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Hosted by Lester Nare and Krishna Choudhary , this rundown episode covers four new science stories at a high level: a huge new 3D ant imaging database built with synchrotron X-ray microtomography, a lunar agriculture experiment that grew chickpeas in simulated moon soil using fungi and worm waste, AI-assisted discovery of strange objects in the Hubble archive, and a new programmatic roadmap for room-temperature superconductivity. There is also another round of Are You Smarter Than a Scientist? in the middle. Summary Particle accelerators meet biodiversity — researchers built a massive…

Transcript

Read the transcript · about 6,050 words, follows along as you listen

They identified 1300 objects that have an odd appearance, right? And more than 800 of these objects have never been identified in scientific literature before. The secret to farming on the moon is worm poop and fungus. That's quite exactly what NASA put in the brochure, However. Could be the future of a permanent lunar presence. Now that could transform our society more than I think any other technology that I can really think of. Hello Internet.

This is your captain speaking, Lester Naray, joined as always by my Co host and our resident PhD, Krishna Chowdhury. This is a rundown episode. We'll be covering four stories at a high level. If you're interested in our deep dives, check out the previous episodes from this week. We may even, in fact, have another round of Are You Smarter Than a Scientist? So in this week's rundown, first of the four stories, 3D scanning of ants, Thousands of ants with a particle accelerator, published in Nature Methods.

Fascinating story. We're following that up with growing chickpeas on the moon with fungus and worm poop, the good stuff. Scientific Reports brings us that one. We will follow that up with some discoveries from the Hubble archive and that Hubble archive discovery of cosmic hidden anomalies. But we used AI to discover them. That was an astronomy and astrophysics. And we will end with an LK 99 throwback about the state of room temperature superconductivity where we may have a road map that was published in PNAS. We are going to learn about the science from the ground up today because this is from first principles. So for our first story, we are going to be talking about ants like the movie, the Pixar movie 3D scanning of thousands of ants with a particle accelerator team led by researchers at the Okinawa Institute of Science and Technology used a psychotron particle accelerator to create detailed 3D micro CT scans. I've gotten a couple CT scans, but not a micro one of a 2921 hundred Ant farm colony with nearly 800 species. And this was published in Nature Methods because the method is what is interesting here.

Yes. So for the longest time, if we want to find if we want to make like a 3D version of these really tiny insects and we want to do it to super, super high resolution, you use something called a micro CT scanner. This thing is going to send out X-rays and then those X-rays are going to scan and allow the scientists to examine like the physical structure, the stuff inside without actually like destroying the specimen. Each of these scans takes about 10 hours for a single specimen. So it's not very high throughput, right? And we want to be able to do a lot in parallel. And so that's what these researchers did. They employed the Carlsruhe Institute of Technology, which has a giant synchrotron particle accelerator, X-ray imaging. It's also got robotics.

It's also got artificial intelligence and what they're able to do is generate interactive digital reconstructions of 800 Ant species. It's pretty incredible if we, if we look at the the headline right and if we look at this animation, look at the detail that we're getting this Ant. This is this is crazy. Yeah, this is that Micron resolution of an Ant, right? And this is rendered using AI because a lot of times we get little slices when we do ACT scan, we get like little slices or like different sort of configurations of what the Ant is in depending on the specimen. But you can use AI now to reconstruct that 3D version of the Ant.

It's, it's really quite amazing. With that setup. They've scanned 2000 species, sorry, 2000 specimens in a single week, 800 different Ant species. The effort is called Ant Scan. They've got a free public website calledantscan.info and it's kind of like a Google Maps for Ant anatomy. The data is also synchronized with genome sequencing projects, so you've got 585 of those scans linking to the genomic data of about 186 different species. I think it's a really cool application of particle physics and like particle accelerators that we don't really think about. It reminds me of a very old story that we did on this podcast about the T Rex bone.

Yeah, Remember that? Yeah. Where it was like they found blood clotting in AT Rex bone and showed that, you know, the T Rex was capable of rapidly healing its bones after some kind of, you know, I think it was a fight with another T Rex, probably. But it's just it, it's a really cool application of particle accelerators that doesn't have anything to do with fundamental physics. It's just leveraging it as a tool to do very high resolution 3D imaging of biological specimens. Which if you asked me before I, you know, we did the original T Rex story, which my reaction was like, this is unbelievable because we're sort of repurposing this very expensive large scale device to become multi purpose beyond its sort of initial mission or context.

