Foundry Fireside

Intro:

Hi everyone, and welcome to this episode of Foundry Fireside. My name is Megan Nguyen and I’m an intern on the Foundry Communications team. Today I’ll be chatting with one of the Foundry staff scientists, Stephanie Ribet. Her research focuses on transmission electron microscopy development at the intersection of computational, experimental and hardware advances.

Her research focuses on scanning transmission electron microscopy development at the intersection of computational experimental and hardware advances. Welcome Stephanie, and thank you for joining us today. Could you please introduce yourself? 

Hi, Megan. Thank you so much for having me. I’m very excited to be here with you today to talk more about my research. Um, so as you mentioned, I’m Stephanie, I’m a staff scientist here at the Molecular Foundry. I work at the National Center for Electron Microscopy, uh, which is located here at Berkeley Lab. 

1st question:

Thanks, Stephanie. How about we get started with you telling us about a brief overview of your research? 

Sure I’d be happy to.

Uh, so first of all, I’ll say that I’m a material scientist. Uh, material science is an interesting field. Um, it’s at the intersection of a bunch of different types of sciences and also engineering, and it’s all about making better materials for our everyday lives. So this can be anything from designing new computer trips, um, so we can have better, faster, more efficient computers.

To designing new foams that go in football helmets and running shoes, so that way we can have sports equipment that lasts longer and keeps us safer. This is all material science. Um, and what I work on specifically is transmission electron microscopy. This is a technique where you have electrons that are very high energy and what you do is you accelerate them.

So they go through your sample, um, and you image what happens when they come out the other side. And this allows you to form images and get structural information at much higher resolutions than you could with a conventional microscope that you might be familiar with. So in your science classes, you may have used something.

Um. Microscope. This is an optical microscope, so by comparison, we use electron microscopy to be able to image features that are even smaller. 

2nd question

Leading onto this, why is it important to study materials that are too small to see with the normal microscope? 

Uh, yeah. So as I mentioned just a second ago, a transmission electron microscope allows you to image things at the nano to atomic scale.

So I’m talking about anywhere down to, um, even seeing individual atoms to the nanometer scale. To give you a little bit of context, this is much, much smaller than anything we have in our everyday lives. So something the size of, uh, less than a million times smaller than a tennis ball. So we really don’t have a very good intuition for what materials look like at the sling scale.

And we often learn some very surprising and interesting things when we do these experiments. So one example you can think about is batteries. Um, we know batteries are very important. They’re in all of our devices and, uh, are crucial for the future of energy storage. And in general, the way batteries work is they’re these layered materials.

Um, and when we design them and think about them and think they’re these perfect layered structures. So one thing you could do is take batteries and put them in the transmission electron microscope, and you can take perfect batteries when they’re first made. Um, you can take them after you use them a little bit and you can take batteries when they’re close to failure and try and see what’s going on.

And it actually turns out that they look nothing like we think they do. Um, you do have this layered structure, but it turns out they’re much more intricate and complex and they have these sort of messy, what we call interface or where these layers meet. And when we do this sort of failure analysis for the transmission electron microscope, often what we find out is it’s not the layers themselves that are the problem, but actually where they meet.

And this gives us a lot of ideas for how we can think about designing better batteries. So this sort of gives you a sense of why we care about doing characterization at these very, very, very small light scales. 

3rd question:

Additionally, what are the kinds of things that you can see in a microscope? Uh, you, you can see all kinds of things.

And I guess one thing to say is you can put a lot of different types of materials in the electron microscopes. So I’ve looked at everything from, uh, meteorites to human tooth enamel to polymers. All these different types of materials go in the electron microscope. And as I mentioned before, you do imaging at very high resolutions.

So if you’re looking at something that’s ultra thin, you can get down to doing atomic resolution imaging, where you’re seeing how, uh, different materials are organized down to the atomic scale. In general, what you can do with a transmission electron microscope is understand how things are ordered. So in material science, we’d say something that’s very ordered is crystalline, whereas something that’s much more disordered, we’d say, this is amorphous, and you can actually study this with electron microscope at these very high resolutions to see how the structural changes from crystalline to amorphous or something in between.

