So good morning. My name is Will Chu. I'm a faculty member in the Material Science and Engineering Department here at Stanford. I'm also a member of the Precourt Institute for Energy. And it is a great pleasure and honor to moderate this next session on energy storage and conversion. It's really a great pleasure to introduce some of my colleagues in this area and learn about their research. So I was asked by GCEP to give a quick overview. This is really a difficult task because energy storage and conversion is such a vast topic. So it really presents a really difficult one to think about how to organize and tie the talks together. So I'll give you my best shot here. So I like to say that electrochemistry and electrochemical process is at the heart of carbon-neutral energy cycle. But my colleague and friend, Mike McGee, told me it's really not a good idea to think about electrochemistry as the heart because it's not just doing the job of circulating, say, your energy carrier.  So I think that's a really interesting way to think about the biological analog of electrochemistry. Needless to say, what is really important is that electrochemistry plays a role almost in all aspects of energy storage conversion. So what I have here, it is a quick slide over viewing some of the technology being pursued here at GCEP, looking at a carbon-neutral cycle that starts with the sun. We can also start with other forms of energy. This is just one example. What I wanna highlight here is there are four elements to a carbon-neutral energy cycle, starting with capture, storage, delivery, and utilization. And what we will hear about today from three speakers are electrochemistry and electrochemically active material for energy storage in the form of batteries, for the conversion of water to hydrogen and oxygen. So we can also view that as a storage process as you have a dispatchable fuel such as hydrogen. Then after all this fuel is made, you have to have some way to convert it back to electricity and that is the fuel cell aspect of the energy conversion cycle. So you'll learn about how critical materials are for enabling high efficiency, low-cost energy storage conversion in these three areas. So I thought it was fun to think about where things are at today. And I made these two plots, one for energy storage and the other one for electric catalysis. The left plot basically shows you the inevitable trend that we're trying to battle. If you look at energy storage such as batteries, on the y-axis you have reversibility, so this is the number of time you can recharge your battery and on x-axis you have energy density. It's almost always as you increase the energy density the reversibility falls. This is the battle that we're trying to find. What you will hear from Dhanan and Hanji are new material approaches to reverse the trend. So how do we increase the energy density and at the same time increase the reversibility or the rechargeability of the system? And this really underpins one of the greatest challenge with energy storage. In energy conversion for example in electric catalysis for dissociating water into hydrogen and oxygen with electricity you're also faced with a very similar challenge. The x-axis is the activity, so how good the catalyst is, and the y-axis is stability. So inevitably as you increase the activity of the material you decrease the stability. And what you'll hear from Hanji and Regi is developing material that goes other way. So can we increase the activity but while maintaining x-sensibility? So you'll see that in the y-axis this really concerns with the efficiency, scalability and cost of energy conversion and storage. So we can do a lot in the x-axis but y-axis is the key challenge here. We have Professor Hanji Dai from the Department of Chemistry here at Stanford. And Professor Dai is very well known for his pioneering work in carbon nanotubes but in recent years he has been expanding into the areas of nanomaterials for energy storage conversion. And over the past few years he has developed new material for battery, new material for electric catalysis, new material for water splitting, some of them under the support of GSAP. And today he's gonna take us through a journey looking at some of these materials and how nanostructuring can really lead to interesting new property in terms of the efficiency, stability and reversibility of these materials. Hanji, please. Okay, good morning. I'd like to thank Will for the great introduction and GSAP for the invitation. My group really came from materials chemistry and physics angle to study energy problems. So I'd like to tell you the work that's going on after we spend a lot of time working on carbon nanotubes and see how novel carbon nanomaterials can be useful to expanding from carbon to new kinds of materials and for electric catalysis and battery. So I'm sure this really doesn't need a lot of introduction, but I think everyone working this field is really to look at ways to harvest energy from nature, you know, using them in a very meaningful way to improve our society. So you really would like to have materials or devices that can harvest the solar energy so you can have very good solar cells, so you can have electricity from the sun, but quite often your energy can, you know, you cannot use all this energy or all this electricity so you would like to store them in that sense you would like to have very good batteries, okay, or you could think of ways of converting solar energy or wind, hydro that we have a lot of in nature into chemical energy, right, like make very clean hydrogen fuels. So everything here really comes down to materials, catalysis, okay, electrodes, how do you make things better, more stable at lower cost. So the two main areas in my group to approach these materials problems in renewable energy is really in two areas, one is make electrocatalysts that may be more active, more stable as Will was saying, but also at a very low cost, okay. And so that's one of the areas, but then we also would like to make batteries that may be a little different from what we had before. So people have been improving lithium-ion batteries over 30 years or so and making progress steadily, but very slowly. So can we actually have new kinds of batteries that runs with different materials, maybe at much lower cost, maybe will have less safety issues than lithium-ion batteries. So I'd like to share with you the story we had recently on making aluminum-ion battery, okay. All right, so the first topic is really about the splitting water into hydrogen and oxygen. And I think it's really something that most of us, if not all of us have done when we were kids. So we take water, put salt in it and put electrodes into it and try to apply a voltage and get hydrogen and oxygen gas out of these two electrodes. Now there are two reactions in this system. One is a hydrogen evolution, which is HER, okay, hydrogen evolution reaction. And the other one is oxygen evolution reaction. So both have to happen at the same time. And theoretically the voltage you would need is only 1.3, 1.23 volts. Okay, so it's relatively low energy to get this reaction to go, but in reality, you actually have a lot of a so-called over potential. So you really have to supply more voltage than necessary thermodynamically in order to get these gases to evolve. And simply because these are really multiple proton or electron processes, so there are lots of kinetic barrier you have to overcome, okay? So for these reactions, typically you would need a precious metal as the catalyst to get very high activity, okay, to lower the over potential. So for the hydrogen side is typically platinum being the best catalyst. And for the oxygen side is the uredium oxide as the best catalyst, okay? So in that case, you would be able to get a 1.5 to 1.6 volt to get these two reactions to happen simultaneously. And in industry these days for this water splitting basic solutions, in order to lower the cost, so typically the catalyst for HER is nickel, okay? Nickel is very abundant and low cost. And then the electrode for oxygen is stainless steel, which is also quite abundant and quite low cost, but the caveat is that activity is quite limited. So you wouldn't need high voltage like 1.8 or even 2.2, 2.3 volt in order to have large amounts of hydrogen and oxygen. So we would like to have cheap and scalable catalysts, okay, so that you can get water to split at very high energy efficiency. It doesn't make a lot of sense if you really had to use a lot of electricity to make hydrogen because it was a low efficiency. So my group over the past three years have identified a few materials that are pretty active for water splitting reactions. So this compound here is called a laryte double hydroxide, okay? This, by the way, has a series of compounds. The many LDHs are the different kinds of transition metals you can make, okay? People have been studying LDH for decades for a lot of scientific or engineering applications, but rarely people have tried using this class of material for electro catalysis. So a