LLNL scientists have discovered they can use machine learning to automate microencapsulation quality control in real-time, devising an algorithm to determine “good” capsules from “bad” and developing a valve-based mechanism that can sort them without human intervention. Illustration by Jacob Long/LLNL.
(Download Image)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. A new image-based machine learning algorithm can detect problem capsules and trigger a response up to 40 times per second, eliminating the monotonous task of monitoring microcapsule manufacture, saving on wasted material. Read more at LLNL News.
