Technology

Electrical and Computer Engineering

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Mon, 03/10/2025 - 13:07

Two years after the launch of ChatGPT, generative AI has yet to produce a game-changing use case. Professor Paul Watta breaks down whether the trillion dollar bet on generative AI will pay off or go bust.

Electrical and Computer Engineering Professor Paul Watta. Photo by Annie Barker

For a ��-Dearborn student and professor, the influence goes both ways

lblouin — Mon, 24 Feb 2025 13:06:36 +0000

For a ��-Dearborn student and professor, the influence goes both ways lblouin Mon, 02/24/2025 - 08:06

In 2020, Electrical and Computer Engineering Professor Wencong Su was looking for an undergraduate student to assist with a project focused on adding innovative energy efficiency measures to the already LEED-certified Engineering Lab Building. At the time, Su was teaching all graduate courses so he didn’t have many undergrads in his orbit. So Su asked for a little help from one of his graduate assistants, who emailed the job posting to the students in a 200-level lab. LaRico Andres, a sophomore and recent transfer from Henry Ford College, thought it sounded interesting and reached out to Su with his resume. Su remembers Andres didn’t exactly fit the description of the type of student who usually applies for these kinds of opportunities. Andres was a few years older than Su, already had a full-time job working as an instrument tech at the Great Lakes Water Authority, and his GPA at the time wasn’t knocking anyone out. But after the two chatted for a little while, Su offered him the job. Andres was personable, seemed excited to learn, and though he was a research novice, it turned out his background in instrumentation was actually quite relevant to the types of things they would be investigating in the ELB.

Andres excelled on the project and it led to many more opportunities to work alongside Su. On one project supported by the National Science Foundation’s I-Corps — a program that helps researchers translate their laboratory work into marketable products — Andres traveled to conferences in Orlando, San Diego and Austin, completing a flurry of more than 100 interviews with industrial professionals in seven weeks about a product Su’s lab was developing. On another project, he and Su created an app that used the ELB’s building management software and the utility’s fuel profile to give users an idea of what being a building occupant added to their personal carbon footprint. As you’d expect, Andres picked up a lot of skills working with Su. But as often happens when two people spend a lot of time together, Andres and Su also started talking about things other than their work. Andres jokes that they initially bonded over the fact that they “both like to eat.” But the conversations gradually grew more personal. Both men had daughters, so they talked about their kids. Su shared some of the challenges he faced when he first came to the United States for college and he knew very little English. Andres reciprocated with stories about some challenges he faced growing up in Detroit, not always having enough money to pay all the bills at once, and some things that happened when he was younger that tripped up his plans to go to college earlier in his life.

Su says he enjoyed Andres' perspective — in part, because Su is someone who’s interested in people, and the ups and downs of Andres’ life made for a compelling personal story. In addition, Su says he’s always been interested in Detroit, and through Andres, a lifelong Detroiter, he enjoyed learning about a side of the city that “you don’t hear about on the TV news.” Interestingly, at a certain point, both men noticed that many of their personal conversations weren’t actually that separate from their field of study: energy systems. For example, Andres shared stories about how, in his family, when the power would go out, that meant you didn’t open the fridge so his grandfather’s insulin wouldn’t spoil. Or how, if the power was out long enough, they’d start barbequing the meat they had in the freezer so it wouldn’t go to waste. Or how he and his relatives used to take turns refilling his grandmother’s boiler when she couldn’t afford to fix it right away. “The biggest burden a lot of my people have is energy, whether it’s fuel, gassing up your car going to work, or just trying to keep your house warm,” Andres says. “I know what it’s like to struggle to pay a utility bill and choose between paying that bill on time or buying food. I know people whose furnaces go out and can’t afford to get them fixed. That’s the impact energy has. Energy helps. But if you don't have a lot of money, energy can be a burden.”

Andres’ testimony left an impression on Su, and eventually the two of them started kicking around ideas for projects focused on energy justice, an emerging field that focuses on equity, affordability, accessibility and participation in the energy system and the ongoing transition to new energy technologies. That lens became the cornerstone of their 2023 Summer Undergraduate Research Experience project, in which Andres helped create an interactive mapping tool that explores the connection between historical real estate redlining and disparities in energy access in metro Detroit. Using Andres’ dashboard, a user can quickly toggle through different layers and explore whether a neighborhood is formerly redlined, where current utility outages are and their causes, locations of renewable energy installations and EV charging stations, as well as census data that provides demographic information about the people currently living in the neighborhood. One of the big reveals from Andres’ work is that metro Detroit neighborhoods that were historically redlined tend to be lower-income, non-white, have power outages that tend to be longer, and have less access to renewable energy and EV charging infrastructure. Their work on the app led to an IEEE conference publication in 2023.

Andres shows off an interactive mapping tool he helped create that explores the connection between historical real estate redlining and disparities in energy access. Photo by Annie Barker

After the project, Andres was nearing the end of his undergraduate career, and Su began asking him a little more frequently and persistently if he’d ever thought about grad school. Andres says that honestly wasn’t on his radar when he initially transferred to ��-Dearborn. At that time, his thinking pretty much revolved around the straightforward calculus that getting an engineering degree would help him get a higher-paying job and lead to a more comfortable life for him and his family. But after working in Su’s lab, particularly on the energy justice-focused mapping project, he says his perspective began to change. “If I did the safe thing and got a job, yeah, that would help me. But working with Professor Su helped me see what it is that I really wanted to do. And what I really wanted to do was help people,” Andres says. To do that, Andres decided to take a chance and continue his education, enrolling in the department’s doctoral program, where he’s now continuing his work alongside Su. Andres is still just in his first year of the program, but the pair have already started discussing ideas for Andres’ dissertation research. Not surprisingly, energy justice topics are rising to the top of their list. One idea they’ve been kicking around recently is creating a machine learning-based app that would use weather and outage data to forecast the likelihood of power outages, sort of like the snow day prediction models that have become super popular with parents and kids. “A technology like this could have multiple applications,” Su says. “It could help a utility identify where they need to upgrade their infrastructure to make it more resilient, or where they might need to deploy the most crews ahead of a storm. But it could also directly help people. Like, if you saw that there was a 90% chance you were going to lose your power for five hours tomorrow, people could charge their batteries or make a trip out to get food.”

