Advancing Rare-Earth (4<i>f</i>) and Actinide (5<i>f</i>) Separation through Machine Learning and Automated High-Throughput Experiments.

ABSTRACT: Identifying improved and sustainable alternatives to "classic" separation techniques is an active research field due to its potential widespread impact in fundamental and applied chemistry. As basic purification methodologies, like liquid-liquid extraction, undergo continuous refinement by chemists and engineers, identifying new conditions that outperform existing techniques can be difficult. A major contributor to this challenging problem is the need to explore a vast experimental space to identify the precise conditions that optimize the separation procedure. The advent of artificial intelligence and the advancement of robotic technologies offer the potential to shift the traditional design paradigm. Toward that end, we applied a combination of Bayesian Optimization and high-throughput r

SUBMITTER: Augustine LJ 

PROVIDER: S-EPMC11558677 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature