainability of electronic systems for next generation geothermal power plants.

Jackson Kay ’27

Electrical Engineering and Computer Science

Advisor: Tonio Buonassisi, Professor, Mechanical Engineering
Direct Supervisor: Alexander Siemenn, Graduate Student, Mechanical Engineering
Sponsor: Friends of MITEI UROP

Machine learning research for autonomous accelerated materials discovery device
My research focuses on optimizing the ZoMBI-Hop algorithm, a cutting-edge method for efficiently locating high-performing materials in vast compositional spaces. This research targets the improvement of perovskite solar cells, which have the potential to surpass traditional silicon-based photovoltaics in cost-efficiency and performance. By enhancing ZoMBI-Hop to identify multiple promising material compositions (or “needles”) within a given dataset, we aim to streamline the discovery process, reducing time and resource consumption while increasing the likelihood of finding materials with desirable properties. To evaluate these improvements, I have devised a comprehensive benchmarking system for ZoMBI-Hop. This system assesses the algorithm’s accuracy, complexity, speed, and adaptability to higher-dimensional search spaces. Insights from this analysis guide the refinement of model hyperparameters and the exploration of new strategies for managing multi-optimum searches. These enhancements will ultimately contribute to the creation of cheaper, more efficient perovskite solar cells, bolstering the economic and environmental viability of renewable energy technologies. Beyond this, my work integrates machine learning models with autonomous material sampling apparatuses, offering a scalable solution for addressing complex discovery challenges across various domains. By combining advanced algorithms with physical experimentation, this project demonstrates the transformative potential of AI in scientific research and innovation.

Nebus Kitessa ’25

Mechanical Engineering, Concentration in Product Design

Advisor: John Ochsendorf, Professor, Architecture
Direct Supervisor: Nia Iman Rich, Graduate Student, Architecture and Planning
Sponsor: ExxonMobil

Shoreline project expansion
Despite significant advancements in the size and accessibility of solar panels, only 6% of our energy currently comes from solar power. There have been efforts to integrate solar panels into everyday items like clothing and bags, but most of these solutions fail to generate enough power to fully charge a phone. My research this summer focused on designing a solar-powered bag that maximizes surface area to generate sufficient power for charging small electronic devices. This innovation will help integrate solar panels into everyday items, reducing the need for separate installations and saving households money. By making solar energy more practical and accessible, we can promote cleaner energy use in daily life.

Danielle Knutson ’27

Urban Studies and Planning with Computer Science

Advisor: Anuradha Annaswamy, Senior Research Scientist, Mechanical Engineering
Direct Supervisor: Vineet Jagadeesan Nair, PhD Student, Mechanical Engineering
Sponsor: Friends of MITEI UROP

Improved dataset curation and synthetic data generation for realistic DER-rich grid studies
The rapid decarbonization of the power grid is essential to emissions reduction goals, which will entail a transition from fossil fuels to distributed energy resources (DERs) such as renewables, batteries, and electric vehicles. However, current datasets on DERs are scarce and often incomplete, which hinders their accurate modeling and optimization in grid studies. My work tackles this by (i) compiling datasets from many public sources and (ii) leveraging various statistical and machine learning methods to fill gaps in existing data for multiple regions in the United States, in order to empower researchers, policy makers, and industry professionals. In addition to understanding the current state of DERs, I’m working on predicting future growth trends until 2050 and providing a large, high-quality, and open-source database for researchers. This database will be highly valuable not only to support grid decision-making and accelerate DER integration but also to enhance energy affordability, reliability, and equity. It will improve quality of life by fostering cleaner air, lower emissions, and more resilient communities, thus supporting a sustainable future.

Lauryn Kortman ’24

Materials Science and Engineering

Advisor: Michael Short, Associate Professor, Nuclear Science and Engineering
Direct Supervisor: Alexis Devitre, Graduate Student, Nuclear Science and Engineering
Sponsor: Friends of MITEI UROP

Quantifying cryogenic stored energy release in irradiated YBa2C3O7 through molecular dynamics annealing simulations
Magnetic fusion energy has the potential to provide abundant, carbon-free electricity, anywhere, any time. But the bombardment of fusion neutrons can affect the longevity of fusion magnets, responsible for the confinement field that sustains fusion in the core. My research utilizes molecular dynamic simulations to quantify the energy stored in radiation-induced defects. This quantification will inform strategies to prevent local hotspots in superconducting magnets (called quenches) that can severally damage these expensive and critical camponents. By mitigating the risk of quenches, my research increases the operational certainty and overall attractiveness of fusion power.