Andres says it’s still a little wild to think he’s in a PhD program, and the future he’s contemplating for himself now is much different than the one he was thinking about a few years ago. For the time being, he’d love to continue to do research in the energy justice field and develop innovative ways to help people. He also wants to start a nonprofit that gets young people excited about things like sustainable energy technologies and smart cities. Ultimately, if all goes well, he’d love to be a college professor just like his mentor. “It’s funny, the opportunities that come up in your life,” Andres says. “Like, what if I had ignored that email? What if I hadn’t stepped out and emailed professor Su? He saw something in me that I didn’t even know about myself. And he probably doesn't even know how many other people that’s going to touch. My daughter and little cousins, they all get to see that, ‘Hey, Uncle Rico is gonna be a doctor.’ Because of Professor Su, I’m setting a new example for the people in my life. My wife, sister and other family members, everybody is, like, ‘Wow, you was always smart.’ I just went the long way. And I’m just glad I have an opportunity now to make an impact in the community.”

That a college professor would have such a profound impact on a student isn’t super surprising. It’s what many faculty shoot to achieve every day. But Su says Andres has also had a big influence on him. On a personal level, he finds Andres' life so inspiring, and admires his work ethic, the personal sacrifices he’s making to be part of the PhD program, and his vision for using research to help people in his community. Their relationship has also had a big influence on Su’s own work. “For example, losing power for one hour in a wealthy neighborhood compared to a disadvantaged neighborhood, it has totally different impacts. Or if you lose power, then that suddenly becomes a food issue for some people. I never really thought about stuff like that before,” Su says. “Now, I'm thinking more about how our research has the potential to help people. Because, otherwise, maybe you’re just publishing a paper with some very fancy solutions and complicated equations. But how are you helping average people?”

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Story by Lou Blouin

Electrical and Computer Engineering

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Mon, 02/24/2025 - 12:56

Unlikely collaborators who met through a job posting, first-year PhD student LaRico Andres and Professor Wencong Su have forged a partnership that’s left a lasting mark on both men.

Electrical and Computer Engineering Professor Wencong Su (left) and doctoral student LaRico Andres have teamed up on a number of research projects focusing on energy justice. Photo by Annie Barker

Helping nuclear magnetic resonance spectroscopy go hi-res

lblouin — Wed, 19 Feb 2025 13:48:43 +0000

Helping nuclear magnetic resonance spectroscopy go hi-res lblouin Wed, 02/19/2025 - 08:48

Whether you’re talking about MRI, which doctors use to image tissues in the body, or the brand of nuclear magnetic resonance spectroscopy used by organic chemists, Assistant Professor of Chemistry Christos Constantinides says the core idea behind the technique is basically the same. You start with a sample — in organic chemistry, it’s a compound you want to know the structure of, and in an MRI, it’s your body — and you surround it within a powerful magnet. The magnetic field causes the nuclei in the atoms in the sample, which have naturally occurring random spins, to momentarily align these spins with the external field, either in a parallel (lower energy state) or antiparallel orientation (higher energy state). Then, you shoot radio waves at the sample, which causes the spins of the parallel-spinning nuclei to momentarily flip to an antiparallel state. When you turn off the radio waves, these flipped nuclei then “relax,” returning to their original orientation. That releases a small amount of energy as an oscillating magnetic field, which induces an electrical signal. This signal is detected and processed to generate an NMR spectrum in the case of molecular analysis. For MRI, it can be used to create an image of tissues in the body.

Constantinides says NMR is an extremely powerful technique, but it still has some limitations. Notably, certain substances give off very weak signals when they relax out of their “excited” state, which means the spectra generated through NMR often don’t tell you everything you want to know. Scientists have discovered various ways to enhance NMR’s powers. For example, with MRI, contrast dyes can help doctors see more details in the brain, heart, blood vessels, soft tissues and tumors. In materials chemistry, Constantinides says organic chemists use what are called polarizing agents, which are chemical compounds that are added in solution with the sample. Chemically speaking, these compounds are “radicals,” meaning they have at least one unpaired electron (most atoms have electrons which orbit the nucleus in pairs). In an NMR environment, Constantinides says these unpaired electrons are able to influence the nucleus of the molecules in the sample through spin polarization transfer mechanisms, indirectly assisting the “flipping” process that is essential to NMR imaging. “This basically increases the sensitivity of the technique,” he says. “So for molecules that are difficult to get a good NMR spectrum because they give very weak signals, by adding a little bit of this organic radical substance, it basically amplifies the signal and you get more detail.”

Polarizing agents have greatly enhanced NMR spectroscopy, but they aren’t universally effective. For example, Constantinides says today’s most common polarizing agents, known as nitronyl nitroxides, can only be used with certain kinds of substances, because these radicals react with compounds that oxidize easily. With a new $600,000 project funded by the U.S. Department of Energy and in conjunction with the Ames National Laboratory, Constantinides is looking to create novel polarizing agents that don’t have these limitations. He says when he describes this project to others, it ends up sounding like a lot of physics, because of the potential applications for NMR and MRI. But the day-to-day work will be a lot of advanced and, at times, unglamorous synthetic organic chemistry. During the three-year project, Constantinides estimates they’ll create 50 to 100 new derivatives of a class of molecules known as Blatter radicals — each of which takes weeks and a carefully planned sequence of chemical reactions to create. “Each compound requires six to 10 different steps,” he says. “One step can take multiple days to set up the chemical reaction, then you have to process it, clean it up a little bit and remove all the inorganic stuff, purify it, and then characterize it to see if you’ve made what you think you’ve made.” To assist with the labor-intensive research, Constantinides is hiring a postdoctoral research fellow and several undergraduates, which will give students an opportunity to get hands-on experience in some very advanced chemistry.

Constantinides in the lab with student research assistants Haidar Dakdouk (middle) and Carter Allen (front). Photo by Annie Barker

Even with this patient, methodical approach, Constantinides says success in organic chemistry is never assured. Over the years, he’s refined multiple techniques for creating certain kinds of molecules. But when you’re making something totally new, he says you never really know which methods will give you the best result — or whether your plan will even work — until you actually try it. “Maybe all of them fail, and then you have to try something totally different. It can be a lot of trial and error,” he says. As each new polarizing agent is created, Constantinides says they’ll first characterize it using the NMR setup at ��-Dearborn. After that, they’ll send the new compounds to the Ames National Laboratory, where they will be mixed in solution with substances that have well-known NMR profiles. By seeing how much of a boost in the signal the nuclei give off, they’ll know which new polarizing agents have the most potential to enhance NMR techniques.

Constantinides says some of his preliminary published research on this topic has shown a lot of potential for Blatter-type radicals, which is why the DOE has funded further work in this area. He says if all goes well, he’s hoping for two big applications: One, it’ll give materials chemists like himself tools for providing detailed characterizations of molecules which have been hard to study using other techniques. The even bigger payoff would be if one of the new molecules they create is suitable for use in MRI, which would give doctors much higher-resolution images of tissues and tumors.

