Sure. Yeah, maybe it's good to start with how this type of device is actually applied? What do you actually do with it? So we invented this technology at MIT. And the motivation for creating kChip was we understood that the microbiome, which are all of the microbes that live in, on, and around us, is a very complex ecosystem, where the way that any one individual microbe works depends on the other microbes that are around it, or the environment that that microbe lives in. And the problem, if you want to study that, experimentally, is that it just becomes literally millions, billions of experiments that you would have to run because if you have one microbe and you want to know, okay, well, what if I put in the presence of this other microbe? What about a different microbe? What about this one experimental condition or some other condition? It's a combinatorial problem, and you end up with billions of things that you have to be able to test. So then the question is, how do you actually physically set up all of those experiments? The way that you might think to do it is, let's try to use a robot. So we'll just program a robot into, well number one of 96-well plate, we're gonna put microbe one and microbe two, well number two, we're gonna put microbe one and microbe three, etc, and array out all these microbes. Theoretically, this works okay, except microbes are growing the whole time that you're trying to set up your experiment. So you suddenly end up with these massive batch effects. Plus, if you do the math of combinatorial problems, it just is literally too many things to test. So as an example, if you have a microbiome that has 1000 microbes in it, and you want to measure all of the possible pairs, so make every possible set of two, and then measure how each of those work. That's half a million measurements that you would have to make. And if you make those more complex, say out to sets of four, you're now talking about billions of different possible combinations. And you need to make and measure each one of those combinations to understand how all these microbes are working together. So this issue of combinatorial experiments, how do you physically set up combinatorial experiments? That's what kChip was designed to solve. And how we're using it today is that we basically have these libraries of microbes or different conditions that microbes can grow in and then we make all of the different possible combinations, measure them all, and feed that into a computer algorithm that allows us to then say, well, like if you have this thing, this thing, and this other thing that we didn't test, can we predict how that combination would behave? And can we pull out the best combinations to put into specific products that will do useful things for people. And those can be things like, let's make a combination of microbes that we can use to treat eczema, which is one of our early applications, or let's make a combination of microbes that you could use to treat recurrent vaginal yeast infection. So kind of a long answer. But that's the overview of why the technology matters. And I can tell you more about how it works as well, if that's of interest.