DCIDA uses single-step reinforcement learning with a deterministic action-to-layout mapping to generate distributed resonator circuits that match target transfer functions more closely than prior methods on a surrogate-based benchmark.
Characterization of the dissolution of water microdroplets in oil
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abstract
Water in oil emulsions have a wide range of applications from chemical technology to microfluidics, where the stability of water droplets is of paramount importance. Here using an accessible and easily reproducible experimental setup we describe and characterize the dissolution of water in oil, which renders nanoliter-sized droplets unstable, resulting in their shrinkage and disappearance in a time scale of hours. This process has applicability in creating miniature reactors for crystallization. We test multiple oils and their combinations with surfactants exhibiting widely different rates of dissolution. We derived simple analytical equations to determine the product of the diffusion coefficient and the relative saturation density of water in oil from the measured dissolution data. By measuring the moisture content of mineral and silicone oils with Karl Fischer titration before and after saturating them with water, we calculated the diffusion coefficient of water in these two oils
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Inverse Design in Distributed Circuits Using Single-Step Reinforcement Learning
DCIDA uses single-step reinforcement learning with a deterministic action-to-layout mapping to generate distributed resonator circuits that match target transfer functions more closely than prior methods on a surrogate-based benchmark.