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Story by Lou Blouin

Faculty Research

Research

College of Arts, Sciences, and Letters

Natural Sciences

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Wed, 02/19/2025 - 13:48

Nuclear magnetic resonance spectroscopy, used in technologies like MRI, helps scientists see the unseen. New research from Assistant Professor Christos Constantinides could help magnify NMR’s powers even more.

With a new $600,000 project funded by the U.S. Department of Energy, Assistant Professor Christos Constantinides is hoping to create novel chemical compounds that can enhance nuclear magnetic resonance-based technologies, like MRI. Photo by Annie Barker

Researchers prep for landmark field test of ‘second-life’ EV batteries

lblouin — Mon, 10 Feb 2025 15:30:35 +0000

Researchers prep for landmark field test of ‘second-life’ EV batteries lblouin Mon, 02/10/2025 - 10:30

Connecting large batteries to the electric grid is an idea that has multiple benefits. Most fundamentally, an electricity system powered by an increasingly large share of solar and wind will almost surely require storing energy in some fashion, given the variability of the wind and sunshine at any particular moment in the day coupled with our expectation that power will always be available. Indeed, large battery storage systems are already being deployed across the world in sophisticated ways. For example, a Massachusetts town of about 27,000 people is building a 15 MWh, grid-connected battery storage system that can help power the town during power outages, stock up on less-expensive energy during off-peak hours and feed electricity back to the grid during times of peak demand, when utilities generally have to fire up their most carbon-intensive diesel-powered backup systems. Grid-tied battery storage systems, however, come with a few caveats. For one, it takes a lot of batteries to store enough energy to actually make a significant difference, which means battery storage systems are expensive. Moreover, if you consider the climate emissions involved in manufacturing the batteries and the mining for the lithium and cobalt needed to make them, battery storage systems are far from being climate neutral.

Throw at this predicament a seemingly too-good-to-be-true solution: Right now, electric vehicles represent the largest use of large batteries, and EVs are steadily gaining market share across the globe. However, manufacturers estimate that batteries in new EVs will likely need to be replaced after eight to 12 years, or when they can hold about three quarters of their original charge. These slightly diminished batteries, however, could still be useful for other things, like — you guessed it — grid-tied storage. Now add into the mix the fact that EV sales have been strong for the past few years and we could be a decade or so away from having millions of used batteries ready for a “second life” in the electric grid. Importantly, this approach could reduce the costs and climate impacts of building large storage systems compared to those that use new batteries.

Undoubtedly, it’s an idea with tons of promise. The catch, at least for now, is that we don’t really know how well it will work, according to Associate Professor of Electrical and Computer Engineering Xuan Zhou. Zhou says our understanding of the performance of new EV batteries is pretty solid and getting better all the time. However, we simply don’t have the same level of data about what’s really going on inside used EV batteries to say for sure they’ll be as reliable an option for grid storage as we hope they’ll be. The reason is that charging and discharging a battery hundreds of times — and subjecting it to thousands of hours of road vibrations — changes its underlying chemical and physical properties. That’s ultimately why it loses capacity over time. And many details about a battery’s potential second life inside a grid storage system — like how many years it would last, how safe it would be and how cost-effective it would be — are still largely unknown.

Zhou, along with his Electrical and Computer Engineering Department collaborators Associate Professor Mengqi Wang and Professor Wencong Su, hope they’re about to provide the world with some of the best data-backed answers to date. With a new $1.48 million grant from the Michigan Department of Environment, Great Lakes, and Energy, the largest state grant in the university's history, the team of researchers is set to build a 500 kW grid-tied storage system that will use actual used EV batteries.

Su and Wang say some of the best work in this field is currently being done at Stanford University. But those experiments involve taking new EV batteries and subjecting them to hundreds of charging and discharging cycles in order to intentionally deplete them in a way that mimics what happens in an EV under certain driving conditions. “For the emulation, however, they might not be able to emulate the true environment the batteries would be operating in,” Wang says. “We can imagine, in an EV, there will be vibrations. There will be accelerations and decelerations. The devices around the pack might also generate heat. And there will be variations in the temperature and all these environmental conditions for the battery pack — they will not be constant at all. This all impacts the battery. So by using real used EV batteries, we hope our data will end up being more practical.”

From left, Associate Professor Xuan Zhou, Associate Professor Mengqi Wang and Professor Wencong Su.

Zhou says they are still working out many details for the design and build of the project. One of the unexpected challenges was simply locating used EV batteries. “Initially, it looked like we were going to have to get them from Europe, where EVs have been more popular,” Zhou says. “But now it looks likely that we will be able to get about eight to 20 battery packs from used GM vehicles.” After they secure the batteries, Zhou says they’ll perform extensive tests to get a sense of their chemical and physical properties. Meanwhile, Wang and Su will work out how the batteries can be connected and tied to the grid. Because the team is planning to test several types of use cases, this will require a carefully planned system of custom power converters, controllers and control algorithms. For example, they want to see how well the batteries perform in a “peak shaving” role. That’s when the batteries would feed electricity back to the grid during times of peak demand, which helps utilities avoid using their expensive, carbon-intensive backup systems. They also want to test “load shifting,” which is where the batteries would charge during non-peak hours and then release that energy during peak hours, when electricity is more expensive. Another variable: the partner site that will host the storage system has a solar array. Wang says connecting all those components and systems in a way that doesn’t produce harmonics on the grid, damage equipment or compromise power quality takes careful planning. Many of the components will have to be custom built, too, since this kind of system is not an off-the-shelf technology.

Another big hurdle the team might encounter is testing the system. At 500 kWh, this project is so big that the current testing facilities at ��-Dearborn — or any other university the team knows of — isn’t equipped to handle a test of the full system. Zhou says the new battery lab in Ann Arbor may be able to provide single pack-level testing, and they may need an industry partner for tests beyond that. Wang says the size and testing requirements also make it more challenging to design other aspects of the system. “I think the piece of equipment that has the highest power rating is our battery simulator in our IAVS lab, which has 100 kW testing capacity. So we’ll start with a smaller system as a proof-of-concept design. And then we’ll scale up to the real-world test. But this makes it much more challenging. The components, like the semiconductors or power converters, can’t just be scaled up. They’ll have to be swapped out with new hardware with higher power ratings. So we’re going to be busy!”

One other noteworthy feature of the project: After the study of the storage system is complete, the research team’s Genesee County-based industry partner on the project, ReCharge ReCycling, which is helping design and build the storage system, will help prepare the battery pack for a third life. “That’s why we’re calling this project ‘Closing the loop,’” Zhou says. “They’ll be dismantling the battery down to the cell level and then trying to recover the precious metals so they can be used to make a new battery. So we’ll really get a full picture of what it’s going to take to take a battery through this full cycle.”