And this is important ’cause it gives us a really good indication for kind of what this material looks like and what its properties are. With this electron microscopy, it’s not just a structure we can see, but we can also study what we call composition or find out what elements are made up of our material.

So whether or not it’s iron or carbon or nitrogen, we can find out all this information. So we end up with not just these images, but these really beautiful colored images that tell us the structure of our material. So it’s organization, but also what it’s made up of. 

4th question: 

That’s really interesting. Leading onto this, uh, topic, what exactly is cryo-electron microscopy and how does it work?

Uh, yeah. So cryo cryo-electron microscopy is a flavor of electron microscopy. So usually when I do my electron microscopy experiments, we’re doing this at ambient conditions. So basically room temperature. It turns out that if you cool something down, so you do this with liquid, liquid nitrogen, which is 196 negative 196 degrees Celsius, so very, very, very cool.

Uh, you can prevent damage. So you can imagine if you have these very high energy electrons that are coming down to interact with your sample, sometimes your material can damage. It gets broken apart by the electrons, and if something damages before you can image it, then you really can’t do a good study to see what’s going on.

So it turns out that if you cool down samples to this liquid nitrogen temperatures, you can prevent damage. And then samples can be, you can see things that you couldn’t see otherwise in the electron microscope and cryo-electron microscopy has been really very influential and, um, beneficial to a lot of fields, but especially, uh, biological sciences.

So the Nobel Prize in 2017 was really awarded based on cryo-electron microscopy for biological sciences and increasingly, uh, recognizing in the material science community as well the value of using these cryo-electron microscopy techniques to be able to see things that we otherwise couldn’t. 

5th question:

What are you working on to change what we can study with electron microscopes? Can you tell us more about your upcoming projects? 

Uh, sure I’d be happy to. Uh, so as I mentioned, cryo-electron microscopy has really been used in biological sciences and has been growing in material science. So that’s really something I’m interested in, is working at the intersection between these two fields and finding new ways to leverage cryo-electron microscopy to see things we couldn’t see before.

And the electron micro. Um, and in fact we have, um, a new initiative here at Berkeley Lab. We have some new electron microscopes that are coming. This is going in the brand new, uh, BioE building. This is the collaboration between biosciences, um, and, uh, molecular foundry as well. And this is gonna be a new initiative to look at electronic microscopy, um, and apply it to these different fields and kind of learn how we can work together to see new things.

Now cryo is a big part of this, but also computation. So in recent years as microscopes have gotten better, we’ve had a lot of new, um, hardware developments as well. And one thing now is we can take data instead of just collecting an image, but each time we try and look at our material, we collect a lot of different types of data all at once.

And the data has gotten so big that we can’t really interpret it by eye as the data’s coming off the microscope. So we rely on all these advanced computational tools to be able to impact it. The reason we do this is because it lets us see things that we couldn’t see otherwise in the microscope. So I’m very excited for this new initiative because it’s gonna be a collaboration with a lot of groups on campus here at Berkeley. So both in biological sciences, material sciences, and also some computational groups as well. 

That’s really cool. I hope I get to see these new microscopes at the building in the future. Uh, yes. Please come visit us. 

6th question:

So in your development of hardware and studies of novel materials, I understand you work with something called phase plates. Could you explain what these are? 

Uh, sure, I’d be happy to. So face plates are a type of hardware that we’re developing. So in general, in your electron microscope, you have these electrons that come and interact with your sample. And there’s some really fundamental physics that governs what these interactions are like and the types of, um, interactions that you can.

Study and the type of data that you can collect. Um, so we’re working on trying to change how these electrons that before they impact your sample, what they’re like. So we can, um, maybe think about this in terms of analogy. So electrons have something that’s called a phase and we could, um, describe a phase instead by color.

So imagine that your conventional electron cross could be an experiment. All your experiments you’re doing are with green electrons, let’s say. And so you were only really studying how green electrons interact with your material. But having a phase plate would allow you to have not just green electrons, but then you could have.

Purple electrons, yellow electrons, blue electrons, so all these different colors. And not only can you have these different colors, but give you a lot of control over, um, what types of electrons you have. So now you can study all these different types of physical interactions that you couldn’t before. Um, so this is something I’m definitely very excited about.

