Science of Scale-Up

A scientific breakthrough is only the beginning.

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  Inside the corporate research facility of one of the biggest petroleum companies in the world, Stanley Whittingham and his colleagues had just discovered a new way to store energy. They found that moving lithium ions back and forth through a material could store energy, release it, and then store it again. Within weeks, in a beaker on a laboratory bench, Whittingham and his team had built a working rechargeable lithium battery. They soon found themselves pitching the technology to leadership at Exxon headquarters in New York. The reaction was immediate.

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  This looks good.

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  They approved further development, and a few short years later, they were displaying rechargeable lithium batteries at the 1977 International Electric Vehicle Exposition in Chicago. The discovery became something enormous.

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  We're going from a car with a massive internal combustion engine to a car with an engine that might just be the answer to all our fuel problems.

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  Just not yet. The 80s aren't remembered for the transition to electric vehicles. At real-world scale, the questions change. Can you manufacture millions of them?

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  Manufacturability and the cost of manufacturing.

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  Will they last?

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  This is a bottleneck, right? So how do you prove something can operate for five years without taking five years?

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  Are they affordable enough to build into products that consumers will actually buy?

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  Does your technology do what it needs to do for the customer you're trying to serve?

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  Stanley Whittingham's work would eventually be recognized with a Nobel Prize in Chemistry. Yet even discoveries this consequential can face a long, difficult road to real-world impact. Looking back, Whittingham would say...

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  They're really too early. The timing was just not right.

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  And historically, many transformative technologies have followed that same path, spending years, sometimes decades, figuring out what they need to become before the real world can use them. But what if those questions didn't have to come later?

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  If you're going to be developing this technology on a benchtop, how do you have an eye for where it's actually going to be used and be able to design it or to do the technology development so that it actually is usable?

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  At Lawrence Livermore National Laboratory, the science of scale up is asking how scientists can identify the problems that emerge in new technologies at scale earlier and shorten the path between breakthrough and impact.

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  Those are the kinds of things we think about, things that have the opportunity to impact thousands, tens of thousands, millions, potentially even billions of lives.

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  Welcome to the Big Ideas Lab, your exploration inside Lawrence Livermore National Laboratory. Hear untold stories, meet boundary-pushing pioneers, and get unparalleled access inside the gates. From national security challenges to computing revolutions, discover the innovations that are shaping tomorrow today. Rechargeable lithium batteries trace back to Whittingham's work in 1972, but lithium

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  ion wouldn't reach consumer products until 1991. Solar photovoltaic cells existed in the 1950s, long before solar panels appeared on rooftops. And the MRI was demonstrated in the 1970s, years before the machines became hospital staples. In each case, the core scientific principles had been proven. The difficult part was scaling them into technologies people could reliably use. The average disruptive technology takes roughly 35 years to cross that divide. In the time a breakthrough remains stuck between proven and available, someone else can reach the market first. Supply chains can form around foreign manufacturing.

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  Costs remain high, and the public misses out on the technology's benefits. Livermore scientists are trying to compress that timeline. Andrew Wong is a material scientist at Lawrence Livermore. He started at the lab after working on batteries for grid-scale energy storage.

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  We do a lot of early-stage research where we end up publishing or presenting it to the scientific community, but if we don't think about the application, then it just stops there. We'd like to get better at actually translating that and being active about that pipeline so that the things we work on today aren't impacting your life in 50 years, maybe they're impacting your life in five years.

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  In 2022, Lawrence Livermore launched a multi-year effort called the Science of Scale Up, funded as a strategic initiative for the lab. Its goal was to understand why promising technologies stall and what scientists could do earlier to keep them moving.

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  Oftentimes, we think about, oh, I need to be building a bigger battery. Well, it turns out, save for your phone, you don't actually want a bigger battery. You might not even want a more powerful battery, but you do want something that's going to last longer. You don't want to charge it every couple hours, you want it to last for most of a day. But let's say that you have a car that has a battery. You might care a lot more about being able to put your foot down on the pedal and get that energy out of it. Those kinds of things are different dimensions of scale up, of how do you actually make it work for a user in its application? Those are the kinds of things we think about.

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  Scale can mean producing more, lasting longer, delivering power faster, reducing costs, or changing how something is manufactured. A technology might already be large enough, powerful enough, or efficient enough in the lab. The real challenge may be producing thousands of parts instead of one, finding materials that are easier to source, or developing a manufacturing process that can move quickly. And sometimes, the most important dimension is not physical at all.