Ronaldo Lee ’28

Electrical Engineering and Computer Science

Advisor: Tonio Buonassisi, Professor, Mechanical Engineering
Direct Supervisor: Tianran Liu, Postdoctoral Associate, Research Laboratory of Electronics
Sponsor: Friends of MITEI UROP

Understanding the impact of environmental parameters during fabrication for highly reproducible perovskite solar cells
My UROP addresses the challenge of reproducibility in the fabrication of perovskite solar cells (PSCs), which are promising for high-efficiency, low-cost solar energy conversion. The efficiency of PSCs can vary significantly due to environmental factors during fabrication, such as temperature, humidity, and oxygen levels, affecting the crystal formation process crucial for performance and stability. My role involves designing, fabricating, and characterizing PSCs, controlling environmental parameters, and using a large language model (LLM) to analyze results and output optimized environmental factors for the greatest efficiency. This research aims to develop standardized fabrication protocols to enhance reproducibility, contributing to the scalability and commercial viability of PSC technology. By improving the reliability and accessibility of PSCs, especially for energy-poor communities, this work supports a just energy transition, promoting equitable access to renewable energy through more efficient and reproducible perovskites solar cells.

Trent Lee ’26

Materials Science and Engineering

Advisor: John H. Lienhard, Abdul Latif Jameel Professor of Water, Mechanical Engineering
Direct Supervisor: Zi Hao Foo, Graduate Student, Mechanical Engineering
Sponsor: Friends of MITEI UROP

Nanofiltration for valuable metal extraction from aluminum waste cryolite
The current production process of aluminum, which is heavily used in the manufacturing of automobiles, drink cans, and electrical wiring, is problematic because of the large quantities of hazardous waste it generates. My project is investigating the viability of nanofiltration as a method for purifying this waste cryolite stream to recycle the aluminum that is otherwise unused. From experimental data, I have achieved significant aluminum retention (>98%), while separating it out from 70-95% of other metal species—a result that would reduce hazardous waste and enhance aluminum’s circular economy.

Erik Liang ’26

Nuclear Science and Engineering

Advisor: Stephen J. Wukitch, Principal Research Scientist, Plasma Science and Fusion Center
Direct Supervisor: Andrew Seltzman, Research Scientist, Plasma Science and Fusion Center
Sponsor: Friends of MITEI UROP

Optimizing a lattice-based raft for reduction of L-PBF build plate warping
Additive Manufacture (AM) with Laser Powder Bed Fusion (L-PBF) is an emerging technique enabling rapid development of fusion system components in configurations otherwise unachievable through traditional manufacturing techniques. However, thermal stresses induced due to rapid heating and cooling cycles during the printing process can distort the build plate, particularly larger parts. Newly developed underlying support structures first tested on an LCD-based stereolithography (SLA) photopolymer resin printer decouples the stress transfer reducing build plate distortion in L-PBF. Use of these support structures during L-PBF of an existing reactor component reduced distortion by 40% compared to the component printed without the support structure.  These support solutions will be implemented in future L-PBF prints to improve precision of future fusion components.

Elaine Liu ’24

Mathematics

Advisor and Direct Supervisor: Marija Ilić, Adjunct Professor, Electrical Engineering and Computer Science; Senior Research Scientist, Laboratory for Information and Decision Systems
Sponsor: Friends of MITEI UROP

Multi-layered optimization for coordinated electrical vehicle charging
The rapid adoption of electric vehicles (EV) introduces higher demand for electricity, which stresses the power grid, especially when drivers come home from work in the evening. Our research recognizes diverse charging needs and power grid constraints and we designed a multi-layer coordination scheme to maximize social welfare for all market participants that include generators, demand aggregators, and EV owners. Our research shows that, given the right market structure and incentives, companies will optimize for their charger allocation and retail pricing in this way and ensure our power grid is efficient and sustainable.

Leala Nakagawa ’27

Mechanical Engineering

Advisor: Tonio Buonassisi, Professor of Mechanical Engineering, Mechanical Engineering
Direct Supervisor: Tianran Liu, Postdoctoral Associate, Research Laboratory of Electronics
Sponsor: Friends of MITEI UROP

Advancing stability testing equipment for perovskite solar cells
Thin-film perovskite solar cells show high potential for cost-effective production and energy conversion efficiency, but they degrade quickly as their stability lags behind. To accelerate research on improving their stability, we are developing specialized equipment that tests large quantities of solar cell samples under lighting conditions similar to the sun’s illumination. This equipment would be used for high throughput experiments that could lead to development of market-ready perovskite solar cells.