Yeah, and the scans are like so detailed that they can be used for further training of machine learning systems to recognize ants in the field. For example, you know, I'm out in the field, I get some specimen. Now I have a pre trained network, a machine learning model that is trained using this really high fidelity, really high resolution data and it can immediately tell me what kind of species it is or at least what species it is most related to if it's a new species, right? It's also something that can be used for like if Disney or somebody wants to make what is it called the visual effects, like would like a giant Ant.

You know, remember that that one movie where like it's like honey, I Shrunk the kids. You want to make like a, you know, a giant Ant visual effect for that. Now you can be really, really biologically accurate. So if you want to be as precise as Interstellar was in their replication of the physics of a black hole and the amount of science that went into creating. So now particle accelerators can give us animated 3D ants that are giant at very high fidelity, yeah. I.

It kind of is a little bit the the example you just brought up sounds a little bit like a Pokedex. You can tell if it's a Caterpie yeah or AB drill or a weedle yeah or any of these butter free. I'm trying to flex my Pokémon knowledge I. Don't know any of I just know Pikachu and Charizard. Charizard Classic. Classic. Always interesting to see the intersection of different disciplines. Yeah. And how again past work can inform new work. And so this was again a method story, fascinating and published in Nature Methods. We have this coming out of the Okinawa Institute of Science and Technology and the I'll let you, you had the good pronunciation on the second institute.

Oh yeah, that's the Karlsruhe Institute of Karlsruhe Technology and Okinawa Institute of Technology of Science and Technology. OYST is what we used to call it. I actually did a summer workshop there for like computational neuroscience. It's an amazing, like place. It's on, it's on a hill in Okinawa, in the middle of the Pacific Ocean. Beautiful, beautiful spot. Lot of bugs, I have to say, a lot of bugs, a lot of bugs. But you know, it's a tropical island.

And you know what does not have bugs? This podcast. And if you've been listening or watching, or if you're a new listener or a new watcher, we're grateful for you joining us today to learn more about the universe around us. A like a comment, a share, a follow on any of the platforms helps us reach more curious people and helps us continue to do this podcast and deliver the best science podcast on the planet in our opinion, every week, day in and day out. And we're going to now transition into our second story, which is about growing chickpeas on the moon. Now we are from California, but this story comes out of Texas. Not only just out of Texas, but from researchers at UT at Austin and Texas A&M published in Scientific Reports where they demonstrated that they can successfully grow and produce seeds in soil that is very similar to the soil that we know exists on the moon.

And if we ever want to expand the human experience beyond our local Gaia on planet Earth, being able to do agriculture in space is valuable. And it seems like we may have an interesting finding in this recent paper. Yes, trying to establish a permanent presence on the moon will require probably, you know, doing some form of agriculture there. Light is pretty easy to come by, but soil is pretty hard to come by because moon regolith, which is the name for lunar soil, is very, very different from Earth soil. Earth soil has an entire ecosystem inside, right? There's bacteria, there's fungi, there's all sorts of creatures living in there.

And so soil is not just like a bunch of like, you know, small particles of rock, which effectively is what the in, you know, the a biological part of soil is. You need a lot of these helper agricultural necessities to actually create an environment to grow crops in The Martian with Matt Damon, right? He grew potatoes, I think, but he supplemented it with his own poop, right? This is trying to get that dream a reality. Without the poop or. Without the poop.

In this case, it's not human poop. They're actually going for worm poop. OK, So what they're saying is we can take lunar regolith, which is this super, you know, kind of dry dead substance. We can supplement that with both fungi, which are going to help the plants, and also poop from worms where the worms eat like sort of biological matter and trash and other kinds of stuff that comes out of the human presence there, right? Because we're going to have a lot of like biological waste.

