We report a microfluidic system for individually tailored generation and incubation of core-shell liquid structures with multiple cores that chemically communicate with each other via lipid membranes. We encapsulate an oscillating reaction-diffusion Belousov-Zhabotinsky (BZ) medium inside the aqueous droplets and study the propagation of chemical wave-fronts through the membranes. We further encapsulate the sets of interconnected BZ-droplets inside oil-lipid shells in order to i) chemically isolate the structures and ii) confine them via tunable capillary forces which leads to self-assembly of predesigned topologies. We observe that doublets (pairs) of droplets encapsulated in the shell exhibit oscillation patterns that evolve in time. We collect statistical data from tens of doublets all created under precisely controlled, almost identical conditions from which we conclude that the different types of transitions between the patterns depend on the relative volumes of the droplets within a chemically coupled pair. With this we show that the volume of the compartment is an important control parameter in designing chemical networks, a feature previously appreciated only by theory. Our system not only allows for new insights into the dynamics of geometrically complex and interacting chemical systems but is also suitable for generating autonomous chemically interconnected microstructures with possible future use, e.g., as smart biosensors or drug-release capsules.
In this paper, we present general methods that can be used to explore the information processing potential of a medium composed of oscillating (self-exciting) droplets. Networks of Belousov-Zhabotinsky (BZ) droplets seem especially interesting as chemical reaction-diffusion computers because their time evolution is qualitatively similar to neural network activity. Moreover, such networks can be self-generated in microfluidic reactors. However, it is hard to track and to understand the function performed by a medium composed of droplets due to its complex dynamics. Corresponding to recurrent neural networks, the flow of excitations in a network of droplets is not limited to a single direction and spreads throughout the whole medium. In this work, we analyze the operation performed by droplet systems by monitoring the information flow. This is achieved by measuring mutual information and time delayed mutual information of the discretized time evolution of individual droplets. To link the model with reality, we use experimental results to estimate the parameters of droplet interactions. We exemplarily investigate an evolutionary generated droplet structure that operates as a NOR gate. The presented methods can be applied to networks composed of at least hundreds of droplets.
Unconventional computing devices operating on nonlinear chemical media offer an interesting alternative to standard, semiconductor-based computers. In this work we consider database classifiers formed of interacting droplets in which a photosensitive variant of Belousov-Zhabotinsky (BZ) reaction proceeds. We introduce an evolutionary algorithm that searches for optimal construction of a droplets-based classifier for a given problem. The algorithm is based on maximizing the mutual information between the database and the observed evolution of medium. As an example application of chemical database classifiers we apply the idea to the dataset of points belonging to a unit cube. The dataset contains two output classes: 1 for points belonging to a sphere with radius 0.5 located in the cube center, and 0 for points outside of the sphere. The reliability of optimized chemical classifiers of such database for different numbers of droplets involved in data processing is presented.
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