Vinn Nguyen ’27

Mechanical Engineering

Advisor: Tonio Buonassisi, Professor of Mechanical Engineering, Mechanical Engineering
Direct Supervisor: Tianran Liu, Postdoctoral Associate, Research Laboratory of Electronics
Sponsor: Friends of MITEI UROP

Exploration of high-entropy perovskites for improved performance and stability
Perovskite solar cells offer a promising alternative to traditional silicon-based cells, thanks to their high efficiency and lower manufacturing costs. However, challenges remain in ensuring the reproducibility and long-term durability of these cells, particularly under real-world operating conditions. Our research focuses on investigating how various fabrication parameters—such as temperature and humidity—affect cell performance and stability. By addressing these factors, we seek to understand the reproducibility and enhance the stability of perovskite solar cells. Advancing the reproducibility and durability of perovskite solar cells is critical for the broader adoption of renewable energy, reducing dependence on fossil fuels, and accelerating the global transition to sustainable energy sources.

Almira Nurlanova ’27

Chemistry

Advisor: Heather J. Kulik, Lammot du Pont Professor of Chemical Engineering, Chemical Engineering
Direct Supervisor: Husain Adamji, Graduate Student, Chemical Engineering
Sponsor: Friends of MITEI UROP

Leveraging machine learning to accelerate MOF discovery for methane to methanol conversion
The current state of the industry of synthesizing methanol is posing a threat to the environment, mainly due to the susceptibility of methanol to overoxidation to carbon dioxide, a greenhouse gas. In nature, however, metalloenzymes like methane monooxygenases (MMOs) have demonstrated capability to convert methane to methanol under mild conditions, such as ambient temperature. Our project is the development of a Molecular Organic Framework that could synthesize industrially valuable methanol, while reducing greenhouse gas emissions associated with current methods of methanol synthesis. This project has a potential to lower the carbon footprint in this industry, which will matter in the long run to create a green society.

Mairin O’Shaughnessy ’27

Electrical Engineering and Computer Science

Advisor: Martin Bazant, Professor of Chemical Engineering and Mathematics, Chemical Engineering
Direct Supervisor: Sam Degnan-Morgenstern, Graduate Student, Chemical Engineering
Sponsor: Friends of MITEI UROP

Learning the material physics of graphite electrodes through image inversion
The material physics of graphite, a predominant component in lithium-ion batteries, are currently not well understood, slowing improvements in the development of battery technologies. We are developing a machine learning based image pre-processing pipeline to prepare graphite microscopy images for further analysis, removing noise from the data and identifying particles at different reaction stages. With a processed data set, further work to learn graphite properties can be expedited and more informed development of lithium-ion batteries at all stages of life is possible.

Joshua Rivera Camacho ’27

Mechanical Engineering

Advisor: Matteo Bucci, Esther and Harold E. Edgerton Associate Professor, Nuclear Science and Engineering
Direct Supervisor: Matthew Hughes, Postdoctoral Associate, Nuclear Science and Engineering
Sponsor: ExxonMobil

Experimental investigation of natural convective heat transfer in the presence of bubbles
A third of the nation’s electricity generation comes from boiling heat transfer, as well as nearly all of our refrigeration and air conditioning. However, little is known about boiling mechanics, as boiling is an erratic, difficult-to-measure process. This can lead to huge overheating issues which can lead to devastating crises. Though prediction of these crises is possible, it is limited due to lack of understanding of boiling mechanics. My research focuses on designing an experiment which investigates the effect of bubble agitation on heat transfer. By separating one boiling mechanic, we can construct more accurate and meaningful heat transfer models, and by continuing to investigate boiling mechanics, we can lower the price and environmental footprint of many essential power systems while also improving their efficiency.