The worms can subsist on that, create organic material. We supplement that organic material into dead lunar regolith, and then we grow chickpeas. And it actually worked. That's incredible. Yeah. And one, one thing that they actually did test was without the fungus, the plants did not produce any seeds. So it's really important that we have the fungus and we have this biodegradable waste that comes from the worms. So the part of the idea here is like, we know we can't. We can't grow in a completely dead substrate. We do need some biological ingredients mixed in to make the recipe work.

Yeah, and they simulated the lunar regolith because obviously you can't like get the little bit of lunar soil that NASA brought back from its space missions. And now other people have also brought back with their non human space missions there. But I mean, lunar soil is very hard to come by. OK, I don't think you're going to convince anyone. Hey I want to grow chickpeas. In your lunar soil. Right. That's going to completely contaminate lunar soil.

So they simulated lunar soil, and then they made sure that that sort of composition closely reflected the stuff that actually came back. And then they, you know, did this experiment with creating better soil with worm, compost and fungi. The secret to farming on the moon is worm, poop and fungus. Yes, quite exactly what NASA put in the brochure. However, could be the future of a permanent lunar presence in better understanding how we can sustain, particularly on the ability to have a food supply when we might not be able to send Starship rockets or whatever the defense prime rocket of the day is that can carry humans to the lunar surface.

Yeah. And one thing I do want to mention is that we don't quite know whether these grown chickpeas are safe to eat. We just know that they've been grown and they look like chickpeas, right, in every sense of the word. So there's still more testing that needs to be done to make sure that these plants don't absorb like harmful metals that are in the soil on the lunar surface, and they actually provide the nutrients that the astronauts would need. So there's still more to the story, but it's a great first step.

We will one day traverse the stars and one of the key aspects to us being able to do so is being able to survive, and food is a key aspect of that, an early first step. Are you going to be the first to try to test the lunar chickpeas? I won't be the first. I'll tell you that you sound like Elon. He's not going to be the first of Mars either. Now I'm very excited to transition to one of my favorite parts of the show. Ladies and gentlemen, welcome back to another episode of Are You Smarter Than a Scientist? And the quizzical look on Krishna's face is how he's going to attack this question on this week's rendition. Name the 10 deadliest animals to humans as you know. Are you smarter than a scientist? We have a question.

We have 10 answers. We have three strikes you at home? You might have some ideas in your head. Our resident pH DS has some ideas in his head. And we are going to see how many of the 10 deadliest animals to humans. Interesting. That Krishna can name in this round the floor is yours, good Sir. Man, I don't think I'm going to do well on this, but let's give it a try. You know, the first thing I can think of is mosquitoes because of malaria. It is a very good guess. OK.

And mosquitoes is #1. OK, 750,000 per year. Wow, that's a lot. It's it's not a small amount and it's incredible that such a small thing can kill so many of us. Yeah, OK, let's do OK In that vein, let's do like insects that carry disease. So like what about like fleas, Fleas or ticks? Can you give me that? Fleas or ticks impacts dogs a lot, but it is not. It's not for us, OK? It is not on our. List. I'm already on a strike, all right. This is a tough one. This sounds good.

This is your purview. Definitely I hear about bears. No, I'm not going to say bears. Nobody interacts with bears. Sharks are probably also overblown. Overblown. I will note that some of them are classic. OK, they are. Let's go with bears. Well, that's not what I expect you to say. Unfortunately, no. No, it's not even. Oh my God, I'm going to be so bad at this guys. We're on strike two. I will note that Christian is a biophysicist. So this is. Yeah, this is zoology and like society. Oh my gosh, I'm doing so bad.

I'm already on 2 strikes. Some of them are classic. Maybe. Maybe I should not. Maybe I should not reference that because I don't want to throw you off. OK, OK. Can you give me a hint because I'm only on one. So let's, let's so, so many of us may be familiar with a gentleman who rest in peace was really impactful for us when we used to watch things about Stan Ray. Oh. Or manta ray. Not what I was expecting. You oh, I think you said Steve Irwin. I was talking about Steve Irwin.

Was it not a Stingray? But it was not a sting. Oh, it's a crocodile. All right, OK, just all right. Now let me just see how many I can get. Come on. I I got one bro. So crocodile is number 9 on the list. We will have one lifeline. Again, we're very new to this. All right, how about? So I'll give you one extra strike, which is against the rules, but we will have one Mulligan in this exception. So we have #1 mosquitoes #9 crocodile dials. This is this is a tough one.

