Carbon Monoxide: Foe . . . . or friend? Leo Otterbein on the potential of carbon monoxide in medicine

usual diffusion. There's no shock boundary. Yeah, the contrast compact expansion have a sharp boundary. There is a point where analytically it's zero. So when you look at regular diffusion, there's this very clear homework. The area grows linearly with time. For these repulsive particles, the theory predicted that it actually spreads much slower than regular diffusion. Okay. Not as the square root, but as T to the power of a quarter. We saw something even more, more slowly. Hi. Welcome to the Science Fair podcast. I'm your host, Susan Keatley. I'm a PhD chemist, writer, and I love talking to scientists. On the Science Fair podcast, I aim to bring you conversations with scientists doing fascinating, cutting-edge work on all kinds of interesting phenomena, ranging from physics to chemistry to biology and even the nature of science itself. In this second season of the podcast, we'll start by asking each scientist a little bit about their journey to becoming a scientist. And then we'll talk about their research and how it relates to one or two of the high school science standards from that scientist state. So come along and tune in for some science fair. We have two guests today, Naomi Oppenheimer and Matan Yabbenzayan. Naomi is an assistant professor of exact sciences in the school for physics and astronomy at Tel Aviv University. She's interested in complex fluids, statistical mechanics, soft matter, and biology inspired physical systems. She uses theoretical analytical tools, numerical simulations, and a dash of experiments. Some of her research achievements include predicting the effect of protein concentration on membrane viscosity and understanding why crumpled paper is shapeable. Her future directions include studying heterogeneous materials in biology and for next generation functional materials. Matan is an assistant professor of artificial intelligence at the Donders Center for Cognition at Radboud University in Nymagin in the Netherlands. Matan's research focuses on natural computation and collective behavior. He uses a combination of applied physics, statistical mechanics, artificial intelligence, and material science to explain collective behavior in nature and to realize it in robotic swarms. Some of his research achievements include programmable self-assembly on the subcellular scale, developing a synthetic swarm of micro-swimmers on the cellular scale, and designing decentralized learning in robotic swarms. Naomi and Matan, welcome to the show. Thanks so much. I would love to start today's episode by asking about your path to scientists. Maybe you could tell us how you got to where you are today, and if you could share some of the highlights of your journey, any unexpected twists and turns, you hit along the way, and how you came to work together. Sure. I think as a little kid I used to love detective stories, and in particular, Sherlock Holmes. When I grew up at it in high school, I had this wonderful chemistry teacher, and from her I learned that in order to have a good detective story, you don't have to be a detective, you can be a scientist. And just the questions are not about a murder case, it's about molecules and life itself. And so that's how I wanted to, I knew I wanted to become a scientist, in particular I wanted to be a chemist, just like her. And I added the physics portion as a wham really, because I read it at the time just before joining undergraduate studies, I read the biographical books by Richard Feynman, and I loved how he taught about things, about everyday things. And that's just what I wanted to do. So I decided to combine both of those passions. And Matanda, you know, tell about yourself. Sure, not only. So yeah, I guess I also had an inclination that was already young, to science. In particular, I remember reading some books from the books, by Russell Stanard, about Uncle Albert and blank, blank being black holes or quantum mechanics, where he's a fictional book about his niece that walks into this very physical thought bubble that he has, and she learns about quantum mechanics and black holes. Through that, and in particular, it made me realize that the cosmos has a manual, and it lies in these sciences. And then I had a lot of really good teachers that were fortunate in that regard. In particular, my high school chemistry teacher, Gregory, with whom I'm still in touch with, he got me really passionate about chemistry, which later led me to take chemistry and undergraduate from an bachelor degree, and it was only later that I moved to physics and artificial intelligence and robotic. Well, I just wanted to say, I also love those Richard Feynman books. And I didn't actually know about them until I started dating my husband, who's a trained physicist, and I would take them off his shelf and read them. And I just completely fell in love. And then my husband told me, well, Richard Feynman says, nobody really understands quantum mechanics. And I was like, yes, and Matan, I'm so happy to know about the Uncle Albert books. I haven't heard of them, but my son, who is eight, is so interested in space, black holes, you know, also like volcanoes. But I think that he would really like these books. So I'm going to check them out for him. And so how did you two come to work together? Great. So we actually met on the first week of college. We both took chemistry, and we both kind of went into the physics direction later on. But it was the first week, and it was a huge