New AI-driven mobile robots team up to tackle chemical synthesis

Polymer–solvent systems exhibit complex solvation behaviours encompassing a diverse range of phenomena, including swelling, gelation, and dispersion. Accurate interpretation is often hindered by subjectivity, particularly in manual rapid screening assessments. While computer vision models hold significant promise to replace the reliance on human evaluation for inference, their adoption is limited by the lack of domain-specific datasets tailored, in our case, to polymer–solvent systems.

A first example of diglycolamide flow synthesis was developed, showcasing algorithm-accelerated reaction optimization and its potential to accelerate ligand discovery and enable autonomous, AIcontrolled manufacturing of critical materials. The increasing demand for manufacturing rare-earth elements (REEs) as critical materials has intensified research on sustainable synthesis of advanced extractants such as diglycolamides (DGAs).

Screening powder properties such as flowability, compressibility, and particle geometry is crucial for controlling ceramic processing, particularly in automated workflows that demand high reproducibility. Sacrificial templating for porous ceramics is well suited to automation because it is prone to variability arising from manual handling.