Hippo. Do we have Hippo on the board #10 Hippo we? Have. I have one. We have a. Lifeline. We have a lifeline. Damn, dude, I'm like, like, I guess I'm so sequestered and like, you know, normal society, I have no idea. Sharks. Let's go with sharks. Sharks. Is that on the list? I don't have a four strikes, but I do have the sound effect and that is not correct. That is not correct, unfortunately. We have mosquitoes at 1, crocodiles at 9 and hippos at 10.

If you're at home, make your list now for what you think 2 through 8 is. I purposely threw you a cool curveball. I gave you 2 easy ones the 1st 2 episodes. So I had to had to curve #2 you could argue this was not going to be easy to get, but the answer is humans are there. Deadly ferals to humans. We know that very well here in the United states #3 was snakes. Why don't I guess? Yeah, duh. OK, very. That makes sense. Animals if you're a black mamba. RIP Kobe or others #4 It might not have been your corgi, really, but some dogs are are fourth. My fear of dogs as a child is now reignited. Is cats on here?

So unfortunately, cats are busy taking over the world intellectually. So they're not killing people. They're not killing people. They're smarter than that. Number 5, there was no way you were going to get this one free water snails, which was news to me. Freshwater snails. Wow, so. This is our number 5, number six in our order. Assassin bugs. I've never heard of that. So I just had. Assassin bugs. I got to look. This up later so we may do now an episode on Assassin bugs #7 The TT Fly.

Oh, I've heard of. That in the continent of Africa. And it's interesting some of my dad's research has been around this, this area, like the diseases, a big one. Another funny reference to a family story, which we'll talk about in a future episode about how someone ended up in the hospital for 21 days is scorpions. So our top 10 deadliest animals to humans, mosquitoes, humans, unironically snakes, dogs, freshwater snails at five. I think I said something else earlier by accident. 6 assassin bugs, CC flies, scorpions, crocodiles and hippos.

This was. This was a bad one, guys. This looked like Ferrari at F 1-2 weeks ago. This is not not a good start to the. Season. No. This looked like Aston Martin. What's the other one, Haas? Is Haas doing anything? Haas is doing OK. They've got a Ferrari engine. They're doing OK. Yeah, they're doing OK. OK, so it is simply, it is simply the Ferrari. This is something again we're trying to see. If you all enjoy, let us know in the comments how many you got. Are you smarter than scientists?

Probably on this one and. We'll bring you back. We'll bring it back for next week's rundown. If you want it more, you got to let us know you want it more. But in the meantime, before our next episode game show, we are going to go to our third story, which is about hidden cosmic anomalies in Hubble's archive discovered by AI researchers at the European Space Agency, ESA, published in Astronomy and Astrophysics and new AI system called Anomaly match, very on the nose that scanned approximately 99.6 million images cutouts from the entire legacy Hubble archive in two to three days, which is an unbelievably short period of time. And what did we find?

Yeah. So for background, machine learning and AI systems are very good at something called anomaly detection, right, which is something that is out of the ordinary just because of the way that they learn data and they learn the data space. There's ways that they can group similar items together in their latent space and they can figure out outliers. It's kind of AI mean if you think about like rudimentary clustering algorithms where you project your data into some kind of subspace.

And then in this case, let's say, oh, normal spiral galaxies are over here, normal Bard galaxies are over here. There's going to be little tiny sectors where you find extremely rare objects and machine learning algorithms can do this very, very efficiently. Now, what these scientists did from the ESA was apply that method to the Hubble legacy archive, which is over the past 35 years, the Hubble Space Telescope has just been taking image after image, right?

And one of the great things about the Hubble Space Telescope is all of their images are now public access. OK, if you're, you know, taking something today, then perhaps it's not public access. You've got like 6 months or something to like grind out as many papers as you can. But pretty soon it's going to go into the public archive. So all of these images were cut out into 100 million different little image cutouts that are measuring just a few dozen pixels on a side. And from those hundreds of millions, from those 100 million image cutouts, they identified 1300 objects that have an odd appearance, right?