classroom. But it was almost empty. So there were many empty seats. And one of these empty seats was next to Matan. So I said, I said text to him. And I asked him if he can spare a piece of paper, even though I had paper of my own, but I just wanted to strike a conversation. So I liked him from the very first minute. And from that point, we became friends and lab partners. And later also I liked partners. That's very cool. I should say that I found out that you had paper only years after. For a very long time. That's a great tactic. Well, and I also find it's interesting that you both cited your high school chemistry teacher. I also had an excellent high school chemistry teacher that really turned me on to chemistry. Before we move on, I just did you find it? I mean, I think for me, I always felt physics was more difficult than chemistry. Did you, what was it like sort of adding on physics later in your science journey? It definitely was harder. So I had part of the classes from with the chemistry students and with the physicists. And it was, I struggled for the first time in my life in the physics classes. And chemistry was a breeze as it was in high school, but it was, I felt like it's pure, in a sense. You ask the more basic questions. Chemistry, what I love about it is that it's a lot like cooking. You create these new materials. But physics is the essence of these materials. I really love both of them. I have to say that for me, it kind of went back and forth. So also in high school, I had a few classes in physics. I think I was decent at it at them, but the teachers were very rough. And I think that sort of made me more chemistry inclined because that teacher, a druggler, was really a flifting. But then once I turned back into physics, I felt and still feel like I'm always a little dumb. But this was like that for a long time. And it's actually still like that. But I learned that I'm not alone. And many times in physics, people feel like Everyone else would have understood. what's going on but everyone have this feeling. >> That is so good to hear and I think students listening to this will really appreciate that. So in June, on June 4th, you both published a paper in physical review letters entitled Compact Expansion of a Repulsive Suspension. And you looked at how a repulsive substance, so something made of particles that repel each other, you looked at how that substance diffuses. So before we get into that example of diffusion, I would love to first ask you to explain regular, formal diffusion. What is it? How does it work? What are the hallmarks of it? >> Sure. So diffusion is really just the movement of particles. And actually it doesn't have to be particles, it can be even heat or energy. And it's the movement from a place where they are abundant to a place where they are scarce. And it happens all the time, all around us, across scales. I think the very basic example is if you have a glass of water and you put a drop of dye in it. Or you can think of even just hot water and you put your tea. And you watch as it spreads. It spreads that the process by which it spreads is diffusion. It happens also in the air we breathe. So when we breathe air, there's a high concentration of air in our lungs and it diffuses into the blood stream. Or another example can be of a, for example, plants where there's wet soil and the water diffuses into the cells in the roots of the plants by diffusion. This is actually called the smosses. The smosses is just diffusion of water into the cell. And yeah, so many examples really all around us. That is from. >> And there's a good reason why it's so abundant. And because you can describe diffusion that the examples in only a gauge were from macrosky or how does all the air it is from one side to the other, how does the color of the tea changes or the water changes. But once you zoom in and look at the individual particles or molecules or atoms, they will essentially look very, very much alike, irregardless of their scale. And the individual will do what is known as browning motion. They move around what seems to be a very random motion. In principle, it is deterministic. They're being pushed around by other particles that maybe you don't see. And you sort of average them, I'll just say, okay, I don't care about what's going on beyond. Apart from them, there are little particles that move around randomly. And what's nice about this picture is that now you can look at the individual and ask how does that particle move and taking the average, and the average essentially is what you see globally on the macroscale. This is a diffusion that you see later on. And what's nice about this formalism, there's an mathematical structure for what is known as the castic process, this motion is castic as random, it can be applied much beyond the thermal systems or atomic systems, you can say what is the diffusion of the pride of lions? This is how you can characterize them in the savanna. You can also use the same ideas in the stock market. It's really the basic ideas. And then you can apply it more generally for diffusion of information and so on. People do these days in active places in research. Maybe I should say a word because I think it's related to our research as well. So when you think of diffusion, there's this macroscopic picture and there's a dye spreading in water and there's the microscopic picture of the individual particles moving randomly around. And you can describe each of these pictures mathematically by a different equation. So the individual particle you would