Sebastian Rotella ’24

Chemical Engineering

Advisor: Robert Stoner, Founding Director, Tata Center for Technology and Design
Direct Supervisor: Bosong Lin, Postdoctoral Associate, MIT Energy Initiative
Sponsor: Friends of MITEI UROP

Cost-performance analysis and benchmarking of CO2 capture systems for hard-to-abate industries
Carbon capture and storage (CCS) technologies are a promising solution to addressing the substantial emissions originating from hard-to-abate industries. However, there are many existing CO2 capture systems, and some may be better suited depending on the properties of the flue gas stream. We are developing process simulations of existing and emerging CO2 capture systems to evaluate their cost and performance across different industrial flue gas streams, and in doing so we will identify which CO2 capture systems are best suited for specific industrial processes. By benchmarking the cost-performance analysis of the various CO2 capture systems, we hope to guide future implementation of CCS technologies to be as cost effective and energy efficient as possible.

Advisor: Robert Stoner, Founding Director, Tata Center for Technology and Design
Direct Supervisor: Bosong Lin, Postdoctoral Associate, MIT Energy Initiative
Sponsor: Friends of MITEI UROP

Optimizing rollout of hydrogen for industrial decarbonization
A significant challenge in the battle against climate change lies in mitigating the substantial greenhouse gas emissions originating from hard-to-abate industrial sectors, which collectively contribute to nearly one-third of global emissions. Among the promising solutions to address these emissions, carbon capture and storage (CCS) technologies stand out as crucial decarbonization enablers by being able to significantly reduce emissions from existing industrial plants without disrupting vital industrial processes. However, since many of the industrial emissions are at high temperatures (300 to >1,400°C), conventional CCS technologies require cooling the flue gas to enhance the capture efficiency, which in turn requires more energy and leads to wasted low-grade heat. We are developing process simulations of emerging CCS technologies (such as molten salt) that offer the potential to operate the capture process at temperatures closer to those of the emissions. By evaluating their cost and performance across different industrial flue gas streams in comparison to conventional CCS technologies, we will identify which CCS technologies are best suited for specific industrial processes and help guide the growing implementation of CCS.

Sonia Seliger ’26

Economics and Finance

Advisor & Direct Supervisor: John Parsons, Senior Lecturer, Sloan School of Management; Deputy Director for Research, Center for Energy and Environmental Policy Research
Sponsor: ExxonMobil

Electricity market design for incentivizing investments in battery storage
To achieve its ambitious renewable energy goals, New York State has implemented numerous incentives and policies for including managing battery storage in the energy market. Our project researches how market participants are establishing and utilizing battery storage under these incentive structures by documenting these policies, analyzing the scale of battery investments, and investigating the business models that enable battery owners to profit from these investments. This allows us to gauge the effectiveness of these policies and inform future strategies and market designs to encourage investments in battery storage and a transition to lower-carbon energy systems.

Sokh Visal Soeun ’26

Artificial Intelligence and Decision Making

Advisor: Saurabh Amin, Professor, Civil and Environmental Engineering
Direct Supervisor: Rohit Parasnis, Postdoctoral Associate, Civil and Environmental Engineering
Sponsor: ExxonMobil

A data driven approach to sustainable forestry
As the Indonesian government licenses plots of lands called concessions to palm oil farmers, concession owners have cut down trees to clear land for their plantations, to the point that deforestation due to palm oil has become a leading cause of forest loss in the country. Our team proposes that the concessions neighboring each other share tools, labor, and technology with each other, and this so-called “network effect” leads to increased deforestation rates across all involved concessions. We are developing models that quantify this relationship and hope to use the models to develop policies to reduce the deforestation rate due to network effects in Indonesia.

Emilia Szczepaniak ’27

Electrical Engineering and Computer Science

Advisor: Tonio Buonassisi, Professor, Mechanical Engineering
Direct Supervisor: Tianran Liu, Postdoctoral Associate, Research Laboratory of Electronics
Sponsor: Friends of MITEI UROP

Applying machine learning algorithms to select capping layers for efficient and stable perovskite solar cells
Nonrenewable energy sources raise global temperatures and accelerate carbonization, demanding diverse, accessible clean energy solutions to combat climate change. At the Accelerated Materials Laboratory for Sustainability, we are studying perovskite solar cells (PSC), an alternative to commercialized silicon solar cells, and experimenting the effect on power efficiency from the cells’ 2D capping layer materials suggested by machine learning algorithms. Interpreting power efficiencies of 2D PSCs with machine learning helps answer the open-endedness of possible encapsulation materials to research; this brings us closer to finding reproducible methods that can commercialize effective PSCs and expand clean energy solutions in the long run.