And more than 800 of these objects have never been identified in scientific literature before. Most of these anomalies are just like galaxies going through mergers or interactions. And so they're going to look weird. But every once in a while there was some pretty cool stuff. So if we go to our next photo, you'll actually see some of the very cool stuff. So on the upper right hand side, you're you're seeing Galaxy mergers, but the lower 2 on the right, you can see one Galaxy that's kind of curving around another.

That's a weird gravitational lens. OK, where because of Einstein's relativity, the gravity from the foreground Galaxy is bending the light from a Galaxy that's behind it. And so these are very, very cool gravitational lenses of a background Galaxy that's sort of twisting its light around the foreground, which is in the front. We've also got really weird wing ring shaped galaxies like the one on the upper right. And these are all like completely new.

I think that's so. Cool and druke with the ring shaped Galaxy on the upper left. Sorry, yeah, the upper left. You're right. Got it, got it, got it. And I just think that's so cool that like machine learning algorithm can go through these within 2 1/2 days and figure out all these new galaxies that now we can maybe get our other telescopes to look at, right? Take a look, get some Spectra, figure out if maybe there's a supernova that's happening in one of these like lensed galaxies that we can spot at multiple different times because maybe the light is taking longer on one end than the other end. If it's multiply lensed, you can do all sorts of very, very cool science by looking at these new objects, right? It's again, one of these examples of a news story about AI helping out scientists that again, makes me very hopeful in all of the drab that we're getting about how AI is going to end the world.

I mean, I think we are very much in danger of that as well, don't get me wrong. But there's also hope. And I think if it's in the right hands, tools like artificial intelligence and the anomaly detection that comes with it can be very fruitful for the pursuit of fundamental science. That is always the question is who controls the button. But the anomaly match I'm sure. I mean part of the verci. Reuben already has this built into its very much architecture and. That's going to generate like unprecedented knowledge of data. So an anomaly detector is going to be very crucial for analyzing that data just to begin with.

And look at the fact that we're still discovering new stuff today. Yeah, for some for this archive that's, you know, over, you know, had that has been gathering for over 30 years. Yeah, and the Hubble only looked at a small patch of the sky right in, in all of its 35 years of history. It didn't like tile the entire night sky like the Vera Rubin will. So it's very, very exciting time and astronomy. That means there's a chance I will see the. Yeah, one day. One day we haven't looked.

This is the analogy I always bring up is we're looking out our front window at a house and we're saying there's no one in the street. But we haven't looked out the backyard. We haven't hopped over the fence. We haven't driven around the neighborhood. But we're just looking out the front windows like, oh, we're all alone here. And it might be it might be there might be some roadblocks, right? Maybe they're doing construction on the interstellar highway. I kid.

Maybe maybe our last story, one of my favorite almost things that happened that did not happen is about room temperature, superconductivity. And this is out of the Proceedings of the National Academies of Science from a combination of universities including MIT, Harvard, Columbia, University of Houston, Carnegie Institution, Carnegie Institution, Graz University of Technology and Intellectual Ventures. And this was a programmatic paper that lays out a road map for achieving one of the pinnacles of, I guess, would you say engineering and science, the intersection of the two, which is the idea of this creating a room temperature superconductor? That's right.

This is not a, this is not a proper science article that we usually cover. This is in fact a perspective article. So a lot of times scientists will get together and they will write effectively an opinion piece about where the field should be looking at and how the field should be structured for future advancement. And this is one of those, OK. It's in the Proceedings of the National Academy of Sciences. It's a strategy paper that assesses the current state of research for room temperature superconductors and then sets out future directions.

So superconductors are these materials that have zero electrical resistance, not negligible, not next to 0, but because of fundamental quantum mechanics it is literally 0. The resistance of these electrons moving through this material is literally 0 because of some very fundamental, very cool quantum mechanics that is going on now that could transform our society more than I think any other technology that I can really think of. A very base example would be if we could have room temperature superconductors or high temperature superconductors, we can transfer electricity and power with almost no dissipation, right?

The problem with modern day superconductors is they either require extremely low temperatures, so even colder than liquid nitrogen sometimes, or extremely high pressures. If you want to get to high temperature, like room temperature, you got to like stick it inside a diamond anvil cell where like you're squishing stuff inside of a diamond. And then finally, you like make something that is superconducting, right? If we want industrial scale applications, we need something to be superconducting like on the table, right? You know?