just describe by Newton's laws. So there's a force, there's a force, and there's an additional and there's acceleration, MA, F equal MA, plus some random force from the environment. And the macroscopic picture of the dye in water, you describe by the diffusion equation. It's a differential equation. But you can move from one to the other by what is a process which is called coarse graining. So if you start from the microscopic and you coarse grained these equations, you get the diffusion equation. That's interesting. And in the molecular model is the assumption that the particles don't have any attraction or repulsion to each other. They're just sort of elastic collisions if they happen to bump into each other. Exactly. Yes. So you can add forces on top of that. But for regular diffusion, yes, there is no other form of interaction. And so how did you get the idea to look at diffusion in a repulsive substance? And what did you think you might find? So for this, actually, as happens many times in science school, I was looking at a completely different problem. And that is a problem which is related to particles, proteins, which are rotating in the cell, in the membrane, which is surrounding each cell in our body. These are proteins which are called ATP synthase. And they are related to the process, to the energy cycle of the cell. And this is a work I did during my postdoc with Mike Shelley, which was also in your podcast so there. And then what we were trying to understand is when people look at these proteins in the cell, they see very regular order the race. And it's known that these proteins are rotating. We were trying to understand if this rotation is what's causing them to self-assemble, to order themselves in a hexagonal and this crystal array. And we found out that indeed it is related to the rotation and we have the small dots and simulations. But we saw in the simulation something which is also not related to the original question. And that is we saw that they ordered themselves in this ordered array. But if you think of a lattice, a crystal, the spacing between the particles is always the same. But in our case, the spacing between the particles was not the same. The spacing was larger and larger as you went away from the center of the particles of this suspension. And we asked ourselves why that is. And what I discovered is actually that this is not related to the fact that how these particles arrange themselves in space is not related to the fact that they are rotating. It's simply because there is some repulsion between the particles. And that led us to the more general question. If you have particles, doesn't matter if they rotate, they don't rotate. That happens between them. As long as there is some form of repulsion, how do they spread in space? Right. So this was happening because these particles have some level of repulsion. Exactly. And that was affecting their spacing. Yeah. Yes. Right. When you decided to look at this specifically in this paper, what was your hypothesis in terms of what you might see? So we really didn't know we were surprised that to discover that it looks so much like diffusion. So if you look at it by eye, it looks like diffusion. Particles are just repelling each other. And you start with the small drop and the drop grows with time. What we didn't realize, it's not just like diffusion. And we'll talk more soon about the differences between regular diffusion and what we discovered. There are similarities and there are differences. We were surprised at how general this phenomena really is. So we started with these proteins that rotate in the membrane. But then we tried many forms of repulsion. And we saw the same thing over and over again with all of these. And maybe Matancan gives some examples. Yes. So, yeah, I think this is sort of the interesting thing here because there was some peculiarity in a very specific system, rotating proteins and cell quasi-2D membrane that are interacting heteronanically. But there was something weird there that captures memory's attention. And then she started looking at it. more typically internalize the act of the originareic. It is widespread, sorry, feet. And then we said, OK, maybe we can find an experimental system that can emulate this. Now, the thing is that repulsive particles are very abundant. I mean, in a way, if you really think about it, something is a particle once it's repulsive. If it's not repulsive, if it attracts things to it, then it's just accumulating. It will no longer be this particle. Right. So there are different forms of having things that are repulsive. You can have a charge repulsion. You can have some form of elastic repulsion. You have all sorts of repulsions. So the question was, can you find a system that has many particles so that we can have this individual picture to the individual, the picture of the collective, that we can look at and test the simulations and later the analytical picture. And it would so happen that the timer was working with this experimental system that we were looking at particles that are a few microns in size. So there are roughly the size of, say, red blood cell, maybe even smaller than that. They're charged so they repel. And we had this way of squeezing them, many of them together. So they're really, really tight together. And then we can let them go and roughly and see how they spread. And since they're big enough to be observed in an optical microscope, you can see everything in real time, you can see the individual how it's moving, but you can also sort of zoom