Catherine Tang ’25

Electrical Engineering and Computer Science

Advisor: Cathy Wu, Thomas D. and Virginia W. Cabot Career Development Associate Professor, Civil and Environmental Engineering
Direct Supervisor: Vindula Jayawardana, Graduate Student, Electrical Engineering and Computer Science
Sponsor: Friends of MITEI UROP

Long horizon driving trajectory prediction with LLMs
Transportation is the largest contributing sector to carbon emissions in the United States, highlighting the critical need for effective roadway interventions to mitigate its environmental impact. Evaluating the effectiveness of these interventions requires models that accurately capture human driving behaviors. However, modeling human driving presents significant challenges due to the complex interplay of factors such as inter-vehicle dynamics and human decision-making processes. We seek to build realistic human driver models using real-world human driving data and generative machine learning techniques, formulating the challenge as a next-token prediction task. Our next-token prediction model combines interactions between vehicle trajectories, road topology, and upstream traffic dynamics to achieve realistic and scalable agent-based traffic flow simulations. With a more realistic simulation of human driving behavior, researchers can study roadway interventions such as eco-driving to reduce the impact of emissions from transportation.

Jordan Tierney ’25

Materials Science and Engineering

Advisor: Asegun Henry, Associate Professor, Mechanical Engineering
Direct Supervisor: Seiji Engelkemier, Graduate Student, Mechanical Engineering
Sponsor: Friends of MITEI UROP

Material feeder for hydrogen pyrolysis
Hydrogen has a wide variety of important industrial applications, from fuel to chemical production, however the current dominant production method results in climate-warming carbon dioxide emissions. We are developing a reactor for methane pyrolysis, a pathway which utilizes high-temperature chemical reactions to turn methane into hydrogen and solid carbon. Solid carbon is a preferable byproduct to carbon dioxide, as it can be collected and sold for use in other applications, rather than being released into the atmosphere.

José Vargas ’27

Physics, Electrical Engineering and Computer Science

Advisor: Anuradha Annaswamy, Senior Research Scientist, Mechanical Engineering
Direct Supervisor: Vineet Jagadeesan Nair, PhD Student, Mechanical Engineering
Sponsor: Friends of MITEI UROP

Machine learning for energy forecasting
Energy forecasting is crucial for addressing grid instability, as inaccuracies in predicting demand and generation can lead to power outages or energy overproduction. This results in inefficiencies, higher costs, and increased environmental impact. Inaccurate forecasts can force reliance on backup power sources, often fossil fuel-based, which increases greenhouse gas emissions. Additionally, overproduction may lead to unnecessary energy waste, further straining natural resources. Using Python packages and probabilistic machine learning models, such as AutoTS and Bayesian neural networks, this research improves energy demand and generation predictions. The models address uncertainties like randomness in energy generation and errors in forecasting by validating predictions with real-world data. Accurate forecasting helps integrate renewable energy, reduce fossil fuel reliance, and minimize energy waste. It ensures more reliable and sustainable energy grids, preventing outages and supporting a just energy transition.

Vandell Vatel ’26

Artificial Intelligence and Decision Making

Advisor & Direct Supervisor: John Parsons, Senior Lecturer, Sloan School of Management; Deputy Director for Research, Center for Energy and Environmental Policy Research
Sponsor: CEEPR

Decarbonizing New England’s electric grid
New England aims to be net zero in terms of carbon emissions by 2050 to aid in a global effort to curb the effects of global warming. The MIT Energy Initiative’s GenX model is a tool for identifying cost-efficient portfolios of generation capacity. My project focuses on fine tuning GenX so that the model it generates more accurately mirrors New England’s electric grid. The more accurate the model, the more we can trust GenX’s investment decisions, and the more reliable of a resource it becomes.

Annika Vivekananthan ’26

Artificial Intelligence and Decision Making

Advisor: Cathy Wu, Thomas D. and Virginia W. Cabot Career Development Associate Professor, Civil and Environmental Engineering
Direct Supervisor: Yining Ma, Postdoctoral Associate, Laboratory for Information and Decision Systems
Sponsor: Friends of MITEI UROP

Learning to optimize vehicle routing problems in real-world road networks
It’s challenging to efficiently route vehicles through complex, ever-changing city road networks, but applying transformer-based machine learning models to non-Euclidean transportation data can help overcome this difficulty. By refining and adapting neural combinatorial optimization techniques, our work aims to deliver more reliable and cost-effective routing solutions. Ultimately, this research could streamline urban logistics, reduce congestion and emissions, and pave the way for more sustainable, data-driven transportation systems.

