Enabling Applications of Nano/Micro Machines Brainstorm | Ayusman Sen, Pennsylvania State Uni

10:20AM Mar 9, 2021

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Keywords:

applications

technology

talking

people

motors

pumps

membranes

field

nano

proposals

product

arpa

called

molecular machines

nano machines

analyte

properties

thinking

sensor

systems

Welcome, everyone, to another meeting enforces molecular machines group, we have James o chair here as well. So he'll also be in the chat in case you have any questions as to how this group is running. And just a few words. For me, this is not one of our normal keynote presentations, as I think many most of you are hopefully aware. And this is another a little bit of a different thing in which we're trying to generate potential proposals for enabling applications that are a little bit more future looking. So this is work that may already exist, but they could be expanded upon to actually generate, you know, really ambitious proposals for long term progress. And it was proposed by Iseman Sen, after our last meeting with Isaac cassioli, from ARPA E. And I like, of course, you know, was talking about aparece program overview, and even though that many of the applications that he was talking about are miles away, and I think for many of the things that people in this group are working on, nevertheless, people in this group have been funded by IRB, and by about a year in general, before, so I think it would be really helpful to think about ways in which perhaps the research on the long run could connect to those applications on the very, very long one. So I think this is one of hopefully many sessions in which we can get together and brainstorm a little bit more creatively together about potential future applications, even if they're enabling rather than actual applications just yet. Okay, we have a shared meeting example presentation before, we'll have a session of potential ways to think getting such proposals. And, and I will share more information as we go about the session. All right, I guess what do you want to kick us off?

Sure. So this was, as Allison said, was kicked off by the presentation by the ARPA program leader. And ARPA, as you know, is interested in things that are likely to become commercial but but we thought that this was a good time to take stock of, you know, where the field is going in terms of enabling applications. And so we thought, we'll have some kind of a roadmap I, I say roadmap, it's not really a roadmap is like an entry that keeps changing depending on the chemical gradient that is being laid, laid down by all the participants. So so for example, we know that materials started with passive nano structures, recordings, polymers and nanoparticles. And then we move to active nanostructures, which is what molecular machines are right now mostly actuators, amplifiers, switches, adaptive structures, and so on. And we are slowly moving towards systems of nano systems. So guided assemblies, hierarchical architecture, nanorobotics, how to translate nanoscale motion into macro scale effects and so on. So, what I wanted to kick off is this is the first session, hopefully many, as Allison suggested, is, is to basically brainstorm Why do you see the field to be in five years? In 20 years? How do you see your work to fit into this vision? We all have a vision of the field in our head. And we were curious about how you see the field progressing, and how your work fits into that. What do you need to advance what signs Do you not understand that you would want to understand to advance the field? What kind of techniques Would you be interested in using to move the field forward? What is your expertise? And what is the anticipated social benefits? This is this is important more and more. The funding agencies especially the National Science Foundation, the US are interested in understanding what the societal benefits are from any particular science or technology. So the overarching goal is is to come up with a possible enabling applications now. Given the sophistication and the therefore the cost of making nano machines, we better have some killer apps applications where this is the only way to solve a problem. So it's, it's it's a place where nano machines and nano motors indispensable are are the tools, tools of choice. So what I want to do is two. So I'm I'm mostly in now what I call nano motors. And so I wanted to start off by giving you some conceptual ideas about where I think, and this is my subjective view, where nano machines can contribute to enabling applications. So the slide isn't moving. So I'll stop sharing and reshare. And I think that will work fine.

So the, in the case of nano motors, some of the possible enabling applications are, for example, motion based targeting, where the fact that you can move these particles around, they can sample a large space, fluid space by power diffusive motion, so they won't get hung up in some local minima. So this is a much more interesting way to sense things to deliver a cargo for example, drug I use when I use masking anything, oh, sorry, you're not

sharing?

