Agentic workflow enables the recovery of critical materials from complex feedstocks via selective precipitation
🤖 AI Summary
This work addresses the long-standing challenge of efficiently and selectively recovering critical metals from complex real-world feedstocks—such as process water and magnet leachates—where conventional approaches are often hindered by protracted development timelines and poor adaptability. For the first time in this domain, a multi-agent collaborative framework is introduced, integrating AI-driven agents, an automated experimentation platform, and selective precipitation chemistry into an intelligent workflow. By leveraging AI-guided decision-making, the system achieves high-purity separation of target materials from actual feed solutions using only simple reagents. This approach dramatically reduces the process development cycle from months or even years to just a few days, substantially enhancing the adaptability, scalability, and overall efficiency of critical metal recovery protocols.
Application Category
📝 Abstract
We present a multi-agentic workflow for critical materials recovery that deploys a series of AI agents and automated instruments to recover critical materials from produced water and magnet leachates. This approach achieves selective precipitation from real-world feedstocks using simple chemicals, accelerating the development of efficient, adaptable, and scalable separations to a timeline of days, rather than months and years.
Problem
Research questions and friction points this paper is trying to address.
critical materials recovery
selective precipitation
complex feedstocks
produced water
magnet leachates
Innovation
Methods, ideas, or system contributions that make the work stand out.
agentic workflow
critical materials recovery
selective precipitation
AI agents
automated separation
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