It’s, um, a new hardware we’re working on developing that will go in the electron microscope. Um, it’s definitely challenging, but also offers the opportunity to do things that we really don’t routinely do. So that’s why it’s exciting and we’re working on it. 

7th question:

How does your work translate to the real world and be broadly applicable across many science and engineering disciplines?

Uh, yeah. There’s, um. A number of ways that the work that we’re doing can be translated. Um, and maybe I’ll answer your question in two parts. Uh, so the first thing is where we work is the molecular foundry. This is a user facility where it’s, um, free to use and people come from all over the world, use our facilities.

So the science that we’re developing, we can share and through collaborations and working with all of our users. So this gives an opportunity for what we’re doing to be more broadly used in all kinds of different fields. The other part of this is it’s really important for us to be able to share our work so other people can replicate it, can use it, can validate it, and we do this through writing papers and also the development of open source code and open source methods that we share online for free for other people to use this.

So that’s the first part is sort of letting other people, um, use our electronic microscopy methods working together. The second part is all about translation of this, what I’ve been describing as more basic research and basic science, but translation of this basic science into everyday applications. So I’ve had, um, uh, opportunity to work in industry and also in academia on the translation of these.

Basic scientific ideas to real applications and devices and sort of products that you would find in your everyday home. And it’s really cool to see how, um, the type of research that we do here at the lab can easily be translated into types of applications and things that you might be familiar with. So I mentioned before, um, foams and football helmets and computer tips, but these are all different types of applications.

Um, and when I think about the types of users we work with and the people we work with, the lab, they really span all kinds of different applications. So everything from quantum technologies and next generation computers to biological sciences, to energy sciences, to metallurgy, to engineering, to aerospace, polymer manufacturing.

There’s so many different applications. And so that’s what’s cool is you can see how the work that we’re doing in the lab and in these labs can translate to all kinds of real world everyday applications. I did not know that all of these could be applied to many different fields. Yeah. Uh, electron microscopy is pretty versatile, and that’s what’s cool is to work with people with so many different expertise.

8th question:

Lastly, what makes you excited to do this work everyday? 

Um, yeah, as I, I alluded to in the last, um, question. Um, we have collaborators from all different fields and all kinds of different applications, and it’s pretty inspiring to talk with all of them and hear about the applications they’re working on.

And I’m excited every day and inspired by the people I work with and the opportunity to, uh, work with them and contribute to the type of research that they’re doing. Okay, this is my outro. Thank you so much for talking with me today and I’ve learned a lot. 

Outro:

Okay. Thank you so much for talking with me today and I’ve learned a lot about your research. For those of you watching, stay tuned for more episodes and see you next time on Foundry Fireside.

 Hi everyone, and welcome to this episode of Foundry Fireside. My name is Audrey and I’m an intern on the Foundry Communications team. Today I’ll be chatting with one of the Foundry’s staff scientists, Ed Barnard. Ed leads this Foundry’s data group supporting building new automated equipment, implementing data sharing and analysis pipelines, and building data support infrastructure.

He’ll be sharing more about his work, the impact of the data group, and how their efforts are shaping the future at the Foundry. Welcome Ed, could you please introduce yourself? Thanks for the introduction. My name’s Ed Barnard. I’ve been a staff at the Molecular Foundry for I think about 10 years.

And I’ve been happily working on this new data group for the past couple years. I’m excited to tell you about it. The data group is relatively new to the Foundry. Why was it created and what specifically in data and automation was it designed to address? At the foundry and in science in general, we really realized that scientific work has been increasingly data driven. There’s bigger instruments, more instrumentation, and data sizes are increasing. For example, we have instruments at the foundry now that can create terabytes of data per minute. And so there’s a lot of need to handle data and also a lot of interesting new opportunities using machine learning techniques that have been developed recently in order to make data capturable and accessible along with a lot of robotic systems that we might be able to use machine learning and data-driven techniques to control. 

Why is having a shared data infrastructure so critical for research at the Foundry? As I said, because of the increasing amount of data and the need to be able to search it and kind of clues in that in large data sets, having an infrastructure to do so is quite important. As we have larger and more complex instruments and synthesis tools we need to make the data more accessible. We need to store all the data, make it accessible and we also have a need as we grow in different ways. For example, having remote users that wanna be able to access data that is created at the foundry while they’re from anywhere in the world. 