Oh, I didn't. Okay. Um, so yeah, we've talked about emotion based targeting where these things can move around. And, and so you can have mobile sensors, that actually looks for analytes, or things that can look for targets to deliver cargo to. So these are some of the possible applications. We think chemotaxis, for example, which allows these things to move towards specific targets who can call gradients is a way to direct motion to direct assembly. And these can help in promoting photolysis. Because if you bring the catalyst together through proximity, chemotactic proximity, then you can accelerate the reaction or minimize the by products. You can talk about analyzed triggered pumping. Many of these nano motors, if you attach them to a surface, they will pump fluid. So this is a way to pump fluid, and I'll talk more about it in a minute. You can think about nano and micro sensors. So sensors are being miniaturized and more and more than becoming smaller and smaller and smaller. The problem is that if you make a sensor at the nano scale, the problem is, the analyzer has to find that sensor. And that depending on the size of the analyzer, if it's moleculer scale, it could take days or weeks to find your sensor so the sensor can sense it. On the other hand, if the sensor can move around, or it can pump fluid towards itself, then you can speed up this process. So this is a new way of sensing. And also, since the since these things harvest their own power, you don't need an external power source. You can think of tuning rheology of fluids by active particles on the fly. You can change glasses to gel and gels to glass and so on. And of course, you can use them for enhanced transport and mixing in confined geometries. So I wanted to take two specific examples, if I may. And since I sent out this template, I guess I'm forced to use it. So this is this is pumping. And this has to do with the fact that if we attach nano motors or catalyst particles to a surface, pump fluid, and we've been looking at enzyme for the most part, and there are many ways you can attach enzymes. What are these good for? Well, you can use them to deliver drugs or antidotes. You can use them as sensors for toxic substances, because these are not just pumps, these pumps come sensors, because they only start pumping fluid when they see their own substrate. You can use networks of these pumps to focus analytes. So what is the benefit of such a proposed technology? Well, the analyte is a trigger and the fuel source so you don't have to add any power source. And so they can, you can store them and they will turn on when they see their fuel, which is also the analyte. So these are some of the examples of fundamental understanding that's required to move this field along. And as a matter milestone of goal we would like to make? Well, we sort of made them anyway, gel based enzyme pumps that will deliver small molecules or proteins, for example, release insulin, at a rate commensurate with the ambient glucose concentrations will be like an on demand, insulin delivery, depending on how much glucose there is in the bloodstream. Actually, this project that I'm talking about, was funded for several years by ditra, one of the defense agencies, they were not interested in hearing your diabetes, what they were interested in was using these pumps to neutralize nerve agents. And it actually works quite well. But this is an example of an enabling technology. Now, I want to go one more step, which is, since this is a group of very talented people who do things that are related to each other, we can start to think about center type proposals in a big, multimillion dollar proposals. So the using this pump as a concept,

here is here is just an example version, if you will, where you, you, you take the fundamental knowledge that you get from theory and experiment on catalytic processes and kinetics and flow for these catalytic pumps on nano motors. You can do materials, synthesis, scaling, modeling, and with this, you can build a technology base, where you learn how to control flow, use the flow, we call them order flow, because they are autonomous auto flow for synthesis for diagnostics, Chip based micro fluidics. And so you build up a technology base. And that allows you to start making real life gadgets, if you will. And I put in all of these NSX, big ideas that that NSF has been trying to push, and where they fit into this whole whole project. And, you know, what are the feedback loops. And of course, for example, if you're using them for diagnostics, you're going to use it in third world countries, for example, places where they're not no good power supplies, for autonomous things can work well. And you'd need AI and data in for data processing, to understand where, for example, viruses are going, and so on, so forth. So this is just just an example. And so what I'm hoping, through this session, then and sessions further down the road, is for us to get together and start thinking about projects of this type, for example, our pies, obviously, an example that is more more applied. But also there are some basic centers, for example, Center for chemical innovation, that NSF funds, and other centers, and Engineering Research Center, do D, do E has centers like this. And so I think if we start to put our heads together, we can come up with these things. It's not something that a single poi can do. It's something that we can do together. And I think, and Alison can correct me if this is something that fourside is here to help us do. Okay, Mr.