And so there's a prediction challenge which comes from our ability to predict new superconductors. Now that is advanced dramatically because we figured out a lot of the material science and the fundamentals of how to, you know, simulate these things in a computer. Now, what this paper is proposing is a shifting of focus towards like thermodynamics and synthesis modeling, because a lot of times when we try to predict new superconductors in our computer programs, those can't be synthesized through the normal processes. It's like you've given me like the recipe but I have no way of cooking this thing.

You've shown me that unobtainium is a thing, but you don't have the root ingredients to create unobtainium. It's like it's. Like, what are we doing right? And then there's an engineering challenge because we've got all these different knobs that we can turn in our lab. Things like pressure, things like the nanostructure of light. Let me just say that again. So there's also an engineering challenge, right? Because we have these various knobs that we can turn in our lab, things like pressure, things like the nanostructure of the material, light lasers, just like pile a bunch of lasers on it, right, to control that superconductivity. But our ability to predict how each of these knobs effects that material is pretty limited, right? So what this perspective paper is doing is saying, you know, there's actually no physical law theoretically that is preventing room temperature superconductivity. No one's come out with a like this is impossible paper, right? So it's definitely there.

And superconductivity, superconductivity has been observed in so many different materials in so many different conditions. And it's almost like a generic property of materials at some point. And if you if you lower the temperature down, if you increase the pressure enough, things are going to become superconducting. So the idea is we want to be able to now gear our research towards creating something as a community, right, Rather than just going off in all these different tangents trying to do our own thing.

Let's like have like a sort of human genome project style moment. Right. Where all of these different researchers from around the world sort of get together, We plan out how are we going to get there, right? And the first task is always to improve computer aided models. So not just like predict random stuff that we can't cook, but maybe things that we can, things where the recipe actually makes sense. We've got the tools to do it and it's there's a light at the end of the tunnel. In in software, there is this phrase or this rallying cry of it's time to build.

Yeah, it sounds like in the Super conductor community, the rally cry is it's time to cook. Exactly, Yeah. It's time to cook. Exactly. And and, and the other thing that they really highlight in this strategy paper is that AI is here and we need to night now start leveraging that in the way the alpha fold kind of leveraged AI to solve protein folding, which for the longest time it was like, oh, this is an impossible problem, right? There's two to the 300 different configurations.

There's more atoms in the universe than they are. There's more atoms in the universe than there are structures that a protein can take. And so it's an impossible problem. Well, alpha fold is pretty good at like 90% of proteins now, right? So we need to start leveraging these technologies to our advantage, right? Right. And this dovetails with, I mean, it's we don't look to always cover AI in our stories on the pod. No, it is just a factual reality that so many labs and researchers, especially things that are breaking through, happen to have a companion AI component, and it's just kind of the way that it is.

Yeah, yeah. Especially for use cases where it's like protein folding or where you just want something that can go through a like almost like there's a brute force kind of pathway where it's just like throw compute at it. You'll be able to get down to a more narrow band of options than we otherwise would be able to without being able to just throw compute with you at the problem set. And that's kind of like a very level 1 implementation. And there's also level 234 and five.

But superconductors are going to totally change the face of humanity on this planet if we can get to a way to build them in a way that's scalable in terms of from, from a manufacturing capacity, yeah. Exactly. We had we had this whole hype cycle with LK 99 where everyone was doing the think pieces of what it's going to mean for everything. That one, can I just say I, I looked at that archive paper and I was like, there's no way. Like we we we should do like a joke episode on it sometime of just like how to recognize nonsense.

How to sniff the Yes, a bunch of four, again, really fun papers today. You can kind of see how the difference between our deep dive episodes where we really breakdown the fundamentals. So it's not just sometimes in the rundowns, it can be like, well, this is just what they're saying. This is why we have the deep dives to make sure we can understand the real fundamentals that build up the knowledge base to be able to have confidence in making these conclusions from these studies.