few years ago, we stumbled onto this material. So this really is like, if you're familiar with the graph, it's very similar to that, but it's an organic version of it. So you have these sheets of hydroxides that stack together and you can make very thin plates of them. And you see these octahedrons really just have nickel in the middle or some of them would have iron, three plus in the middle as dopants, okay? So this, by the way, was grown, this plates were grown on carbon nanotubes because we would like to have a network of carbon so that the material is very conducting. It give us even better activity. So they would look like this, okay? The nanotubes are swiggly and you have these very thin plates sitting on the tubes. And now if you look at the activity to produce oxygen, it's actually quite good. So the red curve here is the current associated with oxygen production. And you can see the voltage needed is actually lower than Euridium. Euridium is the gold standard for OER. It's more active than Euridium in basic solutions, this in KOH solution. And what's more important is it's also quite stable. So this is a constant current curve you can see with the LDH. It's actually, the voltage you need to produce oxygen is stable, okay? It doesn't decay. But for Euridium, it actually decays. So there's a stability problem for Euridium. So this is really quite active. It's not quite 1.23 volts, okay? You still have 200 millivolts, 250 millivolts over potential because it's a four electron process, so there's a lot of kinetic barrier. So this, after it's published two years ago, it's actually very quickly reproduced by many groups in the world, including prominent groups like Michael Gretzel, and they made LDH exactly like ours, and found that this material is good for oxygen evolution, but also it's quite active for hydrogen evolution. So you can actually use it on both sides to get hydrogen and oxygen out. And then also they were clever to put a very new solar cell, perovskite solar cell, to harvest the solar light and drive the hydrogen and oxygen production using the LDH electrodes, okay? Getting a very good efficiency of 12.3%. So what that means is that you take sunlight, 12.3% of it goes into producing hydrogen and oxygen, which is a fairly reasonable efficiency, okay? All right, now let's talk about the hydrogen side, okay? LDH is pretty good, but it can be better, okay? So we looked at this side and many people have looked at it over the years, and the best is platinum in basic solutions or acidic solutions. And the hydrogen electrode is actually very important in the industry. I don't know if you are aware of it. Other than using it for electrolysis, which is to split water, people actually have been using it for decades to make chlorine gas, sodium hydroxide, okay? These are very important chemicals. That's the so-called chloroacrylate industry. So there's industry around us for that. So one side you produce chlorine, the other side you need to produce hydrogen. And this is extremely energy consuming process, okay? It actually consumes about 8% of the electricity in the United States, 8% just for this single reaction. So you do have a need actually to lower the energy cost or the overall potential for hydrogen evolution. And so far, industry in nickel is the metal that's used for HER, okay, in basic solutions. So recently we found that you can actually do better than nickel, okay? If you make a material that contains nickel and nickel oxide, okay? So we identified a material that looks like this. It's basically look like nanoparticles, but it actually has a nickel oxide so that the green part is oxidized nickel, okay? It's like a shell on the outside. But then the middle of the red face is actually metallic nickel, okay? So you make these, what do we call hetero structure? You have nickel oxide attached to nickel metal, okay? So this material turns out to be very, very active for hydrogen evolution reaction. And we have done theoretical calculations on that with collaborators. What's happening, we believe, is actually when you do have hydrogen evolution, the hydrogen atom can actually attach to the metallic nickel side, okay? And then the OH groups, which are oxidizing species, they actually attach to the nickel oxide face. So that boundary of nickel and nickel oxide is what we believe is the really active site for producing hydrogen. And now with these two electrodes, one for hydrogen and one for oxygen, you can do this experiment, I guess, you did in high school, okay? And use two electrodes to produce gas bubbles. I'm not sure if the movie is playing, oops. Take a look here, is it playing? Okay, maybe there's some technical problems with that. Yeah, yeah, yeah, so you can see gas bubbles, right? Yeah, so this now is only driven by a single alkaline battery, okay? This is 1.5 volts, which is not easy to do if you do it at home, okay? Typically, you would need at least two alkaline batteries to get to about three volts. Then you will see a lot of gas bubbles. And this is good because you're using very low-cost catalyst, right? So you're using nickel, nickel oxide, and ion. Okay, so very active and very stable electrode. Material. And more recently, we went a step ahead and coupled it with a very good solar cell, get a mass-onized solar cell, okay? And we are able to achieve about 15% of efficiency, okay? So 15% of the sunlight is now going to producing hydrogen and oxygen. And what I wanna point out is really look at this red curve here is the two electrodes we made. And you can see we only need about 1.7 volts or so to produce 200 milliampere centimeter square current, okay? So that's a current that you really need in industry to make these gases, okay, 1.7 volts. If you don't use these catalysts and use conventional ones, nickel and stainless steel, and that's what you need, okay? You need about 2.4 volts. So that's a very large reduction in the voltage you need to produce the gases. So what's also important is that it's quite stable, okay? You can do this for 500 hours without much decay, okay? So we figured out ways to stabilize these catalyst. And we also are able to drive the water splitting using lamp light, okay? You can do this at night. You don't need solar light. So here's a video you can replace, try this again. Yeah, so that's the solar cell. We're using a lamp light to shine light onto it.  So you can make quite a bit of hydrogen oxygen using lamp light. So what that means is that at night, if you have a lot of wasted light or straight light, you can somehow collect and feed them to these electrolyzers and make hydrogen oxygen, okay? All right. So we also have other types of catalysts for electrocathases for oxygen reduction action, which is useful for fuel cells. I don't wanna go to a lot of details of this, but here's one, cobalt oxide on nanotube. It's actually can be more active than platinum in basic solutions, okay? So again, these are very simple materials, cheap materials that you can utilize to make a very active and stable catalyst. So let me switch gear to batteries very quickly here. One battery I wanna mention is zinc-air battery, which is the one that actually supported by GSAP. The reason we are working on zinc-air battery is because it has very high energy density. Because if you oxidize zinc, you can get a lot of energy out, okay? But the challenge is can you make it rechargeable and can you make it energy efficient? So there are a lot of issues with zinc-air battery, but what we worked on was the electrolysis side because we have good electro-catalyst, okay? So we use our electro-catalyst to increase the energy efficiency of rechargeable zinc-air battery. So this is our O-R-Catalyst cobalt oxide on carbon nanotubes. This is our LDH on nanotube for oxygen evolution. So you combine them and you use them for the zinc-air battery. You get pretty good performance, okay? So this is rechargeable zinc-air battery over many cycles and potentially this can really lead to very high efficiency, well, higher efficiency zinc-air rechargeable battery in the future using these electro-catalysts. And lastly, I wanna tell you this aluminum ion battery story. And so this was a project we studied about two years ago and we had very low expectation at the time, but now it looks like we can actually make a pretty good battery using very abundant and low-cost materials, okay? So now you can actually take aluminum foil that you can buy from Safeway, okay? And graphite powder you can buy from Alibaba or anywhere, you know, from your pencil lab, whatever. And then these two are the three electrodes but this electrolyte is actually a little special. It's made of salt, okay? There are these organic salt plus aluminum chloride. Remarkably, you mix them together, it makes a liquid, okay? Because there's so much entropic gain by mixing intimately. It actually lowers the melting point a lot. So it becomes a liquid at room temperature by mixing solids, which is fairly remarkable. But we didn't do this part. This is known for 30 years or 40 years, okay? Ionic liquids. And if