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Story by Lou Blouin. Photos by Annie Barker.

Electrical and Computer Engineering

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Mon, 02/10/2025 - 15:30

Connecting millions of used electric vehicle batteries to the grid is an idea that holds tons of promise. But how well will this strategy work in the real world?

Associate Professor Xuan Zhou (right), Associate Professor Mengqi Wang (left) and Professor Wencong Su are heading up a new project that will tie used EV batteries to the electric grid.

How can we make driver assist features play nice together?

lblouin — Mon, 27 Jan 2025 13:14:53 +0000

How can we make driver assist features play nice together? lblouin Mon, 01/27/2025 - 08:14

The periodic cycles of excitement and disappointment over the emerging future of fully autonomous vehicles sometimes overshadow the reality that our vehicles are steadily getting more intelligent. Blind spot monitoring, which is often standard in new cars, will give you an alert anytime a driver (and maybe even a pedestrian) enters an area that generally represents the vehicle’s blind spot. Adaptive cruise control automatically maintains a preset distance to the car in front of you. Lane keep assist varies by vehicle, but in vehicles with electronic power steering, it can nudge your vehicle back between the lane markers if you start to drift too far. Autonomous emergency braking forcefully engages the brakes if your car detects an imminent frontal collision. And a total of eight 2024 models available in the U.S. offer some version of hands-free driving under certain conditions. This is on top of more convenience-focused technologies, like cars that parallel park for you and those with optional one-pedal driving, where the driver brakes and accelerates with a single pedal.

Assistant Professor of Industrial and Manufacturing Systems Engineering Areen Alsaid says these driver assist features have the ability to reduce driver workload and make driving safer. But as an expert in human-technology interactions, she’s also been wondering if the learning curve associated with this growing list of advanced features could limit their benefits. “For example, I have a vehicle that has both lane keep assist and adaptive cruise control, and I was under the impression that you couldn’t have them both engaged at the same time, but I tried it and I totally could,” she explains. “That sort of made it like an AV, because the car was controlling both its speed and lateral orientation within the lanes. I definitely did not expect that.” She says sort of like an AV, of course, because, in her vehicle, she discovered these features only remain engaged as long as she has her hands on the steering wheel. But for Alsaid, the experience underscores an important caveat about new driver assist features: When drivers are purchasing a vehicle equipped with several of these advanced technologies, they don’t automatically understand how they work, what their limitations are, and if features will interact with each other in ways they find intuitive. It’s only once drivers become more familiar with how these technologies function that they can fully reap their benefits.

Doctoral student Duha Alkurdi is helping Alsaid with a new project that explores how drivers respond to using multiple driver assist features simultaneously. Photo by Julianne Lindsey

Alsaid says there is a growing body of research on individual driver assist technologies, but researchers are just scratching the surface on understanding the various scenarios when multiple features are operating simultaneously. As part of a new project funded by Toyota, Alsaid is hoping to build some foundational knowledge in this space by studying how drivers behave as they use multiple longitudinal features — technologies that, as contrasted with lateral features like lane keep assist, control the forward momentum of the vehicle. These include things like adaptive cruise control, one pedal driving, forward collision warning, autonomous emergency braking and hands-free driving at low speeds. In the first stage of her study, she’ll observe 10 participants as they engage multiple driver assist features in real vehicles on real roads, monitoring their behavior with cameras and real-time questions, as well as biometrics, like eye tracking and heart-rate monitoring. Using some of the initial insights from this experiment, she and her student research assistants will then design a full driving simulator experience, where dozens more drivers will be confronted with challenging driving situations.

Alsaid says after this initial study, the team will review the results and try to identify areas where the interactions between features and the user interfaces can be made more intuitive for drivers. Her hope is that their work in this area can help drivers adapt more smoothly to these new features so they can enjoy the full potential of their smarter new cars.

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Story by Lou Blouin

Off

Mon, 01/27/2025 - 13:14

Assistant Professor Areen Alsaid says the growing suite of semi-autonomous technologies have the ability to make driving safer, more convenient and less stressful. But we’ll only fully realize these benefits if the learning curve doesn’t confuse drivers.

Assistant Professor Areen Alsaid (right), pictured here with doctoral student Shiva Rasouli, has been using a driving simulator to study how drivers respond to interactions between driver assist features. Photo by Julianne Lindsey

Planning for EV infrastructure is complicated. Here’s a better way to do it.

lblouin — Wed, 22 Jan 2025 13:45:53 +0000

Planning for EV infrastructure is complicated. Here’s a better way to do it. lblouin Wed, 01/22/2025 - 08:45

Electric vehicles still represent less than 1% of registered vehicles in the U.S. and comprised just over 8% of new U.S. vehicle sales in 2024. But government officials across the country are already planning for a future in which EVs are the go-to mode of private transportation — as evidenced by increasingly large public investments in EV charging infrastructure, like the $7.5 billion pledged under the Biden administration. When it comes to actually making a plan for building out that infrastructure, however, the devil is in a complex web of largely unknown details, says Associate Professor of Industrial and Manufacturing Systems Engineering Jian Hu. Ideally, Hu says, you’d want to build a charging system that serves people’s needs for the next 10 years. The tricky part is that involves correctly anticipating a host of trendlines which are basically impossible to predict. “There are so many uncertainties,” Hu says. “We don’t know how fast the EV market will grow, which means we don’t know what the charging demand will be. Will the public want fast or slow charging? Will people charge at home or at work? During peak hours, or non-peak hours? Will utilities adopt dynamic pricing so it’s cheaper to charge at certain times of day? And how will this affect charging behavior? And what if there are new technologies that emerge? There are so many things we don’t know.”

Typically, when government agencies are planning for things like this, Hu says they might use what statisticians call a deterministic model. When you’re making this kind of model, you take account of what data you have, plug it into a formula that defines the relationship between the variables, and then you typically get a single answer, which then guides your plan of action. A simple retirement calculator, which takes your current age, retirement age, anticipated rate of return, the rate of inflation, and monthly savings target and tells you how much money you’ll have when you retire is a simple kind of deterministic model. But Hu argues that these kinds of prediction models aren’t the best tools when you’re dealing with complex situations with lots of uncertainty. “What if your rate of return on your investments isn’t the 7% you’ve put into that calculator? What if it is less and then you don’t have the money you expect when you retire? If you only use this kind of model, you’ll have this single prediction and you base everything on that. But you’re not preparing for a possible worst-case scenario.”