out or look from bird eye and see how this whole system is spreading. And such a sort of work thing started to be interesting because we started to see some signs that were similar to what we signed up in the original work in the original duration. But some things were a little different. And we were a little puzzled by what's going on. I think let me tell more about that. Sure. But I wanted to say about the experiments that, the fact, I think, Matan, maybe correct me if I'm wrong, the fact that your particles were charged is almost by accident, right? It's true that you had these colloids for a different experiment. And it's just that at this scale, this microscopic particles or even microscopic particles, they're always charged. It's really hard to get rid of the fact that they are charged. So you see it not just in these colloids. You see it in proteins, you see it in vesicles, you see it everywhere in the biological world that particles will have some form of repulsion. Usually due to charge, but it doesn't have to be charged. Right. And so they're really ubiquitous. Whoever knows some chemistry may find it very obvious, because the moment you remove one electron, one proton for any molecule, it is charged. So in a way, neutral is the special case that having everything on it would be one particle off. When electron off, you become charging. It's true for protein molecules, but also for black holes. You take one particle off, charge, and you'll have different dynamics. So yeah, it's easy to get things to be charged. And you can also see this daily. When you were a glass of soy milk, if you want, and add some lemon juice to it, you will change to the pH, again, charge proton. This will take the protein in the suspension that were previously very nice and happy and stable, because they were charged and would tell each other, they will no longer be charged, they will no longer be felt, and all the time, you will find yourself with a nice clump that's at the bottom. You can try it yourself. I tried it yesterday just to make sure that I'm not playing notes. Yeah, I really want to do that with my kids. It's going to be amazing. I just-- I've never thought about repulsion as sort of-- like having a purpose before. But it's so interesting, like actually kind of keeping things separated, keeping individual objects discrete, discreetly their own. So that's really neat. This is used in many industries. So for example, in one way of separating viruses, a very common way to do this would be to either change the pH or change something in the electrical properties or the fluid they're in, because also viruses are charged. They're usually charged. This allows them to recover each other and spread more better, also to attach to whatever I picked up they are interested in. But once you change that, you can calm them together and separate them. It's easy. You're fine water. You can study them. So you had your experimental system, which were these charged colloidal particles. You had your theory, I think, of diffusion just in a system, the most basic system. How did you bring all these together with simulations? I think that maybe students might not realize the role of theory and simulations and experiments in getting to a discovery. Yeah. I think what I really love about this work is how it combines all of these free aspects. And actually, we started from the simulations. I started by the protein. The protein? Well, the proteins were-- Right. There were the proteins, one which were the motivation. And then I did many other forms of simulations of particles with various forms of repulsion. And so how-- So in the computer, it's easy to have tens of thousands of particles and change-- you can change to whatever you want, much more easily than an experiment. And you can see what happens. It's like doing an experiment, but in your computer. So I can actually see them spread. And I can observe-- Right. So I can see them spread. And then, with these simulations, I decided to do also try to write these equations, which turned out to be just like diffusion, but a bit more complicated. So it's what's called nonlinear diffusion. And with the theory, I then could-- I got some prediction. The prediction was, if you have a drop of these particles, what is the radius? How does it grow with time? So I had a prediction. With the prediction, I could go back to the simulations and see if it's true or false. And I saw that indeed it follows the theory. And then we've-- these two-- then we went to the experiments and tried to see if the experiments also answer the same trend. If you look at the radius of the Matan's colloids with time, do we get the same answer? And as Matan said before, we did not get the same answer. So at first, we were very upset. And it was a puzzle. We were upset, but we were also intrigued, which is, I think, also something that I love about science. You realize that you're wrong, and you try to understand why. And then you have a new question. It's always these constant puzzles. So Matan, maybe you can say, what was wrong in the experiment? The experiment was perfect. Just started. It was nothing wrong with the experiment. So you talked about simulations. And now that we're starting to discuss this, I realize actually there's an important point that Naomi touched on. Is it's computer simulations or trying to simulate or to emulate the real world? But when you have the agency to choose whatever sort of interactions particle have, and this great freedom makes-- can create the slippery slope from taking a physical