you mix them in the right ratio, it's also known for many decades that you have these ions. You have aluminum chloride ions. So where is this recipe? It looks very simple, right? You can actually put them together and make a battery that works pretty reasonably well. So this side is essentially aluminum foil being oxidized. So this side going this way is oxidation of aluminum or it can be reduced, okay? You can reduce and make aluminum. And in fact, this reaction has been studied for decades for electroplating of aluminum metal. Okay, so electro deposition technique in ionic liquids. And people have known this and they have also looked for a electrode that can couple with aluminum to make a battery. That has also been going on for 30 years, 20 years, okay? So what is the best electrode you can use to combine with this reaction to make a battery? So that's been a very old problem. And three years ago, we actually found, we actually tried about 150 compounds on this side. Okay, try to combine with aluminum. But at the end, we accidentally found that if you mix graphite into some oxides and try to make a battery, you actually get some battery behavior and you get some capacity, okay? And then we figured out it's not the oxide that's giving the capacity is the graphite. So it's a very accidental result, okay? We add graphite because we want to have conductivity on this other electrode. So now we know what's going on. It's actually these anions, okay? That's actually intercalating in between the graphite. And when this anion goes in, this side has to be neutral so the carbon gets positively charged or gets oxidized, okay? And when anions come out, they get reduced. So it's a very classical redox reaction in electrochemistry, oxidation and reduction. And it's reversible. The intercalation here is reversible. Actually it's reversible for thousands and even 10 thousands of cycles, okay? We have data now on that. And the voltage between these two redox carpoids is about two volts, okay? It's not great, it's not as high as lithium ion batteries but it's two volts, okay? It's enough to drive the water splitting way we have in the lab. It's higher than alkaline battery. So that's the simple version of this battery and you can see these charging and discharging curves to look at the intercalation process. The clumbic efficiency is pretty good. We can get now to 99 some percent, okay? And cycle life is good. This is 200 cycles but we have much higher cycle life data now. And the catheter capacity was low, was about 60 some milliamp per gram and now it's up to about 100 milliamp per gram by engineering the right graphite material we're able to increase the energy density of this battery. And what's also interesting is that if you make the graphite side the porous, okay? Like in this case we have this porous graphitic whiskers in the electrode. You can charge and discharge very quickly, okay? So this is a very high current charging and we can charge and discharge in less than a minute, okay, to get this battery to go in the paper. The cycle life here is about 7,000 cycles but now we have much longer data. Okay, and then we also have done X-rays diffraction studies to look at the anions going into the graphite for intercalation. You can see that this spacing gets expanded or compressed and it's more or less reversible from the XRD. If you charge the graphite and burn it up, right? Oxidize the carbon, you end up with aluminum oxide. It turns into aluminum oxide foam. You can do XPS to look at the oxidation of the carbon so this is a pristine graphite when it's charged the anion. Graphite is oxidized, you see that shoulder that's the oxidation peak of carbon and when you discharge it it reverses. It's a fairly reversible redox reaction. All right, so this is the battery and I think some of the features I'd like to point out is that the following, it really uses the most abundant things on earth, right? Aluminum carbon, we really have them every day life. So this is very friendly and very cheap and aluminum is also safe. It doesn't combust as easily as lithium and the ionic liquid is a little special. It's the most expensive part of the battery right now but even with this ionic liquid we can lower the price to about $20 per kilogram okay, the companies that quoting us this price but we also have new data now in the lab we can actually use a new ionic liquids we haven't published that's really dirt cheap, okay? You can use something that's maybe a few bucks a kilogram to make this battery, okay? So everything becomes very, very low cost but it actually still have all those interesting features including safety, it doesn't really burn as you drill through, we have videos for that and it charges fast, you can charge in a minute and you can discharge in 10 minutes or half an hour if you want, okay? It cycles very well up to 10,000 cycles and this is the theoretical energy density, okay? 62 watt hour per kilogram including all these active species so energy density wise is not great, okay? It's not supposed to be a competitor of lithium ion batteries but I think it's actually a pretty reasonable one to think about replacing lead acid batteries which actually holds the largest market of battery because more market share than lithium ion battery so we hope that this can be useful in the future for great storage, you could also think of having them at home, okay? Because they're safe, they wouldn't catch fire as easily and you could think about heavy mechanical tools using such a battery, all right? So that's my talk and we are now actually having all those materials and I think it's actually quite meaningful now to talk to potential partners in the industry, maybe some in the audience here but we're already talking to some renewable energy companies who for instance have maybe windmills on the ocean because they have electricity and then there's a lot of water around them, okay? You can think of making hydrogen using the windmills, right? So we're talking to some of them. They can make hydrogen with very high efficiency and using water that's very abundant not necessarily fresh water, right? Fresh water we don't really have in California, right? But we have ocean around us and we also hope to have batteries like this to become really useful and I think working with people in the industry would make that happen, okay? So I'd like to thank the people in my group who did the work as well as the collaborators here as well as in other countries. Thank you very much. Thank you, Hanjee. I think we have time for one quick question. I would you prepare this to all batteries and we have a lot of storage. So that would be wonderful if you could do that. Yeah, I really think of the low cost that would be one of the major features of this battery. I don't really have all the cost analysis. It's such a new battery we're dealing with but all we are trying to do is really to see what are the alternative or optimized components would be in this battery. And we're very excited actually as of yesterday we had something that's much better and it looks like much cheaper for the electrolyte. Okay, Hanjee, thank you again very much. Okay, our next contribution to find out one of this session is from Professor Reggie Mitchell from the Department of Mechanical Engineering here at Stanford. So Professor Mitchell is an expert in combustion and over the past few years he has been combining combustion with electrochemistry essentially to do electro catalytic combustion in the form of a solid oxide fuel cell. This is a very interesting approach to achieve high efficiency energy conversion but also as he will show a way of sequestering carbon and producing pure oxygen. So in this talk he's gonna focus on how to deal with very dirty gases in solid oxide fuels. So Reggie, please. Thank you. As I acknowledge my talk is called cold generation of carbon free hydrogen and electricity from coal in a steam carbon fuel cell with carbon capture. Before getting started, I'd like to introduce the people who really got to work at least call their names. David Jonson and Brandon Yong Long are the ones who really do the work. These are my PhD students. Michael Stort is my postdoc. Turgut Gur is the person that I had to really call in to help me with this project because what I really learned is that fuel cells is not about the fuel, okay? I know about the fuel and this is me. So I hope you had an opportunity to see three posters that each of my students and postdocs have had. They will be focused on some of the stuff that I'm talking about today. What is the overall objective of this work? It actually addresses three energy production and storage with respect to coal. We wanna use efficient coal conversion and a specialized fuel cell, electrochemical hydrogen production from coal and CO mitigation of carbon capture without separation. Okay, believe it or not, this technology could result in a significant reduction in global CO2 emissions over the long term with continued use of coal our cheapest and most abundant fuel.  