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Related Experiment Video

Updated: Jun 7, 2025

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level
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Advancing Rare-Earth (4f) and Actinide (5f) Separation through Machine Learning and Automated High-Throughput

Logan J Augustine1, Yufei Wang2, Sara L Adelman2

  • 1Theoretical Division, Los Alamos National Lab, Los Alamos, New Mexico 87545, United States.

ACS Sustainable Chemistry & Engineering
|November 15, 2024
PubMed
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This summary is machine-generated.

This study introduces a novel approach combining artificial intelligence and robotics to optimize liquid-liquid extraction. This method significantly accelerates the discovery of improved separation conditions, reducing experimental effort and costs, especially for radioactive materials.

Area of Science:

  • Applied Chemistry
  • Chemical Engineering
  • Nuclear Chemistry

Background:

  • Traditional separation techniques require extensive experimental exploration for optimization.
  • Developing sustainable and efficient alternatives to classic methods like liquid-liquid extraction is crucial.
  • Vast experimental spaces make identifying optimal conditions challenging.

Purpose of the Study:

  • To accelerate the optimization of liquid-liquid extraction using AI and robotics.
  • To demonstrate a more efficient and sustainable approach to chemical separations.
  • To reduce experimental effort and costs in optimizing separation processes.

Main Methods:

  • Integration of Bayesian Optimization with high-throughput robotic experimentation.
  • Application to the liquid-liquid extraction of thorium (Th4+).

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  • Systematic exploration of experimental parameters guided by AI.

    • Main Results:

    • Achieved optimized experimental conditions with reduced effort (estimated 74% reduction).
    • Conducted 339 distribution ratio measurements across 113 unique conditions.
    • Demonstrated accelerated discovery and optimization compared to traditional methods.

    Conclusions:

    • AI-driven robotic systems significantly enhance the efficiency of separation process optimization.
    • This approach offers substantial time and cost savings, particularly for hazardous materials.
    • The method improves sustainability and minimizes human exposure in chemical separations.