Hassan is sharing his screen. Fantastic. Do you want to take?

Yeah, thank you very much, Alison. And it useful for setting up some of you know, an application and to Jim tour as well, again, a couple of applications in our mind. And it was a couple of things that struck me about the opportunity of the year ahead of us. It was quite unique that we're able to meet relatively frequently, and grapple, perhaps with that 30 year time window. Make 15 year time window. And I'm bringing a position of some reflections. Number one is the position of DARPA in ARPA E. Mr. Take a technology that's 15 years out, and to pour millions and millions and millions of dollars into about three or four research groups to compress that to five years. Okay. So I'm asking us, you know, where is that 15 year technology window? Okay, and then we've got an opportunity to put our heads together and think about what that could be. I wonder if it couldn't be for many of us. That's I'll come back to that in a moment. The other one is to think about that 30 year time window, the things that are much much further out, but which we might pick up from, you know, enjoying a favorite sci fi, movie or program or what have you. And so I was thinking, together with Avi, singer, Roy, who will speak after me, we were thinking about possible classification of applications, some of these elements have come up already today. And there's a couple of things that I bring to the table and recognizing a lot of the people here and that each and every one of us has our favorite, idiosyncratic, molecular or nano system that we like to play with, we like to work with, we know it intimately, we can make it we can study it, we've been thinking about it for 510 15 2030 years. And then Okay, so that's where West what we'll bring. And then we have ideas for applications. And use me to shed some a couple with us. We had one from Jim tour, I'm sure we will be hearing more. And then we have, you know, this idea of a killer app, and that's market poll. Or if you pick up the book that James suggested loon shots, by Sufi by Carl, it's when there is no market that exists at the moment, it is such a far out idea, it is a moonshot. It is something that can can really transform life as we know it. But in lieu of that, if you're thinking about what is a market, Paul, we have, okay, so there is the one where there is no existing market, but then there are ones where there is a marketplace. And the thing to be keeping in mind is the adage of the factor of 10 here. So if the performance in an application of our system and use man was talking about systems in the end, is 1000 fold better in my laboratory, the moment I make it into a prototype, it's only going to be 100 fold better. And the moment I make it into a product, it's only going to be 10 fold better. But 10 fold better is sufficient for a company to be built around it to have mark to actually have market traction and to move the dial and people using that technology. So those are sort of a couple of framing perspectives. And it's got elements of what we've heard about from Adam and from from being looking forwards and looking looking backwards. But I'm kind of motivated by how can we bring it many of us along at the end of the year, and come up with some ideas that we can pitch to our respective governments, right, because a range of people here to get them excited by something that's important. And maybe it's not just me and my buddies, but it's a bunch of us that are pulling in. So with with help from Abby, who who will take you down a pathway of machine learning and gaming all of this. This is what we came up with, if we have all the existing designs, and I can put my my motor molecule down machine or switch or whatever is one, you can put yours as 234 and so forth.

And and Adam mentioned, what are the attributes? Well, I already know the attributes that I know about my, my molecular systems, redox, active light driven, water soluble shape, persistent, whatever those attributes are. And then as we begin to share potential applications, these ostensibly constitute engineering outcomes, the step along the pathway to a product. And so application number one, I have one example. We've heard from a human that could be application number two, and he had pumping. He called that an enabling technology. But really, I think his his actual application was sensing, drug delivery, and nerve neutralization, those are the actual boots on the ground applications. And I can start at the left hand side for my motor molecule, write down with attributes and identify whether it would or would not be in that particular application. And when it is, I can then go ahead and say, Well, is it 1000 fold better than then the existing one out there and if it does, then it has the potential for market pull, where has that element of being able to be recognized by people evaluating the dilution in the performance watch, you begin to make a product down the line. But dream of course is to make something that you can't do otherwise and that topic has also come up but both of these I really the types of things that that that will ring around our mind as we think about innovation. I have one application called nano electromechanical systems are an idea that I stole off the top of a proposal from 20 years ago when I had my head knee deep with a bunch of Fraser startups crew when he was hanging out with mechanical engineers. And it's been around for 20 years, it's nothing fantastic. But I took a leaf out of the the show the the the application here, the sci fi, the expanse and cloaking technology, as this is completely pulling out of thin air. So can I design a system where I just had molecular machines that are wired up on the surface of some spaceship that can cloak it by moving mirrors up and down to bend light? On a nanometer scale? Okay, will this be a technology product? No, that's for the that's for the engineers. But it allows me to say, well, like Adam was asking, what are the attributes? What is the frequency at which I can do make this thing get mirror go up and down? Is it this would have to be redox responsive, or some other sort of way that I might analyze that's and that's knowing about the potential application allows me to go back and make an assessment. Okay. All right, in the 1000 fold performance is quite simply because molecules are 1000 times smaller than most things you can micro machine to make merits. It's something as simple as that these are relevant, we should be able to populate these types of analyses. Quite simply, what are the possible outcomes for us as a group? Okay, we could vote on mass is for different applications. where many of us have a coincident opportunity to use our, our systems to tackle the same idea. We could think that that's a good use of our time looking forward, or we could all fall Instead, focus on an application that has a really high chance of outperforming existing technology, and kind of collectively think in certain directions that ultimately set agendas for the funding funding agencies. Or we could we could take a leap into the unknown for some Fiat technology, a little bit more science fiction. But again, one that we can many of us can gravitate to, that's bringing us all along some sort of high profile moonshots wherever perhaps I might need to retool my molecular machines on my system to meet that application. But I'm joining in, on tried to tackle a problem that can only be addressed by by molecular and nano machines and motives. And Jim took a hit, you know, incited a use, man, right? The reality of this thing is, shouldn't we, as a group be targeting the big questions, the big ideas in the 21st century was heard about environment with, sorry, energy, we've heard about health.

And perhaps the other one is environment. So these are the types of things that are bouncing around in my head. I'm not saying we have to go through the the exercise of filling out this chart and telling it all up and all the rest of it. But as a community, we have an opportunity to present outwardly, a number of big ideas and potentially applications to tackle them. But if we were to go through this exercise and lay it all about, I'll be single Roy hung up past the floor to next has some ideas of how to gain this using machine learning. And before I pass the floor to Abby, and I'll be controlling the slides I'll be so you're all good. Abby is he is an assistant professor at Arizona State University with expertise in molecular motor theory, and does multiscale simulations for nanoseconds out two seconds. He's a co developer of the popular MD program Nnamdi. So some of you may be familiar with that. So longer pass the floor to rd for how to gain this with some machine learning.