And then I'm gonna focus on what we really do. We have a problem with sulfur poisoning of our electrodes. So how do we attack it? Overcoming sulfur poisoning is the whole focus of this project. So we are going to have a little bit of sulfur solvent studies, some sulfur tolerant anode studies and then I'm going to end with some comprehensive modeling that we do of our coupled carbon steam fuel cell. Okay, way of motivation. Coal is our cheapest fuel by far and it's everywhere, it's most abundant. What I've shown here is that we have about 101 million tons of coal reserves worldwide and most of it is right here in the United States. I can't get this to work. Most of it is right here in the United States, okay? Followed by a lot of coal being in Russia, China, India and Australia. At our current, at our current, or at the 2004 production rates, we have about 250 years of coal right here in the United States. Worldwide coal-fired power plants account for about 30% of the global CO2 emissions. What does this mean? Environmentally benign use of coal as an energy resource requires that we capture the CO2 and sequester it, okay? Hydrogen, hydrogen is our most abundant element there is but where as most of it is almost all tied up in water. Hydrogen is an efficient energy carrier. It's environmentally clean when we burn it. More oxidizing in any kind of way. The product is H2O. It's suitable for large scale and energy storage. It's also suitable for distributed generation. And if it's generated from biomass, hydrogen, I mean, it's also a carbon neutral. Hydrogen is also a carbon neutral. So what's the big problem here, folks? Ooh, you know, I'm gonna stop doing that. What's the big problem? Hydrogen is not naturally available. We have to generate it some kind of way. And what we have put together is a way of generating carbon-free hydrogen that can be used in almost anybody's fuel cell. So that's what I'm gonna focus on. A little background. What have we done to date? Well, we've actually created a carbon air fuel cell. Okay, and this is how it works. I have a carbon bed here and I have a solid oxide fuel cell. So if I take air and it can dissociate, O2 can dissociate, give it up some electrons that can travel through and out of the circuit. But the O2 ions can diffuse across this YSC, get your stabilized O-conium, and when it gets to the other side, as long as I have any kind of fuel over there to eat it up, I can keep this concentration gradient going and I can keep a fuel cell working. Okay, this happens to be, this air-carbon fuel cell happens to be exothermic. It reacts spontaneously. We next worked on a steam-carbon fuel cell. One of my PhD students got the idea is why does the oxygen have to come from air? How come it can't come from steam? And so we tried it out and it worked. Instead of putting oxygen on this side of the cell, we actually have steam coming in and at the cathode, we actually strip off the oxygen. The oxygen ions travel across the YSC just like they did before when they get to this side, is consumed by the fuel again. And but this time we have hydrogen coming off, but it's hydrogen is produced from H2O and not from any hydrogen atoms in the carbon. So this is a clean carbon and since they're hydrogen and since there's separation between the carbon bed and the hydrogen, you can't possibly get any carbon into this hydrogen mix. This works too. The problem is that this one is endothermic. So as long as I keep it hot, it works. The air-carbon is exothermic, so as long as I start it, it's spontaneous so it'll continue to work. So what have we done here in our third year? In the third year of whatever. Ooh, can't press that. We've actually decided to couple these two ideas. Okay, one was endothermic, one was exothermic. So let's use the heat from the exothermic side to drive the endothermic side and that's just exactly what we have done. We've put together a coupled air-carbon, steam-carbon, fuel cell. And as you can see, these are the oxygen activities on the hydrogen side. It goes from about 0.5 volts down to zero and a 0.9 side. So both sides are downhill gradients. This couple cell is capable of spontaneously generating both hydrogen and electricity. Okay? So what's the problem? Like I said, we wanted to use coal as the source and coal has sulfur in it and sulfur poisons fuel cells. Coals have anywhere from 0.4 to about 1.4% sulfur in them and when gas is high, that ends up is anywhere from 500 to 10,000 parts per million of either H2S or COS in our system and our electrodes can't handle it. Nickel electrodes can't handle that, okay? So what do we wanna do? We need to try to figure out some new materials, some new electrode materials. Or can we lower the gas-phase concentration of sulfur-containing species to level that these electrodes can handle? So we actually do take a two-pronged approach by doing them both. Can we find sorbents that will lower the concentrations of the sulfur-containing species in the gas-phase to levels that electrodes can tolerate? And also, can we identify new electrode materials that can tolerate sulfur in the system? Okay? So let me start with the first part of it. Sulfur-solvent studies. Our goal, try to identify sorbents that could take the gas-phase concentrations down to less than five Pp parts per million. What do we do? We scan not only the literature, but we did our own thermodynamic analysis. We looked at almost every metal, metal oxides that we could find data for. Delta G data, looking for metals and metal oxides that would react with hydrogen sulfides in COS will form stable sulfide phases. Okay? And what we found of these things in green, the ones that are green, they are good sorbents. There's some light green stuff, are elements that are shaded that, you know, they are okay, but it depends upon the phase that I'm talking about. There are some elements that just won't work at all. And then there are a whole bunch of things that we didn't really look at because we knew they weren't going to work at all. But in general, alkaline metals are better than sulfur solvents than alkaline metals. As such, we selected calcium oxides, strontium oxides, and barium oxides to be the metals that we want to play with, first of all. See if we can't see what their capability is to reduce H2S and COS concentrations. First thing I did was says, okay, let's look at some thermodynamic analysis. And what I did is I took a real sin gas, I went and gasified wild that coal and found out that if I gasified it at the temperature that we run our fuel cells at, I get about this minute, this 0.055% mode percent H2S, 0.00337 mode percent. That's 556 ppm H2S, about 33.7 ppm COS. What I did is I took that composition and a sin gas and I dropped each one of these solvents in one at a time to see what it would absorb. And then I varied the temperature. Now, these concentrations here were at that temperature there. And what I have plotted here is the mode fraction, mode percent of either H2S, solid lines are COS dashed lines. And like I said, it started out at 556. If I put any of these solvents in either calcium, or barium, notice the concentrations of the sulfur H2S in the gas phase falls between the one and 10% level and the COS concentrations fall less than one ppm. This is what we were looking for, okay? Why was this a mystery? Why did we come up with calcium? Other people come up with calcium oxides and used it to extract SO2 from gases. But when you looked at the big particles that they were using, what you would find is that you had a nice core of these solvents that were unreacted with all the sulfide on the outside, okay? What I'm saying is that it's a mass transport problem. You gotta get good solvent utilization by having very small particle sizes. So you can get the gas to all portions of the particles. So what we decided is, you know, maybe a better way would be to disperse the solvent in the carbon matrix. So we decided, well, maybe we could take these solvents of calcium oxide to strontum and actually put them into the carbon matrix. So we wanted to test this idea. Can we get better contact between the solvents and the gas? Remember, the sulfur is already in the solid. So putting the solvent as close to the solid as we possibly can, this was the idea here. So we went and bought a commercial solid, carbon, nor it, because we're gonna have to use a lot of it and we can't keep making a lot of it with the same amount of carbon source. But this is a commercial carbon that has about 0.4 or 8% sulfur in it. And what we did is we tested, we impregnated our norit char with these different sorbents. We use witness impregnation here. But what this shows here, this is the Rodnor filament. We can put these little red spots or pink spots, whatever. They're calcium oxides. You could put strontium in there and you could put barium oxide, actually get it dispersed within the carbon nature's material. Now that we have this, we wanted to test it, see how well it works in our fuel cells. And so we went again here, we did a commercial fuel cell that has a nickel YSC anode, there's a carbon