Abstract
Microalgae-based methods used in heavy metal (HM)-polluted wastewater treatment have attracted increasing attention in recent decades, due to their eco-friendliness, profitability, and sustainability. Unfortunately, their low HM removal efficiency hinders them from practical use. In this work, we report an AI-on-a-chip method, a combination of AI and lab-on-a-chip technology, for identifying Euglena gracilis (a microalgal species) cells with high HM removal efficiency through a morphological meta-feature. In the near future, the implementation of the morphological meta-feature in a high-throughput cell sorting process will pave the way for realizing directed-evolution-based development of microalgae with extremely high HM removal efficiency for practical wastewater treatment worldwide.
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