simulation into essentially a computer graphics or an animation. And it's not clear what's the difference, where do you draw the line? Actually, these two worlds feed onto one another. They teach each other computer graphics and physical simulations. But if you want to say a statement about nature, you want to make sure your simulation doesn't go a-train. And Naomi will be very diligent about trying different interactions and making sure that this is generic. It's not just some fluke of the computer or if there is something inherent that has to do with-- Right. We always saw the same effect. Yeah. And the effect is sort of in the name of the title, it's the compact expansion. So this is something that actually took me a long time to wrap my head around. So I hope I will be able to explain it in a couple of sentences. But by contrast, the usual diffusion-- usually, usually when you look at the microscopic, say, concentration profile, something that is diffusing, the profile is diffused for lack of better words. It's not-- It's not just foundry. Yeah, it doesn't stop abruptly, it's diffused. So if you can think-- if you're familiar with a bell shape, like structure, it has a bell shape, like structure. It's gradually decreasing, it decreases, as you go away from the center. Right. the contrast. compact expansion or compact ton as they summon something call it in technical terms, have a sharp boundary. So you go from the center, our words, there might be some profile that will be variations in concentration, which are related to the lattices that Noni saw. Now we sort of understand it in retrospect. But then there will be a point where it's zero, identically zero. And this is very, very unique. This is something that there was little work that sort of identified this. And this is something very unique. And the fact that we could see it sort of in real life under an optical microscope in real time was sort of the intriguing. And this is what we did see in the experiment. We let we sort of squeeze the bunch of particles and how many thousand of particles together, there are all charged, negatively charged, so they are not happy. They want to go away, or repelling one another. And then we sort of let go of this tweezers that held them. And then you watch them expand. You watch them slowly expand. And it's very clear that there is sharp boundary between the drop and the rest of the world. It does not look like normal diffusion, the classic planes and all diffusion that you know from the textbooks. And this one is the similarity that we found. So this one qualitatively was like, "Oh, okay, so we got something that is very special, different." And then we wanted to get more quantitative. And this is where things were different. And this was sort of creating more of the puzzle that we had to solve and was very interesting. Because we went back sort of to know, man, it's all right, well, okay, so we look at how quickly does it spread, it doesn't look like normal diffusion, but it also doesn't look like exactly what you're predicting through the model. That's something else. Maybe we should say, so when you look at regular diffusion, there is this very clear hallmark. So if you look at the radius of the drop as a function of time, it grows as the square root of time. Or you can say that the area grows linearly with time. Sure. But in our case for these repulsive particles, the theory predicted that it actually spreads much slower than regular diffusion. Okay. Okay. So that's those are the predictions. And that's what we were, that was I saw in simulations in the theory, but that's what we were hoping to see in the experiments. But we didn't see. We saw something even more, more slowly than t to the power of a quarter. That was logarithmic with time. Wow. Yeah. Actually, at the beginning, we didn't even understand what logarithmic, we were just not sure what is the exponent there. It was quantitatively different, but wasn't clear how. Yeah. Right. And that drove and that drove us to sort of reexamine the model. And I think, I think something that was interesting for me, and I think this is why it's so important to do experiments and simulations and really get these to marry together, is that we realized from the experiments how there's actually there are fewer genes of dynamics. So when we started this discussion right now about diffusion, regular diffusion, I said that when one thing that is nice and makes it very ubiquitous is you can describe this whole collective by the individual. What's the individual is doing just averaging of many such individuals. Yep. If I may know these prediction, looked at what happens when this individual interacts with many others through this repulsive interaction. Right. So when particles are repulsive, you don't care, you start to care about your neighbors. That's right. Yeah. And that's much more complicated. Yeah. And looking at many interactions had the prediction that Naomi had. There is an intermediate regime where you're not interacting with many others, but you're interacting with a few others. Only those that you see. Yeah. For example, only the nearest particles around. Okay. And then the behavior is something intermediate and that explain that explain what we saw then in the experiment. Yeah. When we did that out, we saw that there's actually a crossover between the two dynamics. That's great. Right. And then so then we went back to the simulations and we realized that if we just wait longer in the simulations, we might get to this limit, this other limit as well