Because of this, statisticians often use different tools for forecasting complex, highly uncertain phenomena, like investment performance. One of the most popular is called stochastic modeling, which, instead of giving you just one answer, provides a range of results represented by thousands of different simulated scenarios in which the values and probabilities of the underlying variables are allowed to change. A popular type of stochastic model is a Monte Carlo simulation, which is used in financial planning. Unlike a simple retirement calculator, which assumes fixed values for things like the rate of inflation or a rate of return, a Monte Carlo analysis will run thousands of simulations in which all the variables you have in your model have different values and assigned probabilities. As a result, you get a much wider representation of possible outcomes, including best-case, worst-case and most likely scenarios. A Monte Carlo simulation might tell you, say, that in 98 percent of its simulated scenarios, which account for a wide range of possible economic conditions, your savings plans are going to get you to the end of your life with money in the bank. That might give you much more confidence in your investment strategy. You might even decide to take a more aggressive approach. Or, as Hu frames it, stochastic modeling gives us a much more complete way to understand risk.

Because stochastic models are so much better at helping us understand risk of uncertain events, Hu says they could be an excellent approach for planning EV infrastructure, given that reliable data on important details, like consumer charging preferences and future electricity demand, are sparse or lacking altogether. As part of a National Science Foundation-funded project, and in collaboration with Argonne National Laboratory, which is itself partnering with the Chicago Transit Authority, Hu will be creating novel approaches to stochastic modeling that he hopes can profoundly improve this planning process. To get just a little bit technical for a moment: Hu says one of the big challenges of stochastic modeling is coping with “distributional ambiguity,” which is where you’re lacking a lot of reliable information about the probability of key phenomena. Currently, one of the ways statisticians deal with this is a method called distributionally robust optimization, but Hu says DRO has a few limitations. One, it’s computationally intensive, which means as models get more complex, it becomes increasingly cumbersome and time consuming to run them and adapt them to new data. Second, DRO tends to hedge against worst-case scenarios and can therefore lead to overly conservative outcomes. Third, it's a machine learning-based process that is a “black box,” meaning it does not show how it arrives at its answers. It therefore might not be a great option for publicly funded agencies, who, in spending large sums of taxpayer dollars, could certainly benefit from transparent or interpretable models. Hu says his novel stochastic modeling strategy aims not only to improve on these limitations, but also provide more overall usability for public agencies, including models that can be more easily updated as better data emerge.

After developing the model, Hu is looking forward to a big field test toward the end of the project: helping the Chicago Transit Authority as it embarks on electrifying their massive fleet of public buses. Current electric bus battery technology generally provides a range of around 60 miles. But many of CTA’s current scheduled vehicle route blocks are much longer; nearly a third of weekday blocks are longer than 100 miles. CTA therefore envisions building out a bus-charging infrastructure that’s integrated with their garages and bus routes, which will also require enhancements to the local electric grid to accommodate the new electricity load. Figuring out where these grid modifications need to be made and the ideal placement for the charging stations in a way that harmonizes with the complex bus routes — which are likely to change in the future — is exactly the kind of complicated, ambiguous planning task Hu says his model can help with. Most importantly, it gives a government agency its best chance of spending limited public resources for EV charging as effectively as possible.

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Story by Lou Blouin

Off

Wed, 01/22/2025 - 13:40

Given a web of uncertainties, like future electricity price, consumer charging behavior and EV market growth, Associate Professor Jian Hu says a novel type of stochastic modeling can minimize the risks of public investments in EV infrastructure.

Associate Professor of Industrial and Manufacturing Systems Engineering Jian Hu is testing a new approach to planning charging infrastructure with the Chicago Transit Authority. Photo by Annie Barker

The challenges with younger drivers and autonomous vehicles

lblouin — Mon, 13 Jan 2025 15:28:25 +0000

The challenges with younger drivers and autonomous vehicles lblouin Mon, 01/13/2025 - 10:28

Autonomous vehicles promise a future of almost unimaginable driver convenience. After all, who wouldn't want to be able to watch a movie during a tedious stop-and-go rush hour commute? But proponents of AVs think the technology’s even more meaningful impact will be in the realm of safety, given that more than 90% of traffic accidents are thought to be the result of human error. Indeed, we’re already seeing some of the safety benefits as semi-autonomous driver assist features, like lane keep assist, blind spot monitoring and emergency braking, become commonplace on newer vehicles. Even so, the next big developmental leap to so-called Level 3 vehicles, which are capable of doing all the driving for us in certain situations and are now starting to enter American and European markets, could introduce a whole new set of safety and driver behavior challenges, says Associate Professor of Industrial and Manufacturing Systems Engineering Bochen Jia. That’s because Level 3 AVs still require full driver intervention when conditions are beyond the system’s capability — like when lane markers aren’t clearly marked or are obscured by weather; or in the event of system malfunction. That introduces a whole new type of driver experience, which the AV research community calls “takeover.” That’s the tricky liminal moment when a driver has to abruptly shift their focus from, say, watching their favorite Netflix series, to dealing with a potential road emergency.

Intuitively, takeover is a fraught, psychologically complex space, which is why Jia says a lot of research has been devoted to studying how drivers respond to this type of demand. For example, early research has indicated, somewhat counterintuitively, that the risk of driver fatigue actually increases when operating a Level 3 vehicle. It seems the mental task of constantly monitoring a vehicle doing the driving for us can actually be more draining than doing the driving ourselves. And in Jia’s lab, he’s been focusing on a neglected area of takeover research: younger, inexperienced drivers. From a research perspective, he says it’s a fascinating population. First, young people’s brains are still developing, particularly when it comes to attention resources and situational awareness, which can influence things like fatigue development and how well they respond to potentially hazardous situations, like takeover. In addition, younger people tend to, in general, be more trusting of technology, which could lead to less intense monitoring of the vehicle and undesirable takeover outcomes. Moreover, Jia says we might not be too far away from an era where young drivers’ only driving experience is on Level 3 vehicles. “That would mean they actually aren’t getting much experience doing actual driving, which could influence how well they respond to situations when the vehicle asks them to take over,” Jia says.