Thank you. Okay, so then let's say we have as a community made this table. The next thing one can think of is doing some sort of a classification of this table. One example of classification is the periodic table. You have the electronic structure properties and you're classified primarily based on energy as a as an outcome and you have gotten a periodic table might we can make a periodic table of the moleculer motors that we have today. So to do that, then Mr. Can you hit next? Good. So what we have But what we call is a is an SVM classifier, or it's called a support vector machine. That's a kind of machine learning language, which allows you to classify based on an outcome normally wouldn't have needed machine learning, had you not targeted an outcome, just giving a bunch of parameters, one can find clusters of where different properties can be, you know, subgroup by simply the black box here. So it wouldn't have needed anything anything like machine learning, that would have that that's basically, you know, one can do it right away. But if you want to now focus applications, like towards a particular application, you want to find different compositions, if I made a different stoichiometry of each one of the properties to either have a one zero 1000, or 100 outcome, that's what this this this class, this kind of learning does, it creates a very weird hybrid space of all the properties that we might not think of the combinatorial be be linear or nonlinear, will not be able to, you know, like in our mind is difficult to make those math just beyond a two dimensional problem. So what we can do is we can create those that space, the computer can create that for us, the better we have more existing designs, the number of rows we have in this in this matrix, the better our life will become the more ticks than crosses we have, in any one of these, we would actually be able to do a better job, there are certain biases, I'll talk about that, okay, Then am I going to hit next? Great. So what is the outcome of this is going to be so if we train this algorithm, if I may, based on the existing designs, then the algorithm is supposed to tell us for a particular property, it can be a binary one or zero, like let's say it's a nems application or not an EMS application, or let's say anything on a scale of zero to one a continuous scale, that means that a NIMS application with a particular amount of confidence. And what this algorithm is gonna tell us is, this particular motors with a certain number of ticks, and a certain number of crosses, are actually going to finally, or this particular properties, if I'm alone, not even the motors, properties with a certain number of ticks, and a certain number of crosses will actually be successful. The motor, that's what we just learned. Thereafter, what we can do is we can predict designs, we can actually choose our number of ticks and crosses and ask the question, is this going to be a good application or not. So that's kind of the way in which this sort of a classification works. So the idea is to kind of have have have have everything, whatever we know, put it on one table, and then learn what combinations of those can be successful or cannot be successful. There are of course, issues with that, as I said, One issue is, I would say limitations, we have to be very careful, we have to make sure that at least each row will have to have a certain number of ticks and and not crosses. The other thing is bias biases are very, there are people in the group who are working for 30 years, there are people who might be five years, so might be their friends, their colleagues who have multiple motors, and there are colleagues who have just one or two motors. So therefore, the algorithm, the outcome will be biased by experienced literally, it might end up being that people who have literally, you know, ruled the roost for a longer time will actually have their motors working. So we'll have to give attention, that part can be overcome by using the idea of attention, we can give attention to certain younger motors, so that they also get a chance, a rare opportunity to contribute to the larger application. So those can be done, but it is to again, to classify this based on outcomes. So So yeah, that's that was it from my end?

Thank you.

Thank you so much. All right. Well, perhaps, instead of I'm going to stop sharing your screen with your mind. And perhaps, instead of applying your framework immediately to easements presentation, perhaps we go with Marty's presentation at now. And then afterwards, we'll, we'll see if we can use it in the discussion of, of the second proposal. So I'm really, really happy to have Mighty edelson here and Marian and Yale founded covalent. And he's here to talk about a proposal that has actually gotten D funding. And I'm really, really, really happy and grateful for the two of you to be able to join them. Super, super excited for your presentation. Thank you very much. And mighty, it's working. So happy. You're muted.

Okay, now I'm not muted. Hi, everyone. This is sort of a less technical scientific talk than what we've seen before, but it sort of goes to basic principles. You see if I can get this thing to switch slides now.

It also, by the way, it looks downstream, in terms of where it is that things go for everybody, assuming your basic research is successful. It also sort of talks about dresses. Therefore, the other question which is this 30 A year outlook timeframe on the field is how long does it take for stuff to happen. And there was a mention of ARPA e going for time compression. Well, there are other things that give you time compression as well. And we're hoping that our work will lead to time compression. For everybody else.

I am having problems doing the control of this thing.

So you're already using it once or being able to go, Oh, yes,

perfect. It's just very slow responding to the key presses. Okay. So along with a lot of other people, what we've looked at is d convoluting. A project that is almost science fiction, to get to simple components that you could then actually build a commercial product out of, and this case, we went all the way down to saying, okay, we're obviously going to use molecular building blocks of some sort, we're going to use self assembly. And we're going to look at doing 2d components, to get to a structure that's commercialized blee. scalable. We also wanted to generate something that could use a common manufacturing platform to do a whole bunch of different products. In fact, we've identified 90 classes of products that could come out of the early building blocks, that are basically simple organic molecules. Okay, and sort of the principles grew going forward. Keep it simple, stupid, a static structure, which you which people have talked about before, and make it scalable, using only organic chemistry, organometallic chemistry, and straightforward chemistry, like you could do in a pharma company, where you could outsource things and the scaling is not an issue. Also, one of the things that we wanted was to use as little of the nano material as possible, in the final product, to keep the cost down. So that those were sort of the chemistry thoughts about that. But then there's a whole bunch of business principles that you need to put on top of that, and Gail was going to talk about some of those.