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  Let's say I build a battery that solves all the world problems. Let's say it works in your phone, it works in your car, you can put it with your house, it does everything, but costs a billion dollars. It's not going to work in its intended application because no one's going to be able to access it.

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  A perfect technology no one can afford isn't a technology. It's a museum piece.

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  We do a lot of science, and we can do a lot of research, understanding what those bounds are, and that's really important to constrain the focus of the work that we do. That allows us to be able to hit a target, and if we can get within that target, that allows us to be able to say we've successfully scaled up this technology.

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  But when a team works to scale the wrong dimension, the damage compounds quickly. Sarah Baker is a group and program leader for Carbon Technologies at Lawrence Livermore.

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  When people scale too fast, and there's a lot of reasons for that, they scale before they have all the information they need. What happens is they build a giant black box, and it doesn't work the way it worked at small scale. And oftentimes, you have no idea why. And so what do you do then? You've spent enormous amounts of time and money, and now maybe your investors have lost trust in the technology when it's not necessarily the technology.

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  The science of scale up shifts from asking, how big can we make it, to what will this actually have to do to survive in the real world? And can we figure that out now before committing to a large-scale system? Sarah has seen the gap between science and application firsthand.

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  We discovered this new material that could serve to improve carbon capture.

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  Carbon capture works like a filter. Industrial exhaust contains a mixture of gases, and the capture material is designed to separate out carbon dioxide. But instead of trapping particles by size, it selectively absorbs the CO2 while the other gases pass through.

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  It happened to involve making single microcapsules, one at a time, using microfluidic devices. So if you can imagine, like, little fish eggs on your sushi, we found we could stuff some selective absorbent in those little fish egg-shaped things that had a polymer shell that let the gas come in and selectively trap the gas that we wanted.

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  The early results were promising, and as the experiment continued, Sarah and her team received more funding to move the project forward.

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  So naturally, we thought, okay, we just have to make more of these little fish eggs, one at a time. We just have to make them faster. And at the end of that project, we realized the entire time we had been working on the wrong thing.

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  That actually, in the use case for those microcapsules, they had to function in a way where if they're actually going to be used, the way we were making them, they would just collapse and squirt out all their juices everywhere and be ruined within a few hours. We could have probably made so much more progress had we been working on the right material three years ago. So that was the inspiration for me and those of us working on science of scale up at the lab to say, hold on, we need to think about this differently.

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  The team had spent years improving how the material was manufactured before fully understanding what the material needed to endure.

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  All through my research before I came to the lab, it was always about tinkering with these small things, never really asking how it needed to operate in the real world, and never really thinking about what it would take to scale up a technology.

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  The science of scale up reverses that approach.

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  There's this phrase that people say, instead of scaling up, we should be scaling down. So start with the end in mind. That is a mindset shift that I think we are working to instill at Lawrence Livermore.

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  Picture the finished technology doing its job in the real world. What must it endure? How long must it last? What could stop it from succeeding? Then work backward, while the technology is still small enough to study and change.

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  If I tell you to go from point A to point B, but I don't give you a map, you're going to spend a lot of time just kind of wandering around. But if you actually know where your endpoint is, then you can actually chart a critical path for how do you move a technology from where you're starting today to where it eventually needs to end up.

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  Scientists cannot define the endpoint alone. Industry understands the cost, performance, manufacturing, and reliability a technology must meet before anyone can actually use it. In one collaboration, that outside perspective introduced Livermore researchers to a tool Sarah had never used before, one that could save scientists years of work in the wrong direction.

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  The lab was working with a large multinational corporation to build advanced computer models of a new technology.

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  They wanted us to develop the most advanced models of this disruptive type of technology. And in that process of working with them, they dropped this idea of a risk assessment. And of course, I had no idea what that was.

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  The idea was simple. Look ahead before committing to the next scale.

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  A risk assessment, envisioning what we call the minimum viable product. What does that sort of viable product need to look like? And what are the risks to getting there?

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  The framework even went beyond risk alone.

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  And they said, well, we're always thinking about TRL, Technology Readiness Level. This is a three-legged stool. There's technology readiness level. There is the performance you need to have. Okay, we hadn't been thinking about that. And there's risk, the risk to getting there. And so this company said, we have this whole approach to assessing scale up risk. Well, if we would have done that on that microcapsule story that I told you about, the fish eggs, we would have found that right away. This company really helped us generate our own risk assessment protocol for Livermore and the National Lab.