In focusing on younger drivers, Associate Professor of Industrial and Manufacturing Systems Engineering Bochen Jia has been exploring a neglected area of AV research. Photo by Annie Barker

For one of his latest projects, Jia has been digging into the particular issue of fatigue onset in younger, novice drivers. For this study, he recruited both drivers under the age of 19 who had limited driving experience and middle-aged drivers who had years of experience. Using a high-fidelity driving simulator, both sets of participants engaged in extended periods of autonomous driving and were instructed to take control of the vehicle when the autonomous mode was not safe or had failed. During their driving experience, Jia then introduced several types of takeover events to see how drivers would react. By combining physiological measures and self-reported assessments collected periodically during their driving task, Jia then monitored each set of participants for fatigue onset. The results were pretty interesting: Novice young adult drivers developed both physical and mental fatigue earlier than their middle-aged counterparts during extended periods of autonomous driving. "This is somewhat surprising to people because they think young people naturally have more energy," Jia explains. "However, it appears that the mental demands of autonomous driving, or the tendency of younger drivers to be more engaged in non-driving tasks during autonomous operation, may accelerate the onset of fatigue in younger drivers." Moreover, the takeover outcomes for fatigued drivers were often far from desirable. “Essentially, we were looking at how often participants would fail to prevent a collision,” Jia says. “And it happened more frequently than we had hoped.”

Jia says the results point to a need for highly effective fatigue warning systems for Level 3 AVs that have special considerations for younger, inexperienced drivers. Interestingly, Jia envisions this system would be most effective if it wasn’t just part of the vehicle, but integrated with other readily available wellness monitoring technologies, like wearable fitness trackers that monitor heart rates and sleep quality. “I don't think you can have a system that’s confined to the car — that’s not enough,” Jia says. “It should be part of your life monitoring — an overall system — because they don’t suddenly become a different person when they get in the car. With all the personal data monitoring technologies we have nowadays, we could easily identify younger drivers, and we likely have a lot of the data we would need.”

Next up, Jia says he plans to continue his research on fatigue onset with a study that puts participants in real automated vehicles within a closed testbed, such as the Mcity test facility. In addition, he has a similar project that is studying differences in distraction levels between younger, novice drivers and middle-aged experienced drivers. He hopes both studies will provide critical insights into improving the safety and usability of autonomous vehicle systems for drivers of all experience levels.

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Story by Lou Blouin. Want to learn more about Jia's work on driver fatigue? You can read the full-text of his recent article on the subject in the “Journal of Safety Research,” which was co-authored by Jia's student Yourui Tong, ��-Dearborn Associate Professor of Industrial and Manufacturing Systems Engineering Shan Bao, Tsinghua University Professor of Industrial Engineering Changxu Wu and ��-Dearborn Associate Professor of Psychology Nitya Sethuraman.

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Mon, 01/13/2025 - 15:22

Associate Professor Bochen Jia’s latest research is revealing some surprising things about how fatigue can impact younger, inexperienced drivers when they're operating semi-autonomous vehicles.

Associate Professor of Industrial and Manufacturing Systems Engineering Bochen Jia (right) and graduate student Darlington Egeonu check out the driving simulator Jia used for his most recent project on driver fatigue. Photo by Annie Barker

‘Healing’ batteries with ultrasonics

lblouin — Wed, 08 Jan 2025 17:59:20 +0000

‘Healing’ batteries with ultrasonics lblouin Wed, 01/08/2025 - 12:59

High-capacity batteries have emerged as an essential building block of the clean energy future, but it likely won’t be today’s lithium-ion batteries alone that get us there. That’s because, as powerful and ubiquitous as they are, lithium-ion batteries have some major limitations. For starters, they are very heavy — a fundamental quality that curbs the range of today’s electric vehicles and basically rules them out as an option for powering commercial aircraft. The world’s growing demand for the minerals needed to manufacture lithium-ion batteries also requires ever expanding mining operations, which have environmental, climate and geopolitical consequences. And on the safety front, the liquid electrolytes used in lithium-ion batteries are highly flammable, which is one reason why electric vehicle battery fires can be so catastrophic.

Because of these issues, researchers are developing new kinds of batteries, including solid-state batteries, which are lighter, can hold more energy for their weight and require fewer materials. They are also considered safer, since, as the name suggests, they don’t require a liquid electrolyte. But mechanical engineering postdoc research fellow Yaohong Xiao, who works with Associate Professor Lei Chen, says that last quality is both a blessing and a curse. Inside a typical lithium-ion battery, the liquid electrolyte is responsible for transferring charged particles back and forth between the cathode and anode sides of the battery, which is necessary both for charging and discharging. Because this electrolyte is a liquid, Xiao says it creates a nearly uniform interface between the two sides of the battery, since liquids naturally fill tiny nooks and crannies that might exist between the anode and cathode materials. In a solid-state battery, however, the electrolyte separating the anode and cathode sides is a strong, stiff, solid piece of material, and Xiao says that makes it more difficult to get really good contact between the two sides. “Essentially, you have solid on solid on solid,” Xiao explains. “Right after manufacturing, you might have no problem with this interface, but after a few cycles, or after the battery has experienced vibrations from being on the road, you start to get little voids in the interface so the electrolyte is no longer making perfect contact with both sides of the battery.” As a result, Xiao says a battery can develop zones of higher than usual current, which reduces its overall effectiveness.

Typically, Xiao says researchers have tried to solve this solid-state battery interface problem in a couple of different ways. First, they use pressure to basically squish all the components back together. Or they can use an “interlayer” material, which, kind of like double-sided tape, keeps the anode and cathode materials snug up against the solid electrolyte. But each approach has its limitations. Xiao says introducing interlayers to a process is typically expensive. That might not be a huge deal for experiments in a lab, but it would increase the already high costs of solid-state batteries when they’re being manufactured at scale. On the other hand, Xiao says using pressure to smooth out the interface is sort of like trying to squish two pieces of wood together. The rigidity of the lumber and any imperfections along the surfaces mean that pressure alone often isn’t enough to create perfect contact.

A couple years ago, Xiao began kicking around a sort of unconventional solution for this interface challenge. Xiao’s background is actually in metallurgy, not battery chemistry, and he saw this issue primarily as a question of how to get different metals to stick together. As it turns out, this is actually a pretty common problem in the metals universe, and one with a variety of well-established solutions. Specifically, Xiao was thinking that a technique called ultrasonic welding might be able to “heal” the voids in the interface, restoring uniform contact with the electrolyte. Ultrasonic healing, which is commonly deployed in a wide range of industries to weld pieces of plastic or thin pieces of metal together, uses high-frequency acoustic vibrations instead of high temperatures or solders to join materials together. The result is a highly uniform bond, but the process doesn’t use a whole lot of energy and can be done very quickly. Xiao worked with his postdoc colleague in Chen’s lab, XinXin Yao, who helped provide the theoretical feasibility for the welding concept, to set up an experiment, which revealed that ultrasonic welding could indeed nearly completely restore the uniformity of the electrolyte interface in as little as a minute. Moreover, the process required temperatures barely warmer than the water in your hot water heater and used about as much energy as an old-fashioned incandescent light bulb.