Okay, so these are the business principles that we use for initial products. We will modify some of those when you get into more advanced products, and there's more room for experimentation. We've got more slack in our system. What's really interesting is, you notice that as the funding agencies, this was mentioned earlier. So I'm underscoring the point, as the funding agencies have moved away from pure research, and are asking what are the benefits? And there was a mention of, well, how many orders of magnitude improvement Do you see in the lab versus when you get out in the field, these fundamental business principles are wending their way backwards into the early r&d phase. So these are the things that we applied for developing actual product. But they're issues that are going that resonate way back into the beginning. What we did was we, for initial products, chose to reduce risk by having an already established market. The toughest thing in this whole field is education. So one of the things that we have looked for is, how can you possibly minimize the educational costs, and you'll end up seeing that sometimes you can't do it that way. It's very tough. And it's something that we as a group all need to do together. That's been pointed out. It came up as a theme a little earlier. Let me second that, okay. Education is a big deal. So going into a market that's already existing, people already know what this is about. The people are in pain. And they know they're in pain, they know they want it worth key criteria. When you're looking at 90 classes of potential product, you've got to figure out how to narrow your choices, as well as what's going to succeed. So these were the things that we look for, to determine what we're going to be our, our particular game changers. And when you end up looking at the benefits that come out of our choices, you're looking for really hardcore benefits. You're not Looking for minimal improvement, you're looking for things that are life saving. And we can apply that to you know, individual human lives. But you apply it to the planet, you can apply in a bunch of ways, but what we can, what the whole thing with atomic precision is the level of benefit that we all are capable of delivering is incredible. And we should go for it. Next slide. Marty.

I'm trying to make go to the next slide.

Okay, you want to give us a preview of what's on

it? Let me give you a preview of what's on that next slide. Okay, um, the next slide is really talking about the challenges that all of us face. And this in a way relates very strongly to education. The challenges that we all face are financing. Okay,

and showing up on the screen. Okay, okay, keep going. Go.

So, one of the challenges that we all face key challenges. This goes right back to education, okay. But it also goes to risk. So, you know, we're talking about getting grants, right? Yeah. Okay. So there's, The unfortunate thing, by the way, is that if you look at this chart, which comes out of NIST, but it's also been done by Gao, when other people look at that blue curve on the left universities and governments, it's like, as tough as it is to get that funding when there is then a gap. And it just bottoms out. And this was called by NIST and Gao, this is called the valley of death. So your problem is, if you can get, you can get initial grant funding, relatively easily. I say that with appreciation for the level of pain that we all endure on early funding. But it's like, if you've experienced think you've experienced pain, wait, just wait, okay. And it's going into that gap between public financing, and private financing, because nobody wants to step up to get you into manufacturing. So that becomes a challenge that all of us have to deal with. We're hoping that the work that we're doing is going to mitigate and close that gap for everybody. So we'll, you'll hear the rest of the story. And you'll see if you think it's going to pay off or not. One of the key issues that we all face is investor education. And again, it doesn't matter whether you're talking with DLP, ARPA, ARPA E, you know, an equity fund, whatever. One of the things that is very, very hard to get across is the transformative capabilities of molecular machines, which are really obvious to us. Don't resonate in these other marketplaces. And one of the places for example, that just like, let me tell you, it's like we've lived this one is the fact that since you can go ahead and do something as transformative as what Jim was talking about, which is replace antibiotics, you know, where you've got bacteria working around them with something? Well, actually, that's an easy case. If you go to someplace where there has been no progress for decades, and believe me, you can find these fields, okay. It's like state of the art 1940. Okay, no progress, which for everybody who's in tech, very hard to even imagine, but let me tell you true. Okay, so that's a blue ocean zone. And this is, you know, talking about in marketing terms. It's still quiet ocean. It's full of fish. No one is out there fishing. If you go out with your new technology, all those fish could be yours. Surely an investor would want to help you go out and fish this ocean? No. Why? Why wouldn't they? Because there's a red ocean over there. What's a red ocean? A red ocean? Well, it's sharks. sifting through the water eating fish. All kinds of people are out there with spear guns and nets and they're fishing up a storm and the ocean is red with blood. The ocean is red with blood, you got a whole bunch of investors, you've got a huge information flow, talking about this area of the marketplace. Everybody knows what's going on. If you say you want to invest in a red ocean, they all know what you're talking about, okay? You're the 990/9 investor in this space, can't wait to jump in. You say to them, Look, there's a massive problem with latent demand over here. The problem is the technology doesn't exist to address it. Here's the technology, we could go in there and be first movers in his field. And people have no idea what you're talking about. One of the personal examples that I lived was Telecom, okay. In the 1980s, absolutely. Everybody knew that, that the big innovation and telecom had been at&t introduced the princess phony, you could get it in pink or blue. Okay, all of this was yet to come. And when you try to explain it to people, they just didn't get it. It's the same thing with atomically precise devices.