So we touched on the 3D scanning for ants, incredible use of a particle accelerator for 3D model generation. I mean, I might want to use that for my 3D gaming. So how, how, how, how unnecessary it is to use a particle accelerator to do like 3D video game. Yeah, you have a mini particle accelerator in the back. It's helping us model this. We follow that up with growing chickpeas on the moon. If you could go to the moon and know you're coming back and it's fine and it's fun.

It's like as safe as air travel. As safe as air travel and you don't have to pay. Yeah, I'd probably do it. Yeah, if it's as safe as air travel, yes, I think I would. Do it. I would go. Dude, the earth is going to be the size of the moon. I'm so excited. You know, imagine looking at the Earth and it's just like, oh, I can like cover it up with my. Hey Jeff, if you want to do another launch, but instead of another identity based cohort, you want to do people of color where we are. Very.

Yeah, yeah, I'll, I'll be your token, you know. Whatever for the marketing, we'll go. But like there needs to be like 100 launches before because I need we'll. Start with orbits. We'll just, we'll just do a little little small orbit. We're being a little facetious. Although he is a Princetonian, so that's part of the connection. We hit the cosmic anomalies. I love these telescopes, both ground based and space based and the whole ecosystem around them.

It's always fascinating. The data continues to give us value and room temperature superconductivity, which I was going to ask you a question about. Can you explain resistance? But that's that's for a deep dive episode. So if you want to know more about superconductors, let us know in the comments because I want to know more. You could do a deep dive on the history of superconductivity. It's a fascinating tale. BCS theory, it's what it's called from the University of Illinois Urbana Champaign. Very proud of their work there.

They have a little plaque in the physics department like this is where BCS theory was. I thought you were talking about college football for a second, but you were not talking about the old BCS bowl or whatever it is. I clearly don't watch college football. I am your host Lester Nari, joined as always by my Co host and our resident PhD Chowdary. As you can tell, we are having a fun time on this pod. We really appreciate you. We will see you all next week.

Transcript supplied by the publisher with the episode.

From First Principles

by Krishna Choudhary and Lester Nare · English · Tech & Science

From First Principles is a fast, funny, and rigorous breakdown of the biggest science stories of the week, hosted by Lester Nare and physicist Krishna Choudhary, PhD. We go past headlines into the actual mechanics: what happened, why it matters, and what everyone’s missing. Expect physics, space, A

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    How Scientists Actually Study Dark Matter (EP 42)

    Hosted by Lester Nare , this episode features astrophysicist Dan Gilman for a deep conversation on one of the biggest open questions in modern physics: what dark matter actually is . Starting from first principles, Lester and Dan walk through why the evidence for dark matter is now so strong, how strong gravitational lensing works, why tiny distortions in lensed light can reveal invisible clumps of matter, and how the next generation of surveys may transform the field. Krishna is out on family leave for this one, but the conversation stays fully in the From First Principles lane: grounded,…

  3. S2 · E41 · 13 May 2026 · 37 min

    Dr. John Mulchaey on Carnegie Science and the Future of Astronomy (EP 41)

    Hosted by Lester Nare and Krishna Choudhary , this interview features John Mulchaey , the 12th President of Carnegie Science and former Director of the Carnegie Observatories. The conversation starts with his early work on galaxy groups and dark matter, then expands into how Carnegie works as a scientific institution, what the Giant Magellan Telescope could unlock for exoplanets and astronomy, how science funding actually works, and why eclipse chasing is still one of the most magical experiences in science. Summary Galaxy groups and dark matter — Mulchaey explains why small galaxy groups…

  4. S2 · E39 · 21 Apr 2026 · 1 hr 37 min

    The Prometheus Constellation: Dramaturgical and Scientific Analysis of the Physicists in Oppenheimer (EP 39)

    Hosted by Lester Nare and Krishna Choudhary , this special episode ranks the 26 scientists shown in Christopher Nolan’s Oppenheimer by one standard only: their contribution to fundamental science. Starting with the Manhattan Project figures near the bottom and working up through the giants of quantum mechanics, relativity, nuclear physics, and logic, the episode turns a movie cast list into a surprisingly deep walk through the history of modern physics. Summary A ranking framework that actually means something — this list is based on scientific achievement, not movie prominence, clout, or…