Audrey Wei ’27

Artificial Intelligence and Decision Making, Mathematics

Advisor: Crystal Owens, Postdoctoral Fellow, Computer Science and Artificial Intelligence Lab
Direct Supervisor: Wojciech Matusik, Joan and Irwin M. (1957) Jacobs Professor, Electrical Engineering and Computer Science
Sponsor: Friends of MITEI UROP

Digitize soft solids via visual computing
The unpredictable rheological properties of soft solids, such as biomass fuel slurries, impede efficient renewable energy conversion and pose challenges for modeling geophysical flows like lava and mudslides. We utilize neural networks to extract and simulate the physical properties of soft solids from video footage, integrating physics-based simulations for real-time adaptability and enhanced predictive accuracy. Leveraging video-based techniques such as 3D reconstruction methods and physics-based models, our approach enables the identification of material properties like viscosity, elasticity, and flow dynamics. These properties are then incorporated into standard constitutive models, ensuring accurate simulations of real-world behaviors under diverse conditions. This research supports scalable renewable energy by enabling optimized biomass energy systems that are both efficient and reliable. Additionally, the insights gained from modeling geophysical flows contribute to improved disaster preparedness, mitigating risks and safeguarding vulnerable communities. By advancing our understanding of material behavior under diverse conditions, this work addresses critical challenges in renewable energy and environmental resilience.

Jansen Wong ’26

Computer Science and Engineering

Advisor: Christopher Knittel, George P. Shultz Professor of Energy Economics, Professor of Applied Economics, MIT Sloan School of Management
Direct Supervisor: Yifu Ding, Postdoctoral Associate, MIT Energy Initiative
Sponsor: Friends of MITEI UROP

Mapping data-driven coal retrofitting solution with geospatial synthetic power system datasets for India
IAP: Without strategic interventions, operational coal power plants in India hinder progress toward ambitious climate goals, including a national net-zero target by 2070. Leveraging Geographic Information Systems (GIS), development of a comprehensive database, and machine learning, this research project focuses on visualizing effective retrofitting strategies for 284 operational coal power plants. Our interactive web platform and database could provide policy makers and stakeholders involved in India’s energy transition with plant-level strategies as they design retrofitting initiatives.

Spring: India’s 806 coal-fired power units, characterized by aging boilers and low thermal efficiency, impede progress toward the country’s climate goals. This is compounded by incomplete documentation regarding their characteristics, including thermal efficiency. Employing a machine learning methodology, we construct a comprehensive database detailing the operational features of all extant coal plants in India, integrating environmental considerations like water stress and coal price, alongside the development of a visualization tool to enhance accessibility and understanding of our datasets. The ultimate aim is to furnish policy makers with accurate data for computing carbon emissions from coal plants, thereby enabling informed decisions regarding decarbonization strategies.

Katherine Zhou ’27

Mechanical Engineering, Concentration in Electrical Engineering and Computer Science

Advisor & Direct Supervisor: John Parsons, Senior Lecturer, Sloan School of Management; Deputy Director for Research, Center for Energy and Environmental Policy Research
Sponsor: ExxonMobil

The place of provincial and international trade in electricity to maximize the value of Quebec’s hydro resources
Hydro generation can play a valuable role as a flexible, renewable energy resource. This project uses MITEI’s GenX program to identify the lowest-cost method of allocating investments towards various renewable energy technologies, energy storage, and transmission lines across the northeast region of the US and Canada. My contribution was to dive deeper into mapping and analyzing Ontario’s current hydroelectric resources—generating stations, capacities, and major river sources—so that this more detailed data can be used to garner a more accurate depiction of efficient renewable energy technology implementation.

Aaron Zhu ’24

Business Analytics, Electrical Engineering and Computer Science

Advisor: Tonio Buonassisi, Professor, Mechanical Engineering
Direct Supervisor: Alexander Siemenn, Graduate Student, Mechanical Engineering
Sponsor: Shell

Hardware and sensor design/optimization for an autonomous solar cell fabrication lab
Perovskite solar cells depend on a wide variety of factors (such as temperature, humidity, chemical concentrations, etc.) that are incredibly difficult to precisely measure and control for. With the creation of autonomous, enclosed testing chambers that allow for quick and accurate collection of environmental data, we are able to derive accurate results and form sound conclusions to understand what factors truly affect perovskite fabrication, ultimately advancing perovskite research at a quicker pace.