Researchers explore machine learning to automate sorting of microcapsules in real-time

Micro-encapsulated CO2 sorbents (MECS)—tiny, reusable capsules full of a sodium carbonate solution that can absorb carbon dioxide from the air—are a promising technology for capturing carbon from the atmosphere. To create the caviar-like objects, scientists run three fluids through a series of microfluidic components to create drops that turn into capsules when exposed to ultraviolet light downstream. However, fluid properties and flow rates can change during experiments. These changes can lead to capsules that are defective, improperly-sized or otherwise unusable, resulting in device clogging, contaminated samples and wasted time.