  5. S2 · E38 · 15 Apr 2026 · 58 min

    Harder Than Diamond? The New Hexagonal Diamond Breakthrough (EP 38)

    Hosted by Lester Nare and Krishna Choudhary , this episode is a deep dive into one of the strangest and most hard-fought materials science stories in decades: the claim that researchers have finally synthesized bulk hexagonal diamond, also known as lonsdaleite. They break down why this material matters, how it differs from ordinary cubic diamond, why scientists argued about its existence for more than 50 years, and what the new Nature paper actually did to convince skeptical reviewers. Summary Why hexagonal diamond matters — if real, it is a long-sought carbon phase that could be slightly…

  6. S2 · E37 · 9 Apr 2026 · 1 hr 26 min

    Artemis II: Deep Dive on the Moon Flyby, Earthset, and Reentry (EP 37)

    Hosted by Lester Nare and Krishna Choudhary , this episode is a full deep dive on Artemis II as the crew returns from humanity’s first crewed lunar flyby in more than 50 years. Lester and Krishna break down the mission photo by photo, from launch and translunar injection to Earthset, Earthrise, the in-space solar eclipse, the science of lunar observations, and the skip-entry reentry profile bringing Orion home. Summary Why Artemis II is historic, what the crew saw on the far side of the Moon, and why this mission matters for the long-term return to the lunar surface.Why NASA relied on the…

  7. E63 · 7 Oct 2026 · 1 hr 21 min

    Nobel Prize in Chemistry 2026 Explained: Mirror Molecules (EP 63)

    Why does life favor one molecular mirror image? The 2026 Nobel Prize in Chemistry honors Henri B. Kagan and Kenso Soai for nonlinear effects and autocatalysis in asymmetric organic synthesis. Lester Nare and Krishna Choudhary unpack the science from first principles: chirality, Pasteur's crystals, enantiomeric excess, and how a tiny imbalance can grow into an overwhelming preference for one molecular hand. We connect Kagan's catalyst discoveries and the Soai reaction to medicines, the origins of biological handedness, and the serious concerns around hypothetical mirror life. Plus: symmetry…

  8. S1 · E62 · 6 Oct 2026 · 1 hr 2 min

    Nobel Prize in Physics 2026 Explained: IceCube & Neutrinos (EP 62)

    Why build a telescope inside a billion tons of Antarctic ice? The 2026 Nobel Prize in Physics recognizes Francis Halzen's work on IceCube and the discovery of high-energy neutrinos from the cosmos. In Episode 62 of From First Principles, Lester Nare and Krishna Choudhary explain neutrinos from the ground up: why these elusive particles make powerful cosmic messengers, how faint flashes of Cherenkov light reveal their interactions, and why detecting them requires an observatory buried deep beneath the South Pole. We follow the path from beta decay and the first neutrino experiments to AMANDA,…

  9. S1 · E61 · 5 Oct 2026 · 1 hr 11 min

    Nobel Prize in Medicine 2026 Explained: Optogenetics (EP 61)

    How do you prove what a brain cell actually does? The 2026 Nobel Prize in Medicine celebrates a remarkable answer: give cells a light-sensitive protein, then switch their activity on or off with light. In Episode 61 of From First Principles, Lester Nare and Krishna Choudhary explain optogenetics from the ground up and trace the discoveries of Peter Hegemann, Georg Nagel and Karl Deisseroth. We follow the story from algae swimming toward light to channelrhodopsins, precisely controlled neurons, and experiments probing memory, reward and behavior. Then we explore heart-brain connections, early…

  10. S1 · E60 · 3 Oct 2026 · 41 min

    2026 Nobel Prize Predictions: Medicine, Physics & Chemistry (EP 60)

    Who could win the 2026 Nobel Prizes? From the science behind Ozempic to quantum interference and droplets inside living cells, Lester Nare and Krishna Choudhary make their picks for Medicine, Physics and Chemistry, and explain the discoveries behind them. In Episode 60 of From First Principles, we explore seven research areas with a case for Nobel recognition: GLP-1, optogenetics, optical coherence tomography, the Aharonov–Bohm effect, atomic force microscopy, biomolecular condensates and Buchwald–Hartwig coupling. We also discuss Michael Berry’s geometric phase and the awkward question of…

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