Selective hydrogenation plays a critical role in modern synthetic chemistry, particularly in the pharmaceutical industry, where the production of chiral molecules with high enantiomeric purity is essential for the efficacy and safety of active pharmaceutical ingredients (APIs).
Mobile robotic chemists are a fast growing trend in the field of chemistry and materials research. However, so far these mobile robots lack workflow awareness skills.
The integration of automated synthesis and machine learning (ML) is transforming analytical chemistry by enabling data-driven approaches to method development. Chromatographic column selection, a critical yet time-consuming step in separation science, stands to benefit substantially from such advances.
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