bit that we're gonna have the carbon with the dispersed sorbent in it. And on the cathode side, we just have LSM. And the membrane here is hyonics, but it's basically YSC, okay? We wanted to do some voltometry and some EIS measurements to see do we get better performance when we use these sorbents. Here are our voltometry measurements over here. This is volts on the left side and current along the X axis. Look at the red lines first, folks. There's power density on the right, but I'm just gonna look at the voltage drop. See this drop down here, that's the current voltage relationship for the as received norad. Let's put some calcium in there, mix it with calcium, we get better performance. Some strontium, the green lines, we even get better performance. And we get our best performance with the barium oxide. So we see we get better performance with these sorbents mixed in with the material, carbonaceous material. The high voltage attained with better solvents is probably due to better scrubbing. The BAL barium scrubs better than strontium, which is close to the calcium. We then wanted to say, well, okay, we get better performance, what's the lifetime? Did we get any better lifetimes? And so we started doing current versus long time testing, just running the tubes for a long time. Here's the red, that's our untreated nort material. And you can see up to about 11 hours, our fuel cells could put. Let's put some calcium in there and we get much better performance, but we still get a lot of degradation over time, not as much, and it looks like the strontium seems to be much, much better than the calcium. Believe it or not, I didn't show this, but barium, we wanted to check the state and make sure it was right, but the barium actually seemed to come out and actually level off a little bit, but I really wanna run those experiments again before talking about them. So it looks like, mm, okay, well, although we got better performance, it didn't really do too much for our lifetime here. We did some electrochemical impedance spectroscopy every three hours doing one of these long tests, let's say with the calcium that we did it with, we did an EIS curve to see what was going on, okay? We know that this first lobe is associated with the cathode and this node here is the one that's associated with the anode, and what you can see is that over time the resistance is growing. Bad idea, that means the cell is degrading. We're next set, okay, let's look at this MEA. The ASS received membrane right off the shelf, what we did is look at the MEA, this is an XRD, what you see is nice, clean zirconia signals, zirconia outside uterus, they're live zirconia, those are in the red and nickel oxide, nice nickel oxide, so this is what we're starting with. Now, when we run our fuel cells, folks, before we start them, we have to condition them. When we condition them by with hydrogen, we condition the nickel, try to reduce any oxide before we start our test. Now, I'm gonna show you something before we even started our long time test. I'm just gonna show you what happens just doing the conditioning, we looked at our MEA. And guess what? You know, all this doing is sitting there with the carbon bed above it, we're just heating it up to its temperature and all of a sudden we start to see these sulfide phases in the zirconia and in the nickel. We could tell you right away this is not gonna work, that's why we are glad that we took this two-pronged approach. Number one, we feel that these bariums calcium and strontium, yes, they can help promote fuel cell performance, but we need something a better electro material and this is making us go to support, start develop perovskites. What's the problem with perovskites?  So perovskites are these kind of materials, ABL3 type materials, A and B cations, A is bigger than B, and we've been playing around with our strontium titanate, and that's just a little figure when I got off the end of that. Okay, people have done some conductivity measurements on perovskites, here's some perovskites that people have done some conductivity measurements on. The perovskites are good, are catalysts for OCO oxidation, and this one right there, the lanthanum strontium titanate, it has a very high conductivity, so we said, great, maybe this will be the material that we should pursue. This is just what we did, we started trying to synthesize new materials, some of these. What if we synthesize the date in our laboratory? Well, we can synthesize the parent strontium titanate and then we can start doping it with all kinds of materials in different stoichiometries, and the interesting ones here are ones that we're doping with lanthanum and barium, okay? We check to see how good they are, so here's some XRD spectra, if the one down here at the bottom, I'll put this up too, so those lines are there. Here's some nice spectra showing all of the single cubic perovskite lattice, here's the strontium barium strontium titanate, lanthanum strontium titanate, and this is one we're really interested in if they're at the top, and what they show is that these materials are clean, okay, there are no extraneous peaks, no impurities anyway, here's the strontium, here's the barium strontium titanate, okay, here's some barium peaks showing up, lanthanum strontium titanate, some lanthanum peaks showing up, and when we put them both together in there, you can see both of those peaks. Not only that, we can look at the atomic compositions that we tried to mix these things together. You know, I didn't know this, but to make these things, you can just take two solids and grind them all up together and make new solids, interesting. I thought there had to be a little bit more going on to that, but you can make it work, and you can see that we get the stoichiometries that we tried to mix, so that's all that says. We also wanted to know, do these MEAs that we make, are they gonna work? And here's a performance, here's an IV curve, father lanthanum strontium titanate, one of them that we're making, you see we get good fuel cell behavior with them, and then, you know, I never believed this, folks, if you don't wanna work with seals, don't get into the fuel cell business, because fuel cells leak and you gotta try to seal them off. More than half of our experimental time in the laboratories is spent dealing with is our fuel cell sealed properly. So we've finally developed a nice idea, we call it a leak test, can we hold a current over time? And we won't even run a fuel cell until we now can make sure that it's properly sealed, okay? Let's move on to our modeling activities. We have two types of fuel cells, we have what we call a button cell, which we can model using planar geometry, and we have these tubes that looks like real tubes. So we model those also. We have finite difference programs that we have developed. We try to, with respect to input parameters, predict things like cell efficiency, hydrogen production, and electricity production rates. So what have we done? Just for orientation, here's a cell here, a cathode here, so this is our air side or our steam side, and here's our anode, so our cell, our carbon is gonna be on this side. When we talk about our tubes, think about the fact that our fuel cell is going to be creating some CO that has to be oxidized at these anodes. Well, the flow is also going out. So CO can also leave the system. And so we're always concerned about how we need CO2 to leave the system, not CO. So in some of our tubular fuel cells, we have what's a freeboard that has the electrodes but no carbon, and that's all of the CO that's leaving the carbon bed has an opportunity to be oxidized to CO2 as it leaves. What are our variables? Well, how tall is the tube? How long should that freeboard section be? If we have tubes in there, how close can they be? If you put them too close, they're gonna start to interact together. If you have them too far away, maybe some CO start escaping, we use our model to tell us all of these things. Our model's pretty sophisticated. We have electrochemical, electrochemistry. Actually, we go in and make EIS measurements, spectroscopy measurements to try to determine rates of electrochemical reactions, mass transport in the bed. We have bed chemistry. Folks, I put carbon in the bed, but I'm also putting CO2 in the bed. And this is something that people don't understand. I'm relying on the reaction of carbon plus CO2 forming CO in the bed. So I'm not thinking about a solid carbon fuel cell. I do, we do call it direct carbon fuel cell, but really folks, we're turning the carbon into CO by reacting it with CO2, a Boudoir reaction. We have all the kinetics in our code to govern the rate of carbon to CO2. We have all the heat transfer. We allow conduction, convection, radiation in the bed, the whole bit. Okay, we think it's a pretty sophisticated model. We specify the voltage. And for the voltage, we need to calculate the current. And for that current, you see that it's made up of the open circuit voltage. We used the Nernst equation to get that. There's some ohmic losses. That's the