During the pandemic, you helped design custom parts and remote systems for the laser lab. How did that experience shape your current work? I really enjoy the interaction between computers and the physical world, and I wanted to make experiments better and easier to use in general.

During the pandemic, I was actually, my main job was running an optics lab where we had microscopes to look at many different types of materials. But we had to limit access to the lab so that our users or who still wanted to research. They weren’t allowed to be in there so we wanted to enable that user science and really make our tools remotely accessible. So we had to build infrastructure both physical and digital infrastructure to make that possible so that users could continue to use those pieces of equipment even though they weren’t in the lab, and then also access their data once that data was collected.

How is your team working to support the use of machine learning and AI across different facilities at the Foundry? Well, one of the big things about machine learning is it needs data to learn. And so we really need to figure out ways that we can take the data that we are collecting at the molecular Foundry and make it machine learning and AI ready. 

The concept of fair data really kind of applies to make data a more accessible to both people and computers. We want data to be findable, accessible, interoperable and reusable. And so those techniques of making data all fall under all those categories will be very useful in making the data that we collect at the Foundry useful to both our users and machine learning models that we might build.

We also are really looking at a very interesting way that we can use machine learning as a feedback loop while we’re synthesizing materials, we make a material here at the foundry. We characterize it at the foundry, and then we have to make a decision about what is the next experiment we want to try. And so inserting machine learning in that loop, using robots to make materials and to characterize materials is allow will allow us to make very fast feedback loops and make that capability something that the Foundry is really a leader in. 

Can you explain what these platforms of Scope Foundry and Crucible are? Sure, Scope Foundry is an automated microscopy and instrumentation software platform that allows us to build a custom instrument in the lab and then control it with software by orchestrating the actions of different parts of the instrument. An important part of it is that we can control all these different parts, whether they’re an existing instrument or a custom instrument, and collect all of the data that the user wants, along with all the metadata, the information about the data into a standard data platform.

And the stand data platform that we’re using and developing is called Crucible. Crucible is the molecular Foundry’s data platform that we have been developing. It is designed to help with the data collection and centralization of that data collection, so automatically getting data from instruments to the platform and being able to search that metadata, automate automatic analysis of that data and make that data accessible to the users here at the Foundry, and also to users who might be anywhere in the world who need to be access to that data. 

How does Scope Foundry integrate existing laboratory hardware into new custom automated experiments? At the Foundry we have over a hundred different scientific instruments. Some are completely custom and controlled by Scope Foundry, the things that we’ve built. But there’s also instruments that we get from vendors that serve a purpose. But we want to expand on that, and that’s where we can connect with custom built parts that go onto our existing instrumentation and we can control that interaction using Scope Foundry. An example of that is the autobot platform that we developed which is a robot called Spin Bot that makes materials. And we added characterization tools, things that we use. We use optical characterization. We make it made basically a microscope attached to this robot to allow us to get information about. This example once we made it and that’s enabling those feedback loops that I was talking about before. 

How has the Crucible platform scaled to support large data sets and many users across the world? In our design of Crucible, we realized that we have quite a lot of users at the MCL Foundry and then also many potential users around the world. And so when designing the system, we built it on a cloud com computing platform so that it can scale all depending on how many users we need. And so leveraging that capability so we don’t have to build racks of servers here has been very important in design consideration when building a system that can scale.

How do you see the work your team is doing now shaping the future opportunities for scientists and users across the foundry and in the years to come? The foundry and science in general is, like we said, becoming more and more data-driven and machine learning, and exists now as a tool that we can use. And so we will, that we can enable our current scientific workflows and to be more data driven and easier for users to get insight into what they’re doing and to accelerate the scientific process. And I think there’s a huge opportunity to expand beyond that and do things that we didn’t think were possible before by automating the characterization of materials that we make and allowing machine learning to make some decisions or work with humans to make decisions to explore to do material discovery, feedback loops that we never thought were possible before. 

Okay. That’s all the questions I have for today. Thank you so much for joining me. It was really great to hear about your work.

For those of you watching, if you’re interested in learning more about the research at the Molecular Foundry, stay tuned for more episodes of Foundry Fireside.