Xiao says the effectiveness of this metals-based approach came as a surprise to many of his colleagues, most of whom are experts in battery chemistry. Now, he’s hoping the results, which were recently published in “Advanced Energy Materials,” will help inspire other researchers to further explore the technique’s full potential. He says one possible application would be to use this as a tune-up strategy for eventual solid-state battery-powered EVs, which could come in for a quick ultrasonic healing treatment and get their optimal range back. And one other surprise finding from his experiment is that ultrasonic welding actually increased the conductivity of the electrolyte, albeit temporarily. That result was particularly intriguing to some colleagues at ��-Ann Arbor, who recently visited the newly established Battery Manufacturing and Testing Lab for a firsthand look. “They were very interested in this point,” Xiao says. “The conductivity would slowly recover after treatment, but they were thinking, what if we can fix that? What if we can figure out a way to not let it recover? So it’s very exciting to see this research already inspiring further work in this area.”

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Story by Lou Blouin

Mechanical Engineering

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Wed, 01/08/2025 - 17:58

Two mechanical engineering postdoc research fellows have discovered a novel way to solve a pesky challenge with next-generation solid-state batteries.

In Associate Professor of Mechanical Engineering Lei Chen's lab, postdoc research fellow Yaohong Xiao has been working on developing solid-state batteries, a safer, more energy dense alternative to today's lithium-ion batteries. Photo by Annie Barker

Class of Fall 2024: CECS graduate Dania Ammar

lblouin — Mon, 16 Dec 2024 16:46:13 +0000

Class of Fall 2024: CECS graduate Dania Ammar lblouin Mon, 12/16/2024 - 11:46

When Dania Ammar moved with her family from Beirut to southeast Michigan in 2017, her plan was to build on her civil engineering background and start the master’s in construction management program at ��-Ann Arbor. But some family circumstances led to her deferring her enrollment. As it happened, during the unexpected semester off, she reconnected with a friend from Lebanon who was studying mechanical engineering on the Dearborn campus. He had a lot of good things to say about the college, particularly how the research enterprise was growing quickly, giving graduate students a lot of opportunities. It was enough to pique Ammar’s interest. She got on the ��-Dearborn website, checked out the programs and research labs, particularly those in Industrial and Manufacturing Systems Engineering, and set up a meeting with then-College of Engineering and Computer Science Associate Dean for Undergraduate Education and current CECS Dean Ghassan Kridli. “Dean Kridli was very generous in giving me some time to talk with him about the programs and all the research that was going on at ��-Dearborn at that time. I decided this was definitely one of the best options,” Ammar says.

The following semester, Ammar decided to go for it — enrolling in the IMSE master’s program at ��-Dearborn in 2018. IMSE is a broad discipline, and Ammar recalls feeling very open to exploring all the possibilities. That first semester, she took a variety of classes and found an early champion in Associate Professor Jian Hu, who specializes in using data analytics and machine learning in risk management applications. “I had a very good math background and knew some programming, but I had no background in that field,” Ammar says. “Professor Hu provided me with all kinds of support, told me which books I should start reading, and gave me time to get accustomed to programming models that we were using. I learned so much and it was a phenomenal experience.” A little later in her master’s program, she crossed paths with Professor Shan Bao, a specialist in human factors who was helping develop a new master’s program in Human-Centered Design and Engineering, a field that roots engineering solutions in deep understandings of human needs. Ammar says a lot of people think of human factors as a psychology-based discipline — and it does draw on psychology and user feedback quite a bit. But with Bao’s work, Ammar saw how the discipline’s deepest potential lay in combining those insights with complex data analytics and even artificial intelligence. “You can’t really develop any product, whether it is something simple or something very complicated, like an automated vehicle, without going into the human factors aspects of it,” she says. “But I saw this isn’t just about psychology. It’s about figuring out ways to quantify the psychology — to measure human preferences and behaviors so you can build models that help you gain insights into those measures.”

Ammar loved working with Bao and chose to continue her journey at Dearborn with a PhD. For her dissertation research, she took on an ambitious multiphase project in transportation safety that explored one of the more fascinating emerging challenges related to autonomous vehicles: How will pedestrians learn to trust and safely interact with vehicles when humans aren’t doing the driving? Ammar says both pedestrians and drivers typically rely on an exchange of visual signals, like eye contact and waving, to negotiate who has the right of way, and absent a human driver, cars will presumably need some way of declaring their intentions. She began her work with a deep dive into road crash data, identifying the factors that lead to the most dangerous situations between pedestrians and human-driven vehicles in order to derive potentially problematic pedestrian-AV scenarios. She then embarked on building a cueing system designed to satisfy pedestrians' needs while interacting with AVs in these evaluation scenarios. That phase of her research led to several interesting conclusions. For example, Ammar discovered that pedestrians tend to favor technologies that closely mimic their interactions with human drivers, e.g. AVs that communicate a symbolic message, like the familiar flashing silhouette used at crosswalks, either directly on the vehicle or via crosswalk infrastructure (or both). She also investigated many fine-grain details, like whether a pedestrian’s age, gender, and driving and pedestrian behavior (conservative versus nonconservative) influenced their preferences for AV cueing. The final stage of her research involved creating a VR simulation, where she observed participants reacting in tricky situations, like an autonomous vehicle quickly approaching a red light before eventually stopping. By measuring how long pedestrians took to leave the curb and using physiological measures like pupil diameter, she could see which safety cues inspired trust or distrust. She’s hoping the results, which have led to several publications, will help other researchers, automakers and civil engineers as they design cueing systems for the era of autonomous vehicles.

Since officially finishing her PhD program, Ammar has switched gears a little bit, and in some ways, it’s a moment when her diverse studies are all coming together. Since August, she’s been working as a data science project engineer with Hottinger Brüel & Kjær, where she focuses on data analytics and machine learning-based solutions for a wide variety of industries. “These skills can be leveraged in any application, including civil engineering-related fields, which have changed a lot even in the short time since I was an undergraduate student,” Ammar says. “Let’s say you had a project and you needed insights about soil mechanics or wanted to predict settlements in the soil over time. In the past, we would have done that with a bunch of physics-based and numerical equations. But now, with more sensors being installed and collected data becoming available, researchers are developing machine learning-based models to answer those questions. That’s where the field is going now, and in the future, most engineering solutions will be built with data collection in mind so these models can keep getting better.”

Ammar now just has one finishing touch to put on her ��-Dearborn career: Walking across the commencement stage in December. She expects it’s going to be a pretty emotional moment. “��-Dearborn has been my home for the last seven years,” she says. “It’s only been a few months since I left, but to see everyone again is going to be very special. I’ve had the opportunity to work with so many different people, and I always felt, sincerely, that every day I spent with them was a chance to learn something new.”