ions and electrons migrate across the electrons and go around the circuits. We have ohmic losses like that. We use ohms, a lot of characterize that. And we have activation losses at the anode and cast those due to the fact that there's an activation energy barrier that the reactions have to go on those electrochemicals. And we use the Butler-Vomer equations to characterize that. Where do we get the parameters? We actually take EIS measurements and get all the parameters that we need to it. This just shows that we can use EIS, vary composition, a fixed temperature to get information. We can fix composition, vary temperature to get information. We have an equivalent circuit that we use to get the resistances for anode and anode, a cathode. And we put it together to try to get activation energies for the electrochemical reactions. These are the ones for CO electrochemically being converted oxidized. This just shows the representative calculations where we have a model input, 0.7 volts, tube height of about 5 centimeters, with 5 centimeters spacing between the tubes. Here's just to show you what we're doing. Here's a tube with some air running in it. It's sitting in a bed. What do we calculate? We can calculate oxygen concentration profile, CO concentration profiles. We can calculate temperature profiles all in the bed. What are we interested in? Well, what are power densities? What are cell efficiencies? So we've defined those. We calculate the current density along the electrode. And if we integrate it along all of the electrode, we can get the power. We also define a cell efficiency, this power, over the high heating value of our fuel that we're using. We make contour plots that I can tell you things like, hey, what would happen if I had a fixed tube spacing and they increase a fuel height, a tube height? Well, you'll find out that I'd be increasing my power density. What if I fixed the tube spacing and I fixed the height and say increased the spacing between the tubes? I'd see I'd also be increasing the power density. But what about the efficiency if I did that? If I took these tubes farther and farther and farther away, I start to decrease the efficiency of my fuel cell because farther and farther and farther away means that there's a lot more CO being generated that's not seeing any electrodes, and it's flying out of the system. Fuel cell utilization factor is going a while. So we figure out those things. What do we need to do now? Folks, I'm showing you here the fact that we need to make some modifications to our model. This is just a macromolecule of coal, Y-deck, a bituminous coal. And if you notice a whole lot of hydrogen atoms in there, this particular Y-deck coal has 5.6% hydrogen by weight. And if I gasify it at 900 celsius, you can see here's my H2S as that number that I talked about, 556 ppm of H2S as C-C-C-O-S that I get. But guess what? I also get 18.8% H2 in my gas. So although I'm calling this a carbon fuel bed, we haven't put any hydrogen into the system, but the hydrogen coal has hydrogen. This hydrogen gets into the fuel. We need to have electrochemical reactions for hydrogen happening, although we're not putting any hydrogen on our bed. So this is where all our activities have been, trying to modify our model for hydrogen in the bed. So what do we do? We do voltimetry measurements, where we vary the temperature to figure out how performance looks. We keep the pressure. We try to figure out the pressure dependence. We do EIS measurements again to get temperature. And the partial pressure dependence of the hydrogen. How does the hydrogen partial pressure depend on the EOS? We have a nice detailed mechanism that we took from the literature from one of my friends, Bob Key, who's been working on these ideas, hydrogen and nickel electrodes and YSC. And again, we can come up with activation energies for hydrogen H2. Now, this is H2. What happens when we have H2 and CO in the bed? And what I was completely surprised at is what a lack of information there is on fuel cells when I have two species in there. So we have to get our only electrochemistry for when I have both CO and H2 in the fuel bed. We call it a syn gas. So we have to do all these tests again. Temperature dependence. We have a composition of a syn gas change the temperature of what's our response. Change the composition, fix the temperature of what's our response. Look at the EIS measurements. And again, come up with some predictions. But this one's interesting here. We're trying to here predict the open circuit voltage. Remember, that's the first part. We need to get this from the Nernst equation. The Nernst equation gives me the voltage in terms of the partial pressures. Well, what partial pressures do I use in that? Should I use the mole pressure of the H2, the mole pressure of the CO at the equilibrium spot, at the spot that diffuses over there? What should that concentration be? Not only that, if we once you even know the concentrations, how do you calculate the open circuit voltage when you have these two gases? This is something that we came up with, folks. This is open circuit voltage versus temperature. Far, we had a mixture with some H2 and some CO, I think, is the compositions over there. This top black line, that's all H2 plus 1, half O2 goes to H2O. This bottom line here, that's CO plus 1, half O2 goes to CO2, okay? The data, that's our data. These measured lines, we're trying to fit some lines through there. What falls on the lines? There's no theory that tells us what to do. We tried to mole fraction average the open circuit voltages for H2 and CO, it wouldn't fit the data. My student, David and Brandon came up with this eye correlation. These J naughts, those change current densities. Used to change current densities that we got from our EIS measurements and weigh them this way, and we can predict open circuit voltages for those mixtures. Now, what we don't know is what this means, because that's just a fit, folks, and so what we're trying to do, we need to come up with some theoretical justification for this empirical finding. Okay, and that's what we're working on now. I'm running out of time, so I'm gonna just start to conclude. What do we know? Hey, we can put two fuel cells together to make a couple fuel cells as capable of generating both hydrogen and electricity at the same time. What else did we learn, have we learned? We've learned that we can have good sorbent, metal oxides, these are metal oxides, calcium, strontium, and barium. They do improve fuel cell performance, but they do not extend fuel cell lifetime. That's what we still need to figure out better anode materials, and we do believe that lanthanum, barium, strontium, dope titanate will be our answer, okay? Why haven't I shown you any data that actually shows? I can't tell you how difficult it is to get cylinders of H2S delivered to Stanford so that you could test them in your laboratory. It's not easy, folks, not easy, okay? And we have a fuel cell model, and yeah, we can model with the H2, the CO, and everything, and it seems to work. Okay, I'd like to thank you for your attention, and I'd be happy to answer any questions that you have. Reggie, thank you very much. I think we have time for one quick question. Hi, really fast. What is, over here, what is the carbon dioxide yield when you're doing the dry decomposition of the coal to get five or six percent hydrogen? Do you have carbon dioxide coming off? Yes, I do, and it was in the plot right when I gave you right here, the composition right there. The CO2 is like 25%. It's about four to one. Yeah. Thank you. A quick question right here. I have a question right here. Oh, neither one is, is Mike. Okay, which I haven't heard about in any of the talks. There's one parameter that I haven't heard about in any of the talks, whether it's for PVs, or batteries, or fuel cells, or whatever, all the things you talked about. And that is the ratio of the energy payback time divided by the life of the device. Because if we don't reduce that number, all of these things are going to cost too much energy to make over the period of time that we have to install them. I agree. That's all I can say because I have no lifetime on this particular instrument here, but. Energy input, energy input to make the thing. Well, I haven't really thought about the cost of making, especially if we have to start make the energy, the energy input, the energy cost. But it's coming, all the energy is coming from the coal. All the energy, there's no other input here other than the coal. Now, if you're asking about building the systems and everything, but you're asking about energy resource, this only coal. Integrated cost of the material, the workers, the factory. That I don't know. I know, but I know that it's a very difficult thing to calculate, but apparently we don't know these numbers. I agree. And they need to, before we put a cap on it and say system solved, we need to figure out what those numbers are. 100% agreement with you. All right, so that's, thank you Reggie and all the speakers one more time.