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  The industry and lab partnerships did more than help advance one technology. It changed how the lab thought about the path from discovery to deployment. On a separate gas separation project, researchers used the new protocol from the beginning. It gave them a clearer picture of what the technology needed to become, but it could not eliminate every surprise. Changing the manufacturing method still revealed new scientific problems. The technology was a 3D-printed column reactor that would eventually need to operate at the scale of a 10-story building.

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  We are working on a column that you could put on, say, a power plant. It's emitting carbon dioxide, and you want to have some kind of scrubber that actually will remove that. And you can actually turn that eventually into useful material. These actual scrubbers are probably going to be a couple stories tall. Even somebody who's not that familiar with 3D printing could probably imagine if I'm trying to like do that layer by layer, that's a really long time. If you're making really small features, it's hard to make a really big part. If you're doing things that are down to the size of a human hair, it's really hard to print something that's the size of a football field. And that's, in some ways, what would be proposed if you were to try to take this technology that

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  we were just talking about for removing carbon dioxide from power plants, is you're trying to do that really small feature over really large areas.

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  To get around that trade-off, the team stopped trying to print the entire structure as one piece and shifted to a faster manufacturing method, stamping smaller sections and layering them together. That made the structure far more practical to build, but it also introduced small gaps between the layers that changed how the technology performed.

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  They realized that there's actually a gap when they started layering these materials up that made a significant difference in how well this thing performed. And so then they had to go back to the computational realm and figure out, well, what actually happens with that gap? How do we decrease that gap? What do we do to solve that? That work is continued in progress, but I believe that they've gotten up to a multi-meter-tall tower now, and they're working on testing the performance of that.

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  Changing the manufacturing method solved one problem and created another. The team built, tested, returned to the models, and refined the design again. That cycle is the science of scale up in practice. Every change reveals something new about what the technology will need to become. And each cycle brings these technologies closer to leaving the laboratory, where they can support new manufacturing pathways and begin improving lives and processes.

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  One thing that I've seen that's unique about the way we work on this problem here is that we have the same people working on the basic science questions as the scale up. And so they can take that thinking and cycle it back to the basic science questions and back up and back down. And that's very, very rare.

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  Scaling well is rarely one enormous leap from discovery to deployment. Instead, it's a series of better-informed steps, and every solved problem shortens the distance between a promising idea and a technology the public can actually use. In many places, the people who discover a technology, model it, manufacture it, and eventually operate it work in separate organizations. Livermore's advantage is its ability to connect those perspectives earlier, then expand the collaboration across industry and the national laboratory system. Chemists, engineers, computational scientists, manufacturing experts, and industry

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  partners can work together to identify what a technology will need and what could stop it before years are spent moving in the wrong direction.

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  I did learn that we have a whole group at Livermore who works on risk assessments. That way of thinking was here. We just hadn't connected it to what we were doing.

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  Even at an institution as large as Livermore, no single team has every capability needed to move a technology from concept to commercialization. The advantage comes from connecting those capabilities and expanding the collaboration beyond the lab itself.

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  We recently started at Livermore leading this center called SCALE, Solution Center for Commercial Advancement of Large-Scale Electric Chemistry. We're working with two other labs, and the idea is amongst these three labs and probably the whole National Lab system, we have all kinds of scientific and technical capabilities and workforce know-how that if we put it together, we could really help small companies, large companies solve these problems.

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  Successful scale up requires multiple disciplines. It is chemistry, physics, and manufacturing, but also economics, workforce, automation, and policy, all shaping whether a technology can move forward.

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  We are a part of that ecosystem, and I hope that we continue to grow in that role as our technology portfolio continues to expand.

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  As the technologies change, the science of scale up has to evolve with them. Lawrence Livermore is building the physical space to do it. On the Livermore Valley Open Campus, a new prototyping enclave is envisioned, a place designed specifically to help move technologies across that valley between a lab bench and a real production line.

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  There's all kinds of stuff we're doing actively that are really pushing the bounds of what technology can do. Our unique role is that we're working on things for the public good that has never been done before in the world.

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  But the measure of success remains the same.

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  Is somebody willing to adapt it, adopt it, and use it, and do that on a repeated basis because it actually meets the function that they need? That, to me, is the hallmark of successful scale up. If it leaves our hands and is being used in the world, that, to me, is a success.

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