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Story by Lou Blouin

Commencement

Student Success

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Mon, 12/16/2024 - 16:44

The recent PhD graduate almost pursued a career in construction management until ��-Dearborn helped her unlock a future in data analytics and artificial intelligence.

Since finishing her PhD, Dania Ammar has been working as a data science project engineer. She'll be putting the punctuation mark on her seven-year ��-Dearborn career on Saturday, when she walks across the commencement stage. Photo by Annie Barker

How organ-on-a-chip technology is changing research of human diseases

lblouin — Mon, 04 Nov 2024 17:25:26 +0000

How organ-on-a-chip technology is changing research of human diseases lblouin Mon, 11/04/2024 - 12:25

Those who study human disease, and those of us who benefit from treatments developed by their research, owe a great debt to mice. The tiny animals get many of the same diseases as us and have very similar genetics and biological processes, making them a useful stand-in for understanding what goes wrong in our bodies and how we might respond to novel therapies. But as a surrogate for humans, mice aren’t perfect, says Assistant Professor of Mechanical Engineering Aditya Raghunandan, who started at ��-Dearborn in 2023. All you have to do, he says, is look at the thousands of human clinical trials for new treatments that showed promise in mice but failed to deliver similar results in humans. Experimenting on human cells in the lab offers a promising alternative, because researchers can theoretically study diseases more directly. But, like mice, this technique has limitations: Raghunandan says how cells behave, isolated from their neighbors and living in an artificial environment in the lab, isn’t necessarily how they behave in our bodies.

In his early days as a researcher, Raghunandan often speculated that there had to be a better way, and as luck would have it, his career intersected an era of bioengineering in which some transformative new methods were emerging. In the early 2000s, researchers from the Wyss Institute at Harvard University developed a novel technology they dubbed “organ-on-a-chip.” Like traditional cell culturing, the idea was to create an experimental environment for human cells in which researchers could subject them to all kinds of things — genetic engineering, toxins, new drugs, mechanical and chemical signals — and then see how they behave. But their technique promised several advantages. Traditional cell cultures typically contain just one or two cell types, living in some kind of media, but that environment isn’t particularly representative of how things work in our bodies. For one, Raghunandan says our cells don’t just float around statically in goo; they're constantly being subjected to things like fluid flow and mechanical forces, which greatly influence their health and regulate their function. Moreover, a key part of cell function is interacting with other cell types. If you’re just observing how one cell type reacts to something in a Petri dish, he says you’re only getting a small slice of the picture.

Organ-on-a-chip technology, which has been tweaked and improved in the decades since its invention, directly addresses these shortcomings. Raghunandan says you can sort of think of the difference between an organ-on-a-chip and a traditional static cell culture like the difference between a house and a studio apartment. In a traditional cell culture, there’s just one room and everything is just kind of thrown in that room. But with an organ-on-a-chip, you can put up to three or four different cell types in different rooms within the house. Just as walls divide the rooms of the house, membranes keep the cells where you want them, and by tweaking the porosity of the membranes, you can also facilitate different kinds of interactions between cell types. Most importantly, tiny microfluidic channels, smaller than the width of a human hair, function like hallways, connecting the rooms, allowing researchers to pump in fluids to mechanically and chemically stimulate the cells in very precise ways.

Organs-on-a-chip are small devices that can do big things. In Raghunandan's lab, he uses them to study the dynamics of fluid flow in the brain that contribute to Alzheimer's disease.

The setup much more closely resembles how things work in our bodies, Raghunandan says. For example, in his own lab, where he studies how fluid flow in the brain impacts protein aggregation, one of the factors linked to neural diseases like Alzheimer’s, he builds layered organs-on-a-chip that mimic the way that neural and blood vessel tissues are organized in our brains. “If we go from the bloodstream into your brain, the first barrier are endothelial cells. And then the next layer of cells are smooth muscle cells, then you have an empty compartment where you have fluid, and then you have astrocytes,” he explains. “So the brain is layered, and we can reproduce these compartments and membranes and fluid flow where everything can interact with each other.” You can breed a mouse to have a predisposition to develop a certain disease. But you can’t manipulate fluid flow in its brain in real time, he says. “I can do that just by turning a knob on a pump.”

Being able to manipulate fluid flow is extremely important for Raghunandan’s current research. During his recent postdoc at the University of Rochester, he worked with the teams of Mechanical Engineering Professor Douglas Kelley and Neuroscience Professor Maiken Nedergaard, where they discovered that the brain actually has a separate “plumbing system” that bypasses the blood-brain barrier and flushes away waste while we sleep — almost like an alternative lymph system that only exists within the brain. Raghunandan says about half of the waste is physically flushed away, the way your plumbing removes waste from your house. But the other half requires specialized enzymes that are secreted by specific brain cells, like smooth muscle cells, which chop up or digest waste proteins. What’s surprising, Raghunandan says, is that abnormal fluid flow can actually change these cells' behavior in ways that make them less effective. That is, fluid flow in the brain isn’t just a plumbing system. It’s a dynamic that, in itself, can directly change cells and how they function.

Raghunadan says the research teams made these initial discoveries using mice models. But to investigate the details of how abnormal brain fluid flow was impacting Alzheimer's patients — and potentially develop therapeutics — he knew he’d need a different platform. This led to a fruitful collaboration with University of Rochester Biomedical Engineering Professor James McGrath, who had developed a new organ-on-a-chip technology to study inflammation in the brain. Now, Raghunadan is adapting that technology in his own lab to expose brain cells to varying types of fluid flow and precisely measure the effects. That’s something he could never do in a static cell culture or with mice. Raghunandan and McGrath have also created their own custom organ-on-a-chip devices that are much faster to build. “With the original design, it took a long time to build them — maybe a couple days to build 10 devices, and not all of them were going to be successful,” Raghunandan says. “We’ve streamlined the design, so now you can put together the parts like Legos and it takes three minutes.” McGrath even started a company so other researchers can use the snap-together version in their work.

Raghunandan, who’s one of only two researchers that he’s aware of at ��-Dearborn currently using this technique, sees some big practical benefits if this technology is more widely adopted. First, he says you could test new drugs on human cells in an organ-on-a-chip device to get some preliminary indication of their efficacy before moving them to full-blown human clinical trials. Second, we could use organs-on-a-chip to do patient-specific modeling for drugs. “If you had a certain disease, we could biopsy your cells, build a ‘you-on-a-chip’ and then test a drug to see if it had the potential to be a useful therapy for you,” he says.

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Story by Lou Blouin. Photos by Annie Barker.

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