Grass as far as the eye can see. Today I'm standing in the Thunder Basin National Grassland, located in the Powder River Basin, between the Black Hills and the Bacorn Mountains. Occupying over 1.8 million acres of federal, state, and private lands, the grassland was established over the 1930s when homesteaders discovered that tilling, the fragile soil caused massive amounts of erosion and made the lands uninhabitable. As you can see, the ecosystem has recovered from the dust bowl of the Depression era. Today, rolling grasslands and sagebrush provide homes to large herds of antelope, as well as mule deer, elk, prairie dogs, coyotes, and fox, many species of birds, rodents, reptiles, and insects. This land also provides an abundance of forage for domestic sheep and cattle grazing. The area is rich in mineral resources and in the past there has been mining of uranium, coal, Trona, bentonite, oil, and gas, many of which still occur today. The Thunder Basin National Grassland is a wonderful example of how Wyoming's range lands are managed for multiple uses. With its complex land patterns, private, state, and federal land ownership, co-mingling of wildlife and livestock, and vast array of minerals, this grassland truly is a wonder of Wyoming. This has been Rachel Meehler with the University of Wyoming Cooperative Extension Service.


You're looking at me. You're curious. The book that we're going to read is titled The Strange Case of Dr. Jekyll and Mr. Hyde. Now this story, it's a classic. It has murder. It has all kinds of things that you're going to read about and you're going to figure it out. Are you ready?


Good day. This is the session on what, why, and how of federated machine learning. I'm Neeraj Arora and I'm joined by my colleague, Lane Pung. And we are glad to have this opportunity to have you here with us. Both Lane and I work at VMware and are involved in machine learning and artificial intelligence initiatives within the organization. We are also involved, Lane, more deeply than I, in FATE, which is one of the many ways you can realize federated AI. FATE is an open source project and is discussed in this presentation. In this session, we'll cover federated machine learning at a conceptual level before looking at FATE broadly followed by a demo. We have been working on the FML site portal and Lane will take you through a demonstration of that also. This is an introductory session and quite a bit of information for one session. And so we'll leave you with references that you can use to learn more. Of course, we are always available to help you. So please reach out if you need to. Let's get started. AI or ML is realized using three things. Data, algorithms you can apply to model the data and enough computing power to achieve this. Most algorithms you'd want to use are available by libraries. Computing power has also been getting cheaper and readily accessible. The barrier might be cost, but for the most part it is readily available commercially. Data, however, is a different story. Due to regulations or sheer logistics or the foresight required to model a problem and the cost associated with collecting a large quantity of data, getting the right and enough data for model building remains time. As a community and industry, we have tried to solve this using public open data or methods such as transfer learning whereby compute intensive training on a really large dataset is performed once by a capable party and then the model refined for specific uses. Even if an organization has the capability to collect and store data, it may not all be available where the compute power is available to utilize it. Or there may be regulatory issues or privacy consideration preventing relocation of the data. These are the type of problems that federated learning can help with. There are a couple of excellent resources by others specifically Google that can help explain the idea in detail and I'll try and introduce it simply. In what we might call traditional machine learning, we bring all the data together in one central location where one or many compute resources as might be the case with distributed clustering systems train a model using the entirety of the data. In a federated learning system, we don't collect the data in one central location and the data lives on remote devices or edges or nodes. These remote devices or edges train a model with only their data and submit updates to a central aggregator which aggregates all such updates from all remote devices. The resulting model is then pushed back to the remote devices where training continues to refine with the model using data available to them. It is an iterative process and as you can imagine, it allows for some imaginative distribution of work especially when it comes to the train validate test process for model building. We'll see that in a following slide. Significant amount of work has been done and is ongoing since federated learning was first proposed. This is an excellent survey paper if you're interested. As you read through it, you'll see that federated learning provides some very novel answers to the problems with data science. It is not difficult to imagine how federated learning may affect and change the world of IoT and industry language. I'll try and illustrate the essential concept of federated learning graphically and we use federated learning and federated machine learning interchangeably in this presentation. So let's assume we have a few mobile devices with their own local storage as participants and one initiator and aggregated device. It's shown as a mobile device but realistically it will be a heftier system. On the screen, the participants or nodes are shown on the top row named P1, P2 and P3. They're connected via some networking technology be it 5G, LTE, Wi-Fi or Ethernet or any of the other million choices. The initiator initializes a model and sends it to the various nodes. The nodes train the model they receive using their local data and send the updated model back to the aggregator which combines them into an updated model. Surely I have simplified the process for explanation instead of the entire model being exchanged. For example, only the Delta weights need to be exchanged but the concept is the same if you will. Let's look at the process again but this time let's extend our imagination to the three phases employed when model building namely training, validation and testing. Not all nodes always participate in training or validation of a federated model. The aggregator or the server decides which nodes participate in a run or an epoch. By excluding participants and consequently the data those participants hold locally we are dividing the data set into subsets just like we divide data in a data set into training sets, validation sets and testing sets. Notes that participate in training in an epoch will be excluded for validation and testing in that epoch. I hope that it helps explain federated machine learning broadly. Overall, there are three types of federated machine learning at the moment. There's horizontal federated learning, vertical federated learning and federated transfer learning. Horizontal learning is where the same fields exist with multiple nodes. For example, branches of a bank that use the same software may store the same fields about completely different users. If this data is stored locally with each branch then this is a good candidate for horizontal federated learning. Other examples would be network of hospitals, shipping companies or manufacturers with similar assembly lines. Vertical learning is where different fields exist with multiple nodes regardless of the entries in the data having any relationship or not. A good example of this would be different merchants or businesses that service the same population such as a credit card issuer, like a bank that issues credit cards which customers use to pay utilities. A good use case would be a bank geotracking credit card usage and determining either lots of customers not local to an area or descending as tourists or for events with the utility companies in that area or public transport using that information to tailor capacity for changing demands. Federated transfer learning is applicable similar to regular transfer learning where generalized models with aggregators can be specialized for local use by node. For example, infectious disease detection at hospitals, fraud detection across banks operating in different geographies and fault and accident prevention by mechanical failure across an entire industry that uses similar equipment. Summarizing simply, federated learning attempts to address some of the issues that we are seeing more and more, namely the issue of privacy, trust enforced either by user choice or by regulations, the issue of aggregating lots of data in one location, the lack of data itself possibly driven by the cost of bringing significant amount of data together. Depending on the kind of nodes you deploy, it may help with sourcing compute by conquering a large problem by not only dividing the data but also the computational load and depending on your implementation it can be highly scalable. Although federated learning brings in these benefits, it also presents some challenges. Being a heavily distributed system, it can get tougher to secure data and apply security policies. This could compromise data confidentiality with emergent attack vectors. Being distributed will also affect availability of both compute and data. There's