Okay, so what we went far, as most of you probably know, is we went for one atomic layer thick membranes, because there were a vast variety of really large number of applications that we could look at. And we could use a common manufacturing method for all of these, which lowers your r&d, your r&d costs to get into production. And what we've actually implemented is water purification, wastewater remediation desalination, as one spin out company and renal replacement therapy as another spin out company. We're about to move into gas separation and purification. And the future, one atomic layer thick membrane will become a platform for doing application specific components and addressable platform for doing application specific components that you'd add on top of that. In as you can see, the membrane word membranes in quotes. On top of that motherboard,

should we why the word?

cocoa Go ahead.

So this is part of the education process never ends, okay. So if you're coming, okay, so, if you're coming out of physics, if you're coming out of chemistry, if you're coming out of biology, if you're coming out of basic science, you understand, you can think flexibly about what the word membrane means. Once you get down towards the applied end of the world. People can no longer think about structures like this flexibly, and they're really dialed in on things like Paul, American membranes, or membranes, or like Paul, American membranes, the capabilities of any membrane in the world. It's limited to what polymeric membranes do. So you have these conversations where they say things like, Well, what about polarization concentration? It's like the answer to that is step away from the polymer membrane. You go to talk to a biologist, and you say, you know, how do biological systems handle polarization concentration, they say to you what it's like, just don't do that thing. So you've got the level of capabilities that y'all will be developing with atomically precise structures, defeat the standard expectations in the marketplace of whatever the specific crude technology delivers. So you've got a massive reeducation or relabeling renaming and then re education process for whatever it is you're doing. That's why membranes ism quote.

So we were talking about funding and grants, this is what we did and what we're doing. So um, you see that green line right in the middle and that arrow going on down. We started off with private equity financing. We've also done debt financing where we, you know, finance things ourselves. And then later, we picked up grants because unlike people who are in universities where you can come from, you know, you've got a basis where you can do your work and then you can launch to into grants. We did private sector. To do our basic work, and then we launched into grants, our grants were sbrs, small business innovative research grants from NSF and from Department of Energy, we ended up as the splash page for the Department of Energy's SBI, our website, and we held that position for 2016 through 2019. With the the recognition of the, of the atomic precision that was being delivered in this package, what's happening now is that we've licensed out domains of our technology. And now what's happening is we're getting license fees coming back in and when product goes to market, then we'll start to get royalties. But so we're moving we're moving. What this should indicate is, we seem to have navigated our way past the valley of death. And we seem to have made it to the other side of the gap. And life is life is going to be very different. We put this slide in here just for the following reason. We've been talking about a license model, just in case anybody thinks we're recommending a license model. No, no, do not make that assumption. Okay. We did it for some very specific reasons, we're not going to spend time on this slide unless you guys really want it. There's pros. And there's cons that if you're in a university environment, you're used to seeing the university being able to license our technology. But in the in the wild. Only about 20% of investors will even consider investing in a licensed model. So you've really narrowed your you've your potential investor pool, we'll come back to Gail. Yeah, Gail,

this is a brief note that we're overtime already. So if you want to wrap it up, wrap it up you