and that's indeed what we saw. So when we waited long enough in the simulations, we saw this logarithmic behavior, the crossover from one behavior to the slower limit that we saw in experiments as well. Because in the beginning of the simulation, it's in the shorter time, it's the beginning. And so you're seeing you're very dead. Kind of like every single interaction is repulsive, but you get to a point where particles are too far away to feel the repulsion. So it kind of is not a factor anymore really. Right. So many times in nature, there's repulsion, but this repulsion is not infinite. There is some kind of, so if you go beyond this cutoff, you no longer feel repulsive repulsive repulsive repulsed by the particles. Yeah. Yeah. That's really neat. Wow. I mean, so how how long did this take? You know, once you really started doing these particular simulations and then you went to the experiments and then you came back. What kind of time scale was that? I think it took us a couple of years. I mean, it was there was, we moved from the states to Israel as well. So not counting that kind of time in between around two years. Yeah. Yeah. I'm just, and I know you're working on other things at the time. I mean, do you think that there's some benefit to doing things over a significant amount of time so you can be kind of thinking about it in the back of your mind or maybe you're working on something else and that gives you another idea of what you can apply here. Does that come into your process? Definitely. I mean, I think that's also what I like about the academic life. And I like, for example, startups, startups where you have to have your product really quickly here. You have the, you can take your time and you can get motivations from what other people did and from what other things you're that you're doing to help you solve your puzzle. Yeah. Let's talk about why these results are significant for different kinds of systems and what new questions do they raise? So I think as we said, these are so abundant in nature and in our everyday life. So you get examples from biology, these proteins, vesicles, everything is charged, but also materials are charged. So ink, for example, is charged as well. Grains of sand are charged. This is on the small, from going from small scale through larger scales, but also humans are repulsive and robots are repulsive. So we were thinking, we go to demonstrations often nowadays and these have very dense arrays of people and people also we don't want to be too close to one another. So we're also repiled by other people and we are hoping that maybe we can test our theory in one of these demonstrations, you know, film it from above and see when the demonstration ends, how do people spread apart? How does it de-agree? This goes sort of back to the fact that it's not the theory, this model is not very sensitive, it doesn't really depend on the specifics of the interactions. What we would know me started with trying different models in her simulation later on in the experiment. It's not very sensitive, so it could be charged charged repulsion, it could be a sort of elastic repulsion, some two springs that are pushing into another, but could be also some sort of social distancing that effectively is repulsive interaction. It can be very from one country to the other, but based on their social habits, but it's effectively a repulsive interaction. Yeah, and maybe it can help and how to, you know, help crowds go through constructed regions or how to help demonstrations be more safe. Yeah, it could be, this is sort of where we're hoping to inspire others. I just had an idea, I just read something recently about this, but I'm pulling it up, but while it's coming up, how, like, what would you continue researching in this area, and if so, like, what would your next focus look like? So yes, we actually, and we're working on another problem, which is very much related, and this is with my master's student, Edel. So with Matan, we looked at particles that have some cutoff to the repulsive interactions. We feel, we're asking what happens if there is no cutoff. If these are infinitely ranged repulsive interactions. So for example, a magnetic dipole's interaction. and these can be very long-ranged. And he's working on theory. He's also doing experiments in the lab and he's doing promising. - I don't know if you're familiar with this magazine that's called Knowable. There's a lot of summaries of annual reviews, but I will send you this link and I will also put it in the show notes for this podcast. They have an interesting, it's like a comic, which is a neat way to explain science, but it's a comic called Bustling Through the Physics of Crowds. And the subhead is using tools from fields like fluid dynamics to better understand how groups of people move can improve flow and make large gathering safer. But it made me think of what you had just said, like there might be some interesting applications here in terms of managing crowds in certain situations. - Yeah, exactly. Is there anything you'd like to add before we move on to the high school science portion? - I just wanted to make sure that it's clear that we're not crediting ourselves too much. People think about crowds many times as some form of material. Sometimes it's in a fluid. There is, just as an example, there is this really nice work from 20, 23, so almost years ago. - Maybe from Toronto. - Yes, so there are actually a few works from 2022, by about the law about marathon runners, big marathons, where the image that Chicago marathon