also the issue, and this depends on your use case, of the integrity of data that remote nodes are presenting. At a basic level infrastructure events that are relatively easily solved in a data center environment become complex in a distributed network and more so in a widely distributed network. These are some things you want to keep in mind as you consider federated learning. We saw earlier the three pillars that support AI and machine learning. I'd like to add a fourth pillar here, ops. It is one thing to build the model but entirely another to include it in a product. Models are also often refined with new data that is constantly collected. Federated AI technology enabler or FET is a suite of libraries and services you can deploy in various forms on various form factors to enable your solutions with federated learning. FET brings together algorithms on distributed computing power whether it be servers in data center or compute power and edges with data that is local to those nodes. FET also adds this additional pillar with its various sub projects as you'll see later in the presentation and in the demo. FET is also open source and open to community participation and we invite you to participate. The ops pillar is critical for success with any ML or AI initiative in production. Our work on FET adds in pluggable compute libraries, data sources, the ability to scale across clusters and nodes, full lifecycle model management of FML-generated models with resiliency against infrastructure events being built in. With the right combination of third-party products you can also extend the capability of FET. And now I'll pass it to Lane to take you through some parts of FET in a bit more detail. Over to you, Lane. Thank you, Lila. I'm Lane Tan. I will cover the projects we've contributed and working on to bring federated learning into production easier and then we'll demo a full flow of federated learning tests. We know besides the capacity of normal machine learning, federal learning plans, actual operational changes to cluster management to help easier to manage the federal learning cluster with initial and open source project, QFET which is part of the FET community to manage the federal learning cluster in one site. The QFET support both Dock Compose and Kubernetes environment. The Dock Compose is decided to equip staff and for verification and development cases. It can be very quick to provision a two-site federal learning environment in your laptop or machine and may you try the learning develop and the learning operation in minutes. If we consider the production environment, we suggest the Kubernetes deployment because in this case, we provide extra features to show professional changes such as unified control for multiple FET cluster which may happen when an organization collaborate with different pilots and how to keep the network device isolated from each other. Declarative resource management which is proved a good practice we manage a FET cluster and QFET also provides upgrade migration, log creation and monitor all these very useful professional features. Both Dock Compose and Kubernetes is based on container so it can run on premise their centers or public or private cloud. And in FET, we have embedded Jupyter notebook for their scientists to program their machine learning applications. However, as introduced above, federal learning is composed, terrorized and include did not only machine learning but also critical knowledge critical and infrastructure technologies. When we meet a real user from traditional industry, there is a strong requirement to lower the bar to use federal learning culturally. FET is designed an internal pipeline and with a lot of our boss federal learning operations. We can program our application as composing a pipeline. Based on that, we decide the FML side portal which provides a local federal learning experience. The FML side portal provides federal side management, data management, model management and project management features in a web GUI style. With it, we can upload and into the data manage data contribute to the federal learning. Take our boss algorithm in FET such as homogeneous logistic regression, homogeneous SD-boost, et cetera and rating the result all these step by step and put them into the pipeline and kickstart the federal learning job all by creating the mouse. For easier to get what I introduced, the FET to FET FML side portal, let me show a quick demo. In this demo, we will deploy a FET cluster on scratch on Kubernetes with QFET and then we add this new deployed FET cluster to our federation and do various learning with pre-deployed FET. We use Kaggle case as the demo. We firstly call the application in a Jupyter notebook and then we will show the GUI introduction of the FML side portal. In this demo, we use open source Kaggle data to create a cloudflow detection as a data set. As we prepare a small Kubernetes cluster to simulate various management side and other two Kubernetes cluster, the first one we have pre-installed FML stack include FET, FET side portal, Jupyter notebook and other components. In the second Kubernetes cluster, we only pre-installed to FET which is an open source project hosting in the foundation to provision and manage FML cluster of a party. We can provision the FML cluster FET with command line. 10,000 is the ID we used for a FML cluster. We are working on a project called life cycle manager which can provision and manage FML cluster from a GUI app GUI. Now we got an FML cluster installed in party 10,000. We can use it to demo our federally learning job, credit card for our detection. As said before, we downloaded the open data set from Kaggle and 3D, the data into two harvest. Each party keeps only half of the data to simulate its own private data. Then we use the Jupyter notebook which is included in FET as a user interface for the entities. Similar with traditional machine learning, we can write a training code using passion in notebook such as preparing data and setting up the training job. The algorithm we used is called horizontal logistic regression. It is also called homo logistic regression. It is a little complicated to implement this algorithm. FET provides a lot common federally learning algorithm which we can directly use for our job and made the training as a composite pipeline. We run it step by step. Here it goes. After training job is submitted, we can see the training status in FET board, the FET component to visualize the training process. After training, we got our trained federated model. The model contains the knowledge from both parties and the performance is similar to that of an essential training model. But we got it totally without data piracy with leakage in FML. The process of federated learning only is changed in creative gradients. One more, the trained federated model can be converted into other model format such as on this PyTorch and 10th row. Next, we can publish it to our cave serving service and serve it from there. Maybe you found we use Jupyter notebook is still a little complex. For easier confederate learning modeling, we are working on a project called FML side portal which can replace the world we just demoed in Jupyter notebook. In FML side portal, we can manage local data contribute to the project. The model we obtained, the collaboration training project. Our project is a concept to manage the cooperation between different parties. It consists of participants, data and jobs. We can manage different federated learning project in this unified interface. Our job is the FML task we want to complete it. It is a pipeline with different outboards federated learning components. For example, the training we did in the demo with Jupyter notebook is our job. We can have other different jobs such as PSI, predation, etc. I believe if the project we are working on, there will be more FML applications and will appear. Okay, the buffer is the demo. How we can make more clear the project then I will pass back to you Milak. All right. Thank you, Lane. Much appreciated. The demo was awesome. So closing out this presentation. In this session, we went over what federated learning or federated machine learning is at a very high level. And again, at an introductory high level, we saw some of the challenges that arise in high-scale federated learning operations. We saw some information on how to get started with FATE using KubeFate. And Lane presented us with a demo of the system using KubeFate and the FML site portal. And with this, we come to some resources that you can use to get reintroduced after this session acquainted with FATE and federated learning in general. FATE is an open-source project and we are inviting community participation. We would love to work with you on FATE. Love to have you as part of it and to see how it can change and adapt to your needs in your ecosystem and with your organization. You can always reach out to us. I'm your host, Neeta Jarora. And I was here with, I am here with Lane Pung, my colleague. We are both employees at VMware and we are actively involved in AI and ML projects in the organization and outside. So that's it. Hopefully, we get to see you in person sometime soon. Thank you.


PSA 5.7 Rock, chambered in 5.7 by 28 millimeter. It's a full-size handgun and flat-dark earth, 23 round mags, 5.2 inch fluted barrel, six pounds with a very short reset. Night sight, go for between three and $400 depending on what you get. 5.7 round, there is like zero recoil.