This is our This is our first functional licensee. It's a via they've got a dome, they've got water, industrial water applications as their as their exclusive domain. And when you look at the we've been able to attract the top to your talent in the water industry. And when you look at the kind of difference that we're going to be able to make in the world through this application. It's absolutely staggering. It's going to be one of the biggest game changers in the whole co2 thing, as well as life saving because of the groundwater crisis, etc. Already. This is sort of a, you know, a quick delineation of some of those benefits that yeah, that you guys need to talk about when you're doing grants. And just to give you like, two quick examples, one of them is the strange things that the people don't think about, okay, the disease level of disease and death caused by just the blue green algae crisis, or looking at the political implications, the wars in Syria and Yemen, the refugee flight into Europe, the rise of right wing politics, in in Europe, etc, all that stuff, both caused by the rise of the caliphate, both caused by groundwater shortage. So it's going to play a big deal. So there we took your four quadrant slide. And and we filled it in. So we're now moving into we're moving into the the point where our challenges, we got to scale up and get our manufacturing up, and we've got to hire people and keep our quality up as we're, as we're doing this. But we've, it's we've transitioned in terms of help, there you go. In the medium term collaborating, because you notice we've got a we've got a platform, a technology platform, and loads of creativity is capable of being supported by that platform.

Thank you, girl and body. Fantastic. And I thank you so much for your presentation. I will share the slides with people with focus later. If you have any questions or comments, please let us know. Perhaps Marty and Gail, do you have a few tips of folks up are applying to NSF or do grants for people to share that they should that has that have helped you along your weight?

tips.

Just find someone who loves you in that department.

You've got Yeah, we think you need to have somebody In the department who understands what your technology is about, because especially with these transformative game changing things, especially with atomic precision, people still not understand why atomic precision matters. They, well, they just don't, okay. They will look at somebody who is, quote, further along in your domain. In other words, they've got a conventional solution, which has all the liabilities associated with it, they're not going to tweak it make it a little bit better. They really don't understand the game changing benefits that you may be able to deliver. Yeah, and one other thing out here, which is like don't, okay, so don't pull your hair out if what's happening, okay, maybe the hair has already gone. Try not to pull your hair out if what you get is an anonymous what an anonymous reviewer who a comes from a field that you are about to disrupt. So an example would be, you know, when you know, Jim, when you're talking about, you know, I mean, if you're bringing in something that's going to, like, make antibiotics toast, okay, so who's going to review your technology? The guy who's going to review your technology is the guy that you're about to put out of business. Okay, so not unexpected. If you get a certain level of hostility. We've had an NSF reviewer say, I don't care how many facts you show me, I will never give this a positive review. It's like, Whoa, I thought this was science. I thought facts matter, but apparently not. So people. People may not have the skill set to evaluate your technology, even though they're in quote the domain. And you may people may get very, very upset. Okay. Just don't pull your hair up.

Thank you. Well, I think would Marty and Gail, would you be willing that if in case people do apply to the do e or to the NSF that they can maybe contact me now? I'll, I'll, I'll give them your email address.

Lastly,

thank you. Okay. Well, thank you so much for your presentation. And, yeah, fantastic. Please send me any feedback that you have. are we reaching out potentially to schedule a future one of these sessions? If you wanted to be totally off the record with no, we're calling running, that's totally an option. You're just experimenting here and this, you know, it's an invitation from us to all of you to make use of the brainpower that we have on these calls to try to generate something that could be of long term value to the group. Okay, everyone, thank you so, so much for joining and with this experiment, and I'll be in with my for me. Okay, big goal.