and saw how people flow like food and model them as a fluid. There is another nice, really nice paper from 20, 23, in science by Polish group button, but Masiek, probably correctly, where they sort of show something that, when you walk in a very busy street, or a very busy region, imagine times square, who are going in all directions, there is a tendency to form lanes. - Ooh, yeah. - This has been seen in many places, not just in pedestrians, and this work sort of said, okay, let's think about people as a fluid. And then they do some hard math. Then they show that lanes is the stable flow that you should expect. - Oh, that's so neat. - So we, you know, what I feel like, in a very modest way, what Nominat just did was to add another piece of way of thinking about crowds, hopefully, to better understand and make our urban, environment more fluid. - Yeah, that's funny. - Or contact. (laughing) - Right, I'm having a flashback. - Unless they're falsies, yeah. - I had like a last minute trip this summer, I took my daughter to see Taylor Swift in Hamburg. - Oh wow. - But leaving the concert was the situation of like, if someone could look down on this crowd right now, I bet there would be all kinds of interesting takeaways. I have never been in such a large crowd, you know, all kind of like, going through ever narrower tunnels to finally get to a subway. But it was interesting. And also, you know, I wasn't thrilled either. I was like, I was constantly looking for like a way to get out of the crowd if, you know, if we needed to. But yeah, it's interesting. - Yeah, I love how it's really across scales from the molecular scales, all to the human scale. You can use the same equation. - Yeah. And I would imagine, you know, this would have all kinds of implications for medicine, like when you're administering a drug, like where does it go? Like how many of the targets does it actually get to? You know, I don't know, but that comes to mind as something that would be influenced by this as well. - Yeah, a new material. If you wanna design a printer that prints more effectively, for example. - Yeah, that's neat. So we're going to pivot to the high school science portion of the episode, you know, the specifics of curriculum and individual lessons in science education vary from place to place worldwide. But I think it's safe to assume that most high school students are learning about diffusion at some point in the US, where I'm most familiar with the curriculum. This often happens in biology. When students are learning about the cell and how things are getting in and out of the cell, you know, diffusion is the simplest way we address that. So I would love to, you know, just ask you, and I, we've kind of covered this, but just to kind of go over it one more time, how could this work broaden or change how students are thinking about diffusion? Sort of, you know, what could you kind of like add on when we're teaching kids about diffusion that sort of takes into account your work? - I think the generality. So our work looks at a specific case where you have this strongly interacting repulsive particles. And it covers pretty general aspects of interactions, but, you know, diffusion is even sort of more general than that. And you can think, and I think this is sort of the point is the generality is how you can say, okay, so diffusion can be for oxygen molecules getting into our blood shrinks or into cells, but it can also be like, okay, let's put a bunch of pupils in the yard, not here, and then just let them walk around randomly and see how they spread. You can measure it. Our guard is going to be diffusive. - When you think of diffusion, it doesn't have to be these really basic concepts. It can be more complex interactions between particles even. And you can add other forms of interaction, vonderols, electrostatic, any type of repulsion, and it's still going to be diffusive. It's going to be a bit different, but there's still going to be characteristics that are related to diffusion. And I guess there's one thing that we didn't say about diffusion, which, when I say that there are similarities, we didn't say what is similar, really. - Oh, right. - And what is similar is that, actually, this diffusion behaves in what is called self-similar behavior. And repulsive particles behave in a self-similar fashion as well. And self-similar in order to explain what that is. It's a bit like, if you think of babushka, these Russian dolls, and you have one inside another, and they all look exactly the same, except they're at a different scale. So if I take the small babushka, and I multiply it by two, I get the larger one, and I multiply it again by two, and I get the even larger one, diffusion is like that. If you take a snap, if I think of my drop of red color, in a certain, I take a snapshot at some point in time, and I take a snapshot at some later point in time, and I just scale everything, it's going to look exactly the same. I can overlap the two pictures. - Yeah. - This is for regular diffusion, but it's also true for repulsive particles. You take a snapshot of the drop, and you take a snapshot at a different time, and you can scale everything. - Oh, that's neat. - So it looks exactly the same. - That's very interesting. That's conserved in these two systems. - Right, right. - Well, now I'm going to ask you the two questions I ask every guest. So the first question is, can you share a memory from high school science, something that impacted you and stays with you today? - For me, this is a kind of a small story, but Anna got really, but it stays with me, and I feel like it really shaped the way I'm thinking as a scientist, and this is going back to my chemistry teacher. And she, I love just love how she taught us, and that is, I know, well, before when I was in elementary school, we would have a science class, and the teacher would show a demonstration to the entire class. And this high school chemistry teacher, she wouldn't show demonstrations. She would give us a question, and she would have us try to answer the question ourselves in the lab. And the question, one of the examples, which I remember, is popcorn, and she asked, why does popcorn pop? And that sparked a month of trying to think of it, and trying to design experiments, and then going back and seeing the do-de-experiments answer our hypothesis or not. And that's exactly how I do research, and I think every scientist, and that's what made me love. Chemistry and later physics as well, and wanna do that every day. - Great story. Going back to my high school physics chemistry teacher, Guggo, one thing that I remember him consistently doing, and it's easy to overlook us to appreciate it directly as a student, but in retrospect, it's pretty deep. He said, whenever I would ask him a question, and I was not the easiest student, he would sometimes say, who have to think about it and he will come back the next day or next lesson with a very, you know, structured answer to explain something that maybe he was not familiar with in the past. Now, my new year, he has, I was fortunate to have a beach week in chemistry for a high school student, so there was a lot and all the later I appreciated it. And he appreciated that he doesn't know everything and he can come back there later day and answer this. That's great. I know, I wish more people would feel comfortable saying, I'm not sure, you know, let me think about that or let me research it. And Naomi, I love your story. I mean, I worry sometimes that, I mean, at least here in the US, like we're so focused on like getting through the material and like, you know, getting to the test that we don't let students play around with something or try something and maybe fail at it and try again. But you're right. I mean, that is what being a scientist is. And I also think that it's important to get comfortable with feeling frustrated, you know, I tried this and it didn't work. Okay. Now, you know, that's that's part, as you say, like a really important part of the process. So now I would like to ask what advice do you have for high school students today who are interested in studying science? I think, well, I had a great chemistry teacher. So I wanted to be like her, but I had a terrible physics teacher. And that's why I didn't choose physics to begin with. But so I guess my advice is even, I mean, she made me think that I'm bad at physics. But I guess even if you're bad at something, that it doesn't, but you like it. If you're interested in it, it's worth to pursue. And sometimes you're not really bad. You just have a bad teacher, for example. So go ahead and and ask questions about the things around you and being positive. And if you think, if you're interested in it, do it, even if you think you're going to be bad at the beginning, but it's going to improve later on. I would second that. I would say from my personal experience, it was sort of spontaneous in high school, where I had a good teacher and then I got more attracted to that theme, but then in college and later also in grad school, I realized that the courses I should take are not always the courses that their title is the most interesting to me at the moment. But it's the courses that I know there are great teachers. I studied in New York University. There were some excellent courses offered by Quran, the Math Department, where by the way, Mike Shelley is, also took one of his courses. But I was doing a PhD in physics, so I was in a different department. And not all the courses were relevant to my research. I learned that we were very tangential to it. But once I had these excellent teachers, I was able, I'm still using ideas from these courses today. So yeah, just like as professor of physics, Naomi Alpinheimer, who says that she felt like she's done physics, said, take the teachers. Yeah, it's amazing how important teachers are to yeah, I know. And it's I think sometimes in efforts to improve education, we want to like manualize everything to like take the element of the wild card of who the teacher is out, but like that's like everything, you know, as you say, it's so important. And even taking classes, yeah, that might not be exactly and what you think you might learn. But if the teacher is amazing, you're going to learn from them. Yeah. You know, and you can apply that. Well, thank you very much for talking on this podcast this morning about, well, morning for me afternoon for you, about your work and your journeys as scientists. So great. Thank you. Thank you so much. Thank you so much. Thanks again. Thanks so much. That was Naomi Alpinheimer and Matan Ja Benzayan talking with us about a new look at diffusion. They looked at the diffusion of particles that repel each other. And as we discussed, that's actually a more common scenario, both at the microscopic and macroscopic scales. Our next episode will feature Pat Brown, a cancer scientist and doctor who's going to talk to us about leukemia and advances in treatment. Thank you for tuning in to today's episode of Science Fair. Please rate